自查改版，做出横竖两版
你让它从科普专业人士的角度挑毛病，又补了朋友的反馈、几处用词和「深入浅出」的要求。它问了你 4 个问题，下午交回横竖两版 v2.1，片长 5 分 14 秒。
按下播放，用 325 秒看完这 325 分钟。
嗯，那些图片都弄进去吧，我觉得你可以自己 review 一下，然后在保留过去版本的情况下，弄一个你觉得更好新的版本，我觉得你可以调研一下别人的各种地球生命发展史技术爆炸的各种视频设计？他们那些也弄得很不错的
You've said OK to the photos, so I'm downloading the 5 I listed. At the same time I'll save the current version as v1, look at how well-known videos on the history of life and on technology taking off are designed, and then make a v2.
干了 3 分钟，38 步
搜索 11 次，打开 14 个网页，运行 3 条命令，其他操作 10 次
新建待办
{ "activeForm": "保存 v1", "description": "git 打 v1 标签；v1 视频改名保留", "subject": "保存 v1（源码 + 视频），后续改动都在 v2 上做" }Task #8 created successfully: 保存 v1（源码 + 视频），后续改动都在 v2 上做
新建待办
{ "activeForm": "下载照片", "description": "在你电脑上从 Wikimedia Commons 下载，记录作者和授权，传回做进拍立得", "subject": "下载 5 张历史照片并核对授权" }Task #9 created successfully: 下载 5 张历史照片并核对授权
新建待办
{ "activeForm": "调研参考视频", "description": "宇宙日历、melodysheep、Kurzgesagt、bill wurtz 等：怎么表现时间压缩、加速、开头钩子和结尾", "subject": "调研同类视频的设计手法" }Task #10 created successfully: 调研同类视频的设计手法
新建待办
{ "activeForm": "自查 v1", "description": "逐段看画面：可读性、拥挤、重叠、节奏、声音", "subject": "自查 v1，列出问题清单" }Task #11 created successfully: 自查 v1，列出问题清单
新建待办
{ "activeForm": "制作 v2", "description": "按自查和调研结论改版，必要的旁白在你电脑上重新合成", "subject": "制作 v2：照片、开头钩子、时间压缩转场、第二部分重排、大时刻、音效" }Task #12 created successfully: 制作 v2：照片、开头钩子、时间压缩转场、第二部分重排、大时刻、音效
新建待办
{ "activeForm": "渲染并检查 v2", "description": "1080p 成片 + 预览版，放进 ai-tree-video 文件夹，v1 并排保留", "subject": "渲染 v2 并检查（事实、画面、音画同步），交付到你电脑" }Task #13 created successfully: 渲染 v2 并检查（事实、画面、音画同步），交付到你电脑
更新待办
{ "status": "completed", "taskId": "6" }Updated task #6 status
更新待办
{ "status": "deleted", "taskId": "7" }Updated task #7 deleted
更新待办
{ "status": "in_progress", "taskId": "8" }Updated task #8 status
运行 cd ~/ai-tree && cat > .gitignore <<'EOF'
cd ~/ai-tree && cat > .gitignore <<'EOF' node_modules/ out/ *.mp4 __pycache__/ EOF git init -q 2>/dev/null; git config user.email "[邮箱]"; git config user.name "Claude" # put back the two post-render tweaks so the v1 tag matches the rendered film exactly cp scene/film.js /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/film.after.js cp tools/build_timeline.py /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/bt.after.py python3 - <<'EOF' from pathlib import Path p = Path("scene/film.js"); s = p.read_text() new = """function branchGrowth(b, t) { // young branches stay short and lengthen over the years const Y = yearAt(t); return Math.min(easeOut((t - b.tBorn) / 2.4), clamp(0.3 + (Y - b.born) * 0.03)); }""" assert new in s p.write_text(s.replace(new, "function branchGrowth(b, t) { return easeOut((t - b.tBorn) / 2.4); }")) b = Path("tools/build_timeline.py"); bs = b.read_text() bs = bs.replace('("2026-01-26", "“养龙虾”：开源 AI 管家爆火"', '("2026-01-12", "“养龙虾”：开源 AI 管家爆火"').replace('("2026-07-27", "Kimi K3 公开权重：最大的开源模型"', '("2026-07-16", "Kimi K3 开源：史上最大开源模型"') b. …（中间省略 318 字）… tchpad/bt.after.py tools/build_timeline.py git add -A && git commit -qm "v1.1: cap young branch length; fix two note dates" mkdir -p out/v1 && mv out/ai-tree-preview.mp4 out/v1/ai-tree-v1-1080p.mp4 && mv out/ai-tree-preview-720p.mp4 out/v1/ai-tree-v1-720p.mp4 git log --oneline --decorate; ls out/v17e5a868 (HEAD -> master) v1.1: cap young branch length; fix two note dates 52f7100 (tag: v1) v1: first full preview (hand-drawn tree, 4:10, narration + procedural music) ai-tree-v1-1080p.mp4 ai-tree-v1-720p.mp4
运行 cd ~/Movies/ai-tree-video && mv ai-tree-preview-720p.mp4 ai-tree-v1-720p.mp4 &&…
cd ~/Movies/ai-tree-video && mv ai-tree-preview-720p.mp4 ai-tree-v1-720p.mp4 && mkdir -p photos && cat > photos/fetch.py <<'EOF' import json, re, time, urllib.parse, urllib.request, urllib.error UA = {"User-Agent": "ai-tree-video/0.1 (personal non-commercial explainer video)"} API = "https://commons.wikimedia.org/w/api.php" def get(url, tries=6): for k in range(tries): try: with urllib.request.urlopen(urllib.request.Request(url, headers=UA), timeout=60) as r: return r.read() except urllib.error.HTTPError as e: if e.code in (429, 503): wait = int(e.headers.get("Retry-After") or 0) or 15 * (k + 1) print(f"HTTP {e.code}, waiting {wait}s", flush=True); time.sleep(wait); continue raise raise SystemExit("gave up: " + url) def api(**p): p["format"] = "json"; time.sleep(3) return json.loads(get(API + "?" + urllib.parse.urlencode(p))) strip = lambda h: re.sub(r"\s+", " ", re.sub("<[^>]+>", "", h or "")).strip() s = api(action="query", list="search", srsearch="Shakey the Robot developed between 1966-1972 …（中间省略 1381 字）… :200], "bytes": len(data), "original": f'{ii["width"]}x{ii["height"]}'} print(key, len(data), "bytes", credits[key]["license"], "|", credits[key]["artist"], flush=True) json.dump(credits, open("photos/credits.json", "w"), ensure_ascii=False, indent=1) EOF python3 photos/fetch.py && ls -la photosProcess started with PID 75478 (shell: /bin/zsh) Initial output: turing 92890 bytes Public domain | Elliott & Fry perceptron 200782 bytes Public domain | John C. Hay, Albert E. Murray shakey 312323 bytes CC BY-SA 2.0 | Carlo Nardone from Roma, Italy deepblue 97206 bytes CC BY 2.0 | James the photographer stanley 150336 bytes Public domain | total 1704 drwxr-xr-x@ 9 niracler staff 288 Oct 15 10:07 . drwxr-xr-x@ 6 niracler staff 192 Oct 15 10:07 .. -rw-r--r--@ 1 niracler staff 1927 Oct 15 10:07 credits.json -rw-r--r--@ 1 niracler staff 97206 Oct 15 10:07 deepblue.org&utm_campaign=imageinfo&utm_content=original -rw-r--r--@ 1 niracler staff 2633 Oct 15 10:07 fetch.py -rw-r--r--@ 1 niracler staff 200782 Oct 15 10:07 perceptron.org&utm_campaign=imageinfo&utm_content=thumbnail -rw-r--r--@ 1 niracler staff 312323 Oct 15 10:07 shakey.org&utm_campaign=imageinfo&utm_content=thumbnail -rw-r--r--@ 1 niracler staff 150336 Oct 15 10:07 stanley.org&utm_campaign=imageinfo&utm_content=thumbnail -rw-r--r--@ 1 niracler staff 92890 Oct 15 10:07 turing.org&utm_campaign=imageinfo&utm_content=original ✅ Process 75478 has finished execution
搜索「melodysheep Timelapse of the Entire Universe how time scale…」
{ "mode": "standard", "query": "melodysheep Timelapse of the Entire Universe how time scale changes design" }Web search results for query: "melodysheep Timelapse of the Entire Universe how time scale changes design" Links: [{"title":"A Stunning Timelapse of the Entire Universe Over 13 Billion Years as Described by Four Iconic Narrators","url":"https://laughingsquid.com/timelapse-of-the-entire-universe/"},{"title":"< Return to Video","url":"https://amara.org/videos/3wMUWp34fR1v/en/2115479"},{"title":"www.imsc.res.in","url":"https://www.imsc.res.in/~indu/JM/2018/temp/BigBang/bigbang.txt"},{"title":"Timelapse of the Entire Universe","url":"https://www.melodysheep.com/timelapse"},{"title":"TIMELAPSE OF THE ENTIRE UNIVERSE","url":"https://amara.org/v/fUWv"},{"title":"laughingsquid.com","url":"https://laughingsquid.com/?p=704536"},{"title":"this is how the universe ends","url":"https://longnow.org/ideas/this-is-how-the-universe-ends/"},{"title":"This is How The Universe Ends","url":"https://www.goodreads.com/author_blog_posts/18226852-this-is-how-the-universe-ends?tab=author"},{"title":"< Return to Video","url":"https://amara.org/videos/CU2tm1zez2So/en/2466521"},{"title":"Movies Logo","url":"https://movies.oktaycolakoglu.com/movie/1745135"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Carl Sagan Cosmic Calendar humans appear last seconds Decem…」
{ "mode": "standard", "query": "Carl Sagan Cosmic Calendar humans appear last seconds December 31 explained" }Web search results for query: "Carl Sagan Cosmic Calendar humans appear last seconds December 31 explained" Links: [{"title":"cosmic calendar carl sagan video","url":"https://thekidshouldseethis.com/post/cosmic-calendar-carl-sagan-video"},{"title":"thekidshouldseethis.com","url":"https://thekidshouldseethis.com/post/50093849556"},{"title":"The Cosmic Timeline","url":"https://visav.phys.uvic.ca/~babul/AstroCourses/P303/BB-slide.htm"},{"title":"Chapter 1 Science and the Universe: A Brief Tour","url":"https://pressbooks.online.ucf.edu/astronomybc/chapter/1-9-a-conclusion-and-a-beginning/"},{"title":"The Cosmic Calendar","url":"https://www.oshonews.com/?p=72511"},{"title":"The Cosmic Calendar at Loiano","url":"https://www.oas.inaf.it/en/the-cosmic-calendar-at-loiano/"},{"title":"The Cosmic Calendar at Loiano","url":"https://www.oas.inaf.it/?p=7921"},{"title":"cosmic calendar universe 365 big bang compress history","url":"https://www.scienceabc.com/nature/universe/cosmic-calendar-universe-365-big-bang-compress-history"},{"title":"The Cosmic Calendar","url":"https://www.oshonews.com/2016/02/11/the-cosmic-calendar/"},{"title":"Table of Contents (click to expand)","url":"https://www.scienceabc.com/nature/universe/cosmic-calendar-universe-365-big-bang-compress-history.html"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「bill wurtz "history of the entire world, i guess" why it wo…」
{ "mode": "standard", "query": "bill wurtz \"history of the entire world, i guess\" why it works editing style analysis" }Web search results for query: "bill wurtz "history of the entire world, i guess" why it works editing style analysis" Links: [{"title":"Bill Wurtz","url":"https://en.wikipedia.org/wiki/Bill_Wurtz"},{"title":"Bill Wurtz","url":"https://Www.wikipedia.org/wiki/Dance_The"},{"title":"Bill Wurtz","url":"https://Www.wikipedia.org/wiki/Icy_James"},{"title":"# History of Japan","url":"https://meme.com/memes/history-of-japan/md"},{"title":"laughingsquid.com","url":"https://laughingsquid.com/?p=773956"},{"title":"How Bill Wurtz Refined His Incredible Music Talent With His Fascinating Animated YouTube Videos","url":"https://laughingsquid.com/bill-wurtz/"},{"title":"History of Japan","url":"https://meme.com/memes/history-of-japan"},{"title":"How Bill Wurtz’s Videos Make the Internet a Better Place","url":"https://www.okwhatever.org/topics/wtf/bill-wurtz-videos"},{"title":"Bill Wurtz","url":"https://bafybeiaysi4s6lnjev27ln5icwm6tueaw2vdykrtjkwiphwekaywqhcjze.ipfs.ipfs.hypha.coop/wiki/Bill_Wurtz"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Kurzgesagt "Time: The History & Future of Everything" desig…」
{ "mode": "standard", "query": "Kurzgesagt \"Time: The History & Future of Everything\" design timeline zoom" }Web search results for query: "Kurzgesagt "Time: The History & Future of Everything" design timeline zoom" Links: [{"title":"An Updated Informative Animated Timeline of Major Historic Events That May Affect the Future","url":"https://laughingsquid.com/major-historic-events-that-may-affect-the-future/"},{"title":"Time: The History and Future of Everything","url":"https://ritholtz.com/2020/12/time-the-history-and-future-of-everything"},{"title":"laughingsquid.com","url":"https://laughingsquid.com/?p=360249"},{"title":"The History And Future Of Everything","url":"https://bigshinyrobot.com/movies/video-history-and-future-everything/"},{"title":"The History and Future of Everything -- Time","url":"https://amara.org/videos/9MbxXSuBWAA2/info/the-history-and-future-of-everything-time"},{"title":"< Return to Video","url":"https://amara.org/videos/9MbxXSuBWAA2/en-gb/653939"},{"title":"the history and future of everything","url":"https://computus.org/the-history-and-future-of-everything/"},{"title":"The History of Everything 4+","url":"https://apple.co/2Hq9Irv"},{"title":"fueled.com","url":"https://fueled.com/?p=25691"},{"title":"history timeline app","url":"https://fueled.com/blog/history-timeline-app/"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Wait But Why AI Revolution stick figure exponential curve D…」
{ "mode": "standard", "query": "Wait But Why AI Revolution stick figure exponential curve Die Progress Unit illustration" }Web search results for query: "Wait But Why AI Revolution stick figure exponential curve Die Progress Unit illustration" Links: [{"title":"knowledge.wharton.upenn.edu","url":"https://knowledge.wharton.upenn.edu/?p=16420"},{"title":"the ai revolution how far away are our robot overlords 1684199433","url":"https://gizmodo.com/the-ai-revolution-how-far-away-are-our-robot-overlords-1684199433"},{"title":"Magazine / What is AI? Why Should you Care? What Does it All Mean?","url":"https://nextbigideaclub.com/magazine/tim-urban-what-is-the-future-of-ai/5544/s-curves2-600x490/amp/"},{"title":"What is AI? Why Should you Care? What Does it All Mean?","url":"https://nextbigideaclub.com/magazine/tim-urban-what-is-the-future-of-ai/5544/edge-600x427"},{"title":"What is AI? Why Should you Care? What Does it All Mean?","url":"https://nextbigideaclub.com/magazine/tim-urban-what-is-the-future-of-ai/5544/edge1-600x427"},{"title":"Magazine / What is AI? Why Should you Care? What Does it All Mean?","url":"https://nextbigideaclub.com/magazine/tim-urban-what-is-the-future-of-ai/5544/edge-600x427/amp/"},{"title":"Magazine / What is AI? Why Should you Care? What Does it All Mean?","url":"https://nextbigideaclub.com/magazine/tim-urban-what-is-the-future-of-ai/5544/intelligence-600x472/amp/"},{"title":"Magazine / What is AI? Why Should you Care? What Does it All Mean?","url":"https://nextbigideaclub.com/magazine/tim-urban-what-is-the-future-of-ai/5544/projections/amp/"},{"title":"Magazine / What is AI? Why Should you Care? What Does it All Mean?","url":"https://nextbigideaclub.com/magazine/tim-urban-what-is-the-future-of-ai/5544/amp/"},{"title":"Magazine / What is AI? Why Should you Care? What Does it All Mean?","url":"https://nextbigideaclub.com/magazine/tim-urban-what-is-the-future-of-ai/5544/edge1-600x427/amp/"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「B站 地球46亿年 压缩成24小时 视频 播放量」
{ "mode": "standard", "query": "B站 地球46亿年 压缩成24小时 视频 播放量" }Web search results for query: "B站 地球46亿年 压缩成24小时 视频 播放量" Links: [{"title":"An animated history of Earth in one day","url":"https://spokesman.com/blogs/down-earth/2012/dec/13/animated-history-earth-one-day"},{"title":"Earth’s 4.5 Billion Years of Evolution Condensed Into a One Hour Animation","url":"https://laughingsquid.com/?p=923661"},{"title":"An Imaginative Video That Condenses All of Earth’s History Into a 24-Hour Clock","url":"https://laughingsquid.com/earth-history-condensed-in-24-hours/"},{"title":"history of the earth in 24 hours earth in a day 4 5 billion years of history in 24 hours","url":"https://shibboleth.idm.home.nyu.edu/facts-about-earth-day/history-of-the-earth-in-24-hours-earth-in-a-day-4-5-billion-years-of-history-in-24-hours"},{"title":"Earth’s 4.5 Billion Years of Evolution Condensed Into a One Hour Animation","url":"https://laughingsquid.com/4-and-half-billion-years-earth-evolution/"},{"title":"The History of the Earth (All 4.5 Billion Years) in 1 Hour: A Million Years Covered Every Second","url":"https://www.openculture.com/2023/12/4-5-billion-years-in-1-hour-a-million-years-gets-covered-in-each-second.html"},{"title":"www.waterstones.com","url":"https://www.waterstones.com/book/9781783127986"},{"title":"Advertise here with Carbon Ads","url":"https://also.kottke.org/23/11/a-45-billion-year-video-timeline-of-earth-in-60-minutes"},{"title":"the earths story in 60 seconds","url":"https://www.syfy.com/syfywire/the-earths-story-in-60-seconds"},{"title":"www.eurekalert.org","url":"https://www.eurekalert.org/multimedia/551044"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Opus 5.5 AI history video Claude code animation timeline re…」
{ "mode": "standard", "query": "Opus 5.5 AI history video Claude code animation timeline remix" }Web search results for query: "Opus 5.5 AI history video Claude code animation timeline remix" Links: [{"title":"claude opus 5 5 video plan one shot","url":"https://www.orcarouter.ai/blog/claude-opus-5-5-video-plan-one-shot"},{"title":"how claude opus 5 5 generates videos with code","url":"https://www.wps.com/blog/how-claude-opus-5-5-generates-videos-with-code/"},{"title":"claude opus 5 5 video plan one shot","url":"https://www.orcarouter.ai/it/blog/claude-opus-5-5-video-plan-one-shot"},{"title":"claude opus 5 5 video plan one shot","url":"https://www.orcarouter.ai/fr/blog/claude-opus-5-5-video-plan-one-shot"},{"title":"claude opus 5 5 video plan one shot","url":"https://www.orcarouter.ai/pl/blog/claude-opus-5-5-video-plan-one-shot"},{"title":"claude opus 5 5 video plan one shot","url":"https://www.orcarouter.ai/pt/blog/claude-opus-5-5-video-plan-one-shot"},{"title":"claude opus 5 5 video plan one shot","url":"https://www.orcarouter.ai/de/blog/claude-opus-5-5-video-plan-one-shot"},{"title":"claude opus 5 5 video plan one shot","url":"https://www.orcarouter.ai/he/blog/claude-opus-5-5-video-plan-one-shot"},{"title":"claude opus 5 5 video plan one shot","url":"https://www.orcarouter.ai/ru/blog/claude-opus-5-5-video-plan-one-shot"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Our World in Data brief history of artificial intelligence …」
{ "mode": "standard", "query": "Our World in Data brief history of artificial intelligence world has changed fast charts" }Web search results for query: "Our World in Data brief history of artificial intelligence world has changed fast charts" Links: [{"title":"alphaarchitect.com","url":"https://alphaarchitect.com/?p=84475"},{"title":"artificial intelligence","url":"https://alphaarchitect.com/artificial-intelligence/"},{"title":"www.interactivebrokers.com","url":"https://www.interactivebrokers.com/campus/wp-json/wp/v2/posts/186836"},{"title":"technology long run","url":"https://ourworldindata.org/technology-long-run"},{"title":"Our latest work","url":"https://muckrack.com/link/RWeoy/our-latest-work"},{"title":"Explore Topics:","url":"https://singularityhub.com/2023/01/29/technology-over-the-long-run-zoom-out-to-see-how-dramatically-the-world-can-change-within-a-lifetime/"},{"title":"History of AI and Neural Networks","url":"https://libguides.aurora.edu/ChatGPT/History-of-AI-and-Neural-Networks"},{"title":"Technology over the long run: See how dramatically the world can change within a lifetime","url":"https://www.freethink.com/hard-tech/technology-history?amp=1"},{"title":"Emerging Technologies","url":"https://www.weforum.org/stories/2023/02/this-timeline-charts-the-fast-pace-of-tech-transformation-across-centuries/"},{"title":"our world in data history","url":"https://ourworldindata.org/our-world-in-data-history"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.思考：Fixing filenames broken by stray query-string parameters.
Fixing filenames broken by stray query-string parameters.
运行 cd ~/Movies/ai-tree-video/photos && for f in turing perceptron shakey deepblue …
cd ~/Movies/ai-tree-video/photos && for f in turing perceptron shakey deepblue stanley; do src=$(ls ${f}.* | grep -v '\.py$' | head -1); t=$(file -b --mime-type "$src"); case "$t" in image/png) e=png;; *) e=jpg;; esac; mv "$src" "${f}.${e}"; done; ls -la; cat credits.jsonProcess started with PID 75589 (shell: /bin/zsh) Initial output: total 1704 drwxr-xr-x@ 9 niracler staff 288 Oct 15 10:08 . drwxr-xr-x@ 6 niracler staff 192 Oct 15 10:07 .. -rw-r--r--@ 1 niracler staff 1927 Oct 15 10:07 credits.json -rw-r--r--@ 1 niracler staff 97206 Oct 15 10:07 deepblue.jpg -rw-r--r--@ 1 niracler staff 2633 Oct 15 10:07 fetch.py -rw-r--r--@ 1 niracler staff 200782 Oct 15 10:07 perceptron.png -rw-r--r--@ 1 niracler staff 312323 Oct 15 10:07 shakey.jpg -rw-r--r--@ 1 niracler staff 150336 Oct 15 10:07 stanley.jpg -rw-r--r--@ 1 niracler staff 92890 Oct 15 10:07 turing.jpg { "turing": { "title": "File:Alan Turing (1951).jpg", "page": "https://commons.wikimedia.org/wiki/File:Alan_Turing_(1951).jpg", "license": "Public domain", "license_url": "", "artist": "Elliott & Fry", "credit": "https://www.computerhistory.org/timeline/1949/", "bytes": 92890, "original": "800x1067" }, "perceptron": { "title": "File:Mark I Perceptron, Figure 2 of operator's manual.png", "page": "https://commons.wikimedia.org/wiki/File:Mark_I_Perceptron,_Figure_2_of_operator%27s_manual.png", "license": "Public domain", "license_url": "", "artist": "John C. Hay, Albert E. Murray", "credit": "https://apps.dtic.mil/sti/tr/pdf/AD0236965.pdf", "bytes": 200782, "original": "1166x945" }, "shakey": { "title": "File:Shakey the Robot (developed between 1966-1972 at SRI International) - Computer History Museum (2007-11-10 23.16.01 by …（中间省略 690 字）… , "credit": "https://www.flickr.com/photos/22453761@N00/592436598/", "bytes": 97206, "original": "400x601" }, "stanley": { "title": "File:Stanley2.JPG", "page": "https://commons.wikimedia.org/wiki/File:Stanley2.JPG", "license": "Public domain", "license_url": "", "artist": "", "credit": "", "bytes": 150336, "original": "2560x1668" } } ✅ Process 75589 has finished execution打开 melodysheep.com/timelapse
{ "prompt": "Describe how this video presents time: does it use a timeline/time counter, does the time scale change (logarithmic, accelerating, slowing), how long is it, how are text/labels used, and the role of music/narration. Quote at most one short phrase.", "url": "https://www.melodysheep.com/timelapse" }The page describes the video as a **constant-rate** compression: 13.8 billion years shown at **22 million years per second**, which works out to roughly 10 minutes. It doesn't describe the time scale as logarithmic, accelerating, or slowing, so the rate appears to stay the same throughout. The page doesn't say whether the video shows an on-screen timeline or time counter. It states that the sequence starts with the Big Bang and ends with the appearance of *Homo sapiens*, but it doesn't describe any text or labels. Narration is credited to Morgan Freeman, Brian Cox, Carl Sagan, and David Attenborough. The page doesn't describe how the narration is paced or placed within the video. Music, concept, editing, and sound design are credited to melodysheep, but the page gives no detail on how the music tracks or shapes the sense of time. Its footage comes from many sources, including NASA and the documentaries listed on the page.
打开 thekidshouldseethis.com/post/cosmic-calendar-carl-sagan-vid…
{ "prompt": "Explain the Cosmic Calendar device as shown here: how history is mapped to one year, where humans appear (date/time), and any specific numbers given (e.g., seconds per year of real time). Mention the visual presentation if described.", "url": "https://thekidshouldseethis.com/post/cosmic-calendar-carl-sagan-video" }**Mapping:** The Cosmic Calendar compresses the universe's history into one calendar year. The Big Bang is January 1 at midnight, and the present is midnight on December 31. Sagan's 1980 *Cosmos* clip uses 15 billion years as its example, while the page gives the universe's age as 13.82 billion years. **Where humans appear:** Humans first appear around 10:30 p.m. on December 31. Each cosmic minute represents 30,000 years. **Key events on the calendar:** - **11:46 p.m.** (about 14 minutes before midnight): humans tame fire. - **11:59:20 p.m.:** plants and animals begin to be domesticated. - **11:59:35 p.m.:** settled agricultural communities grow into the first cities. **Recorded history:** It occupies only the last few seconds of the final minute. The page says all recorded human memory fits in "this small square," and that everything in the history books happens in roughly the last 10 seconds. **Seconds per year:** The page doesn't state this directly. Since one cosmic minute equals 30,000 years, one cosmic second works out to 500 years. **Visual presentation:** The page describes the calendar as a year-long timeline with a "small square" at the end representing recorded history. It also includes an image titled "Carl Sagan's cosmic calendar" and embeds Sagan's video clip, but it doesn't describe the image's layout in detail.
打开 laughingsquid.com/earth-history-condensed-in-24-hours/
{ "prompt": "Describe this 'Earth history in 24 hours' video: creator, length, how the clock device is shown, and when humans/key events appear on the clock.", "url": "https://laughingsquid.com/earth-history-condensed-in-24-hours/" }**Creator:** ReYOUniverso, a YouTube channel. The Laughing Squid article is by Lori Dorn and was published October 22, 2024. **Length:** The page doesn't say how long the video is. **Clock device:** The video condenses Earth's entire history into one day and maps each major event onto a 24-hour clock. The page doesn't describe the visual design beyond that. **Timing of humans and key events:** Modern humans appear only in the last fractions of the last minutes on the clock. The page doesn't give exact times for other events, such as when life emerged or how long dinosaurs lasted, though the video poses those questions.
打开 openculture.com/2023/12/4-5-billion-years-in-1-hour-a-milli…
{ "prompt": "Describe the design of this 1-hour Earth history video: who made it, the fixed rate of time, what the viewer sees (on-screen counters, labels), and any noted strengths/weaknesses of a constant time rate.", "url": "https://www.openculture.com/2023/12/4-5-billion-years-in-1-hour-a-million-years-gets-covered-in-each-second.html" }**Creator:** The video is from Kurzgesagt, according to the Open Culture page that hosts it. **Fixed time rate:** The video compresses Earth's history into about an hour at a constant speed: "Every second shows about a million years of the planet's evolution." Because the rate never changes, the 4.5-billion-year timeline maps evenly onto the runtime. **What the viewer sees:** The page describes an animated movie with a musical train ride as its framing. It lists chapter timestamps for the geologic periods, from the Hadean at 0:51 to the Quaternary at 1:00:45, plus an intro and ending. The page does not mention on-screen counters or labels, so I can't confirm whether those appear. **Strengths of a constant rate:** The page says the video is meant to show "how long a billion years really is," which suggests the uniform pacing is intended to convey the scale of deep time. **Weaknesses:** The page doesn't discuss any drawbacks of a constant rate. One reasonable inference, not stated in the source, is that a uniform pace gives recent periods the same screen time per year as ancient ones, so the most recent eras get very little time per event. The reader comments don't describe the video's design, so I've left them out.
打开 ourworldindata.org/technology-long-run
{ "prompt": "Summarize the main visual device of this article (the timeline chart of technology), how it shows acceleration, and the key message about how much changes within one lifetime. Give 2-3 concrete facts it cites (with dates).", "url": "https://ourworldindata.org/technology-long-run" }**The visual:** The article's main device is a spiral timeline of technology. It starts at the center with the first stone tools, about 3.4 million years ago, and each turn represents 200,000 years. For the last 12,000 years the spiral is unrolled, and from 1800 onward the timeline stretches out further to fit the rapid run of inventions. A red line marks the past up to the present, and a green line continues into the future. **How it shows acceleration:** Early in the spiral, changes are spread over millions of years. Agriculture, writing, and the wheel were each separated by several thousand years. After 1800, many major inventions follow each other quickly, so the chart's spacing makes the speed visible. The article also notes that a plain linear timeline would leave most of the space empty, with nearly all the action crammed into one corner. **Key message about a lifetime:** Technology can change the world more within one lifetime than people expect. The article's examples: - The Wright brothers' first flight in 1903, followed by the moon landing just 66 years later. Many people saw both. - Fire control took 12 spiral turns, or 2.4 million years, to develop. The author argues that the next decades could bring similarly dramatic change, especially from AI, and that society should help decide which technologies are developed.
打开 gizmodo.com/the-ai-revolution-how-far-away-are-our-robot-ov…
{ "prompt": "Describe the illustrations/analogies used to show accelerating progress: the 'Die Progress Unit' (time-travel) idea and the stick figure standing on the exponential curve. What exactly does the stick-figure picture show and what point does it make?", "url": "https://gizmodo.com/the-ai-revolution-how-far-away-are-our-robot-overlords-1684199433" }The content you provided doesn't describe a stick figure on an exponential curve. The article has no such illustration, and its image captions don't mention one. It refers to a few graphics without describing them, such as a GIF that "illustrates the concept nicely" and a chart of computing power per $1,000, but none is a stick figure. The **Die Progress Unit (DPU)** is a thought experiment, not an image. The author imagines bringing a person from an earlier era into the present and asks how much time must separate the two eras before the visitor would be overwhelmed. A 1750 person brought to 2015 might die from the shock. A 1500 person brought to 1750 would be amazed but would survive, because the gap is smaller. Going back to hunter-gatherer times, a DPU took over 100,000 years, but after the Agricultural Revolution it took about 12,000 years. Since the Industrial Revolution, it takes only a couple hundred years. The point is that progress is accelerating, so each DPU arrives sooner.
打开 okwhatever.org/topics/wtf/bill-wurtz-videos
{ "prompt": "What stylistic features of Bill Wurtz's 'history of the entire world, i guess' (and his other videos) are described: pacing, humor, visuals, text on screen, music? Summarize what makes it effective.", "url": "https://www.okwhatever.org/topics/wtf/bill-wurtz-videos" }The article doesn't describe the "history of the entire world, i guess" video in detail. Its features are described across Wurtz's work generally, and the article calls his "history of japan" video a "frenetic, animated masterpiece" and says the world video is a sequel. **Stylistic features** - **Pacing:** Fast and frenetic in the longer history pieces, while some works are tiny "super shorts," such as a 28-second video. - **Humor:** Deadpan and absurd. Clichéd lyrics mix with nonsense, and logic is "thrown out the window." - **Visuals:** Lo-fi and crude, combining Word-level animation with clip art, and sending neon text "careening across the screen." - **On-screen text:** Bold, moving text is a central part of the look. - **Music and voice:** Jazz-style crooning is paired with deadpan spoken word. Wurtz's voice is described as dreamy, synthy, and comedic. The songs use lo-fi digital synths, bleeps, and bloops, but an analyst found "advanced composition" underneath. **Why it works** - **Surprise:** The mix of genres and sudden shifts makes the content feel fresh in an internet the article describes as dull. - **Deliberate craft:** The randomness hides real structure and musical skill, so the work is more than "crap." - **Old-school sincerity:** It's made for its own sake, free, and mostly ad-free, which recalls the earlier, more anonymous web. - **Consistency:** Wurtz's strict output schedule and focus on finishing work give the style a distinct identity.
打开 besthub.dev/articles/opus-5-5-codes-music-videos-musk-retwe…
{ "prompt": "List the concrete techniques people used to make the best Opus 5.5 code-rendered music videos (beat maps, seek(t), low-res previews, storyboards, etc.), and describe any AI-history or Google-history video mentioned: how was it structured visually?", "url": "https://www.besthub.dev/articles/opus-5-5-codes-music-videos-musk-retweets-prompt-engineering-sonnet-5-5-leak-a562393c62a6" }**Techniques used in the Opus 5.5 music-video builds** - **Reference-driven style guide:** Feed the model a single frame, a full video, or a portfolio. Have it extract a style guide (palette, typography, lens choices, transitions) so later frames stay consistent. - **seek(t) time functions:** Each second maps to a deterministic frame calculation. Segments can render in parallel in a browser, and a single broken shot can be re-rendered without redoing the whole video. - **Beat maps before visuals:** Analyze BPM, downbeats, and volume peaks first, then lock cuts, text animations, color shifts, and sound effects to those timestamps. - **Storyboards and speech-driven requirements:** Donald spoke his requirements for five minutes, then let the model write code, storyboard, generate frames, add transitions, and export a 2:21 video over 12 hours. He published his roughly 9,500-character prompt. - **Low-res previews and contact sheets:** Review storyboards and low-res renders, assemble keyframes into a contact sheet, and check copy, composition, and motion. Fix the three worst issues before a final render. Movez's watercolor short took 163 model calls over nearly seven hours. - **Iterative refinement:** mexicat tuned visuals through repeated passes with Claude. anabology added Midjourney assets and moodboards and extended the runtime past five minutes. - **Production framework over prompt:** Movez summarized the approach as "Prompt is only 10%, the other 90% is the production framework." **Google-history video** The article describes this as a five-minute Google biography with rapid-cut visuals synced to the beat. It gives no further detail on scene order, layout, or individual shots, so I can't say more about its visual structure. **AI-history video** The article doesn't mention one. The closest comparable example is the two-minute Steve Jobs tribute, which condenses his life from garage startup to the personal computing revolution using 23 transitions at 120 BPM.
打开 ritholtz.com/2020/12/time-the-history-and-future-of-everyth…
{ "prompt": "Describe how Kurzgesagt's 'Time: The History & Future of Everything' visualizes time: the zoom-out/scale device, how they show humanity's tiny share of time, and any notable techniques.", "url": "https://ritholtz.com/2020/12/time-the-history-and-future-of-everything" }The page doesn't describe the video's visuals. It doesn't mention a zoom-out device, how humanity's share of time is shown, or any other specific technique. It only says the video uses infographics to give viewers perspective on time. The page does note that: - Kurzgesagt collaborated with "Wait but Why" on the 2013 video. - "Time makes sense in small pieces," but huge spans of time are "almost impossible to wrap your head around," which is the problem the video targets. To learn how the zoom-out and the humanity-share visuals work, you'd need to watch the video or find a more detailed review.
思考：Recalling Kurzgesagt's video style from memory. Designing a …
Recalling Kurzgesagt's video style from memory.
Designing a timeline pace that stretches near the present.
Adding a speed indicator to make slowing time visible.准备从你电脑传回文件
{ "paths": [ "~/Movies/ai-tree-video/photos/turing.jpg", "~/Movies/ai-tree-video/photos/perceptron.png", "~/Movies/ai-tree-video/photos/shakey.jpg", "~/Movies/ai-tree-video/photos/deepblue.jpg", "~/Movies/ai-tree-video/photos/stanley.jpg", "~/Movies/ai-tree-video/photos/credits.json" ] }{"staged":[{"devicePath":"~/Movies/ai-tree-video/photos/turing.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/turing.jpg","mtimeMs":1792030054565,"bytes":92890,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/perceptron.png","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/perceptron.png","mtimeMs":1792030058149,"bytes":200782,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/shakey.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/shakey.jpg","mtimeMs":1792030062440,"bytes":312323,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/deepblue.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/deepblue.jpg","mtimeMs":1792030065744,"bytes":97206,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/stanley.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/stanley.jpg","mtimeMs":1792030069832,"bytes":150336,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/credits.json","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/credits.json","mtimeMs":1792030069833,"bytes":1927,"ok":true,"dispatched":true}]}搜索「melodysheep "Timelapse of the Future" time accelerates loga…」
{ "mode": "standard", "query": "melodysheep \"Timelapse of the Future\" time accelerates logarithmic counter" }Web search results for query: "melodysheep "Timelapse of the Future" time accelerates logarithmic counter" Links: [{"title":"Timelapse of the Future","url":"https://en.wikipedia.org/wiki/Timelapse_of_the_Future"},{"title":"Timelapse of the Entire Universe","url":"https://en.wikipedia.org/wiki/Timelapse_of_the_Entire_Universe"},{"title":"โฐ Last Chance To Save! Check out the deals ๐Ÿ‘‰ here","url":"https://www.asoundeffect.com/timelapse-of-the-future-sound/"},{"title":"www.lastwordonnothing.com","url":"https://www.lastwordonnothing.com/?p=20120"},{"title":"lastwordonnothing.com","url":"https://lastwordonnothing.com/2020/01/09/20120/"},{"title":"the 13 billion years that led to us a cosmological timelapse","url":"https://aeon.co/videos/the-13-billion-years-that-led-to-us-a-cosmological-timelapse"},{"title":"Last Call: The Most Frightening Video On YouTube","url":"https://thetakeout.com/1834136067"},{"title":"theamericanminvra.com","url":"https://theamericanminvra.com/tag/future"},{"title":"16 hours ago · Life · 0 comments","url":"https://bubbles.town/entry/39325245"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「科技树 技术爆炸 人类文明 视频 B站 一口气看完」
{ "mode": "standard", "query": "科技树 技术爆炸 人类文明 视频 B站 一口气看完" }Web search results for query: "科技树 技术爆炸 人类文明 视频 B站 一口气看完" Links: [{"title":"wiki.swarma.org","url":"https://wiki.swarma.org/index.php/特殊:移动版差异/24637"},{"title":"wiki.swarma.org","url":"https://wiki.swarma.org/index.php/特殊:移动版差异/24639"},{"title":"cloud.tencent.com","url":"https://cloud.tencent.com/developer/news/319392"},{"title":"m.qidian.com","url":"https://m.qidian.com/ttsbook/1034919329/1/"},{"title":"tech.ifeng.com","url":"https://tech.ifeng.com/c/7zZ3B2fRbmK"},{"title":"www.dhs.tsinghua.edu.cn","url":"https://www.dhs.tsinghua.edu.cn/?p=7782"},{"title":"cloud.tencent.com","url":"https://cloud.tencent.com/developer/news/1808821"},{"title":"The historical tech tree","url":"https://www.bespacific.com/?p=114691"},{"title":"we are living in a slow moving explosion","url":"https://collapsemusings.substack.com/p/we-are-living-in-a-slow-moving-explosion"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.打开 amara.org/videos/9MbxXSuBWAA2/info/the-history-and-future-o…
{ "prompt": "From the transcript/subtitles, describe the structure of the video: does it start from 'now' and repeatedly zoom out to longer time spans (100 years, 10,000 years, etc.)? List the sequence of time scales shown and how humanity's share is highlighted. Quote at most one short line.", "url": "https://amara.org/videos/9MbxXSuBWAA2/info/the-history-and-future-of-everything-time" }The page you provided doesn't include the video's transcript or subtitles. It's the sign-up page for Amara's free public video space, so I can't tell whether the video starts from "now" and zooms out through longer time spans, which time scales it uses, or how it highlights humanity. The only video-related detail is the URL slug, `the-history-and-future-of-everything-time`, which suggests the video's title but says nothing about its structure. To answer your question, you could: - **Paste the transcript or subtitle text** here, and I'll outline the time scales and how humanity's role is framed. - **Log in to Amara** (the page links to the login), open the video, and copy the subtitle text from the editor or the transcript view.
搜索「Hans Rosling 200 countries 200 years 4 minutes animated cha…」
{ "mode": "standard", "query": "Hans Rosling 200 countries 200 years 4 minutes animated chart storytelling technique" }Web search results for query: "Hans Rosling 200 countries 200 years 4 minutes animated chart storytelling technique" Links: [{"title":"The Marginalian","url":"https://www.themarginalian.org/2010/12/03/hans-rosling-bbc/"},{"title":"With support from the University of Richmond","url":"https://www.historynewsnetwork.org/article/134568"},{"title":"Hans Rosling’s 200 Countries, 200 Years, 4 Minutes","url":"https://www.worldpoliticsreview.com/?p=21746"},{"title":"Visual Software Brings the Joy of Stats: 200 Countries, 200 Years, 4 Minutes","url":"https://www.tibco.com/blog/?p=30300"},{"title":"With support from the University of Richmond","url":"https://hnn.us/article/134568"},{"title":"Brain Pickings Icon","url":"https://www.brainpickings.org/2010/12/03/hans-rosling-bbc/"},{"title":"Hans Rosling’s 200 Countries, 200 Years: The Joy of Stats","url":"https://omiusajpic.org/?p=8115"},{"title":"Watched Hans Rosling's 200 Countries, 200 Years, 4 Minutes from The Joy of Stats - BBC Four | YouTube","url":"https://boffosocko.com/tag/hans-rosling/"},{"title":"200 Countries, 200 Years, 4 Minutes","url":"https://transcend.org/tms/?p=10316"},{"title":"Hans Rosling’s 200 Countries, 200 Years, 4 Minutes – The Joy of Stats – BBC Four","url":"https://www.vismaster.eu/news/200countries200years4minutes/"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.打开 en.wikipedia.org/wiki/Timelapse_of_the_Future
{ "prompt": "How does Timelapse of the Future handle the passage of time (constant vs. accelerating/logarithmic, on-screen year counter, how the rate changes per section)? Also length, release year, and reception highlights.", "url": "https://en.wikipedia.org/wiki/Timelapse_of_the_Future" }**Handling of time** - The rate is not constant. It starts at roughly 0.5 months per frame (12 months ÷ 24 fps) and increases as the film progresses. - The intro describes the lapse rate as doubling every five seconds. - The on-screen year counter drives the first track, "Sun Mother," which is set at 120 BPM and speeds up as time advances. - Boswell rejected a faster rate, such as one every three seconds, because it would cram too much into the opening minutes. - The predecessor, *Timelapse of the Entire Universe*, used a fixed scale: each second equaled 22 million years. - The year counter was hidden in the multi-monitor New York screening. **Length and release** - Running time is 29 minutes 21 seconds. - It was released in 2019, on March 20, on Boswell's YouTube channel, Melodysheep. **Reception highlights** - It won two 2020 Webby Awards: People's Voice and Science & Education General Video. - It became viral, with about 88 million views and 2.8 million likes as of December 29, 2022. - Reviews were generally positive for its audiovisual craft. Some critics noted that certain plot points are speculative. - Filmmaker Eugene Lee Yang called it "a breathtakingly brilliant education video." - It inspired Noah Cyrus's music video for "The End of Everything," which Boswell co-directed.
打开 en.wikipedia.org/wiki/Timelapse_of_the_Entire_Universe
{ "prompt": "How is time presented (constant rate? on-screen counter?), how long is human history in the video, and what is the ending device? Release year, length, notable reception.", "url": "https://en.wikipedia.org/wiki/Timelapse_of_the_Entire_Universe" }**Time presentation:** The film uses a constant rate, with every second representing 22 million years. The article doesn't mention an on-screen counter. **Human history:** Humanity occupies a very short span. The article says that a human eye, "representing the entirety of humanity," appears for just 1/24 of a second. **Ending device:** The article doesn't describe a closing device. The closest it comes is the human-eye shot near the end of the timeline. **Release year:** 2018. It first went up on March 7, was taken down over a copyright claim involving Morgan Freeman's voice, and was re-uploaded without that voice on March 10. **Length:** 10 minutes 49 seconds. The 15:40 figure in the article is for the separate music EP, not the film. **Reception:** Generally positive. Alex Shoolman called it an "amazing overview" of the universe's formation, and Laughing Squid praised its "mesmerizing soundtrack," a mashup of four narrators.
打开 themarginalian.org/2010/12/03/hans-rosling-bbc/
{ "prompt": "Describe the storytelling technique of Hans Rosling's '200 Countries, 200 Years, 4 Minutes': how data animation, the presenter, a year counter and narration work together, and what makes it effective.", "url": "https://www.themarginalian.org/2010/12/03/hans-rosling-bbc/" }The article describes a BBC *Joy of Stats* segment in which Hans Rosling condenses roughly 200 countries and 200 years of data into about four minutes. - **Data animation:** The main device is animated income and life expectancy data, shown with augmented reality. The article argues that watching these figures shift over time makes history more visceral than static charts or text. - **Presenter:** Rosling narrates in what the article calls his "signature sports commentator style," which turns the timeline into something the viewer follows as it unfolds. - **Year counter and narration:** The article doesn't mention a year counter or describe how the timeline is marked. It says Rosling's narration links the data to events such as colonization, the Industrial Revolution, and WWII. - **Effectiveness:** According to the article, the animation shows how historical and political turning points played out in the data, which gives those events context that a history book or verbal account might not provide. The article presents this as a case for data visualization as a storytelling device.
打开 bespacific.com
{ "prompt": "What is 'The historical tech tree' project? Describe its visual design (how technologies are arranged and linked over time) and any lessons for drawing a technology tree.", "url": "https://www.bespacific.com/?p=114691" }The historical tech tree is a project by Étienne Fortier-Dubois that visualizes the history of technologies, inventions, and (some) discoveries from prehistory to today. Its distinguishing feature is that it maps the "prerequisites, improvements, inspirations, and so on" that connect technologies, rather than only placing them in time. The excerpt doesn't describe the visual layout in detail, so I can't say how nodes are positioned, colored, or grouped. It does say that the connections reveal history "in more detail than a simple timeline," which suggests a network of linked technologies arranged across time. Lessons for drawing a tech tree, based on the excerpt: - **Show relationships, not just dates.** Linking prerequisites, improvements, and inspirations helps viewers see how technologies arose. - **Don't force a single path.** Game tech trees can make progress look predetermined, which the author sees as a problem. - **Examine cultural assumptions about progress.** Game trees often rely on them, and a historical tree should be aware of its own. - **Prioritize accuracy over simplicity.** Game trees optimize for fun, while this project aims for a more faithful picture of history. - **State your constraints.** The author acknowledges the tree has assumptions, just different ones from game trees. - **Design for exploration.** The goal is to help people discover unexpected patterns and make modern technological complexity feel less daunting.
思考：Researching the Cosmic Calendar analogy for human history's …
Researching the Cosmic Calendar analogy for human history's timescale.
Collecting pacing techniques and drafting a critique of the first version.
Listing v1 flaws and sketching a redesigned opening sequence.
Planning sound design, layout fixes, and visual upgrades for v2.
Research done. Next I'm looking at how the photos look and reviewing v1 frame by frame to make the problem list.
干了 3 分钟，11 步
读 2 个文件，运行 3 条命令，其他操作 6 次
运行 mkdir -p ~/ai-tree/assets/photos && cd ~/ai-tree/assets/p…
mkdir -p ~/ai-tree/assets/photos && cd ~/ai-tree/assets/photos && cp /mnt/user-data/uploads/ai-tree-video/photos/credits.json . && python3 - <<'EOF' from PIL import Image src = "/mnt/user-data/uploads/ai-tree-video/photos/" ims = [] for k, f in [("turing", "turing.jpg"), ("perceptron", "perceptron.png"), ("shakey", "shakey.jpg"), ("deepblue", "deepblue.jpg"), ("stanley", "stanley.jpg")]: im = Image.open(src + f).convert("RGB") im.thumbnail((900, 900)) im.save(f"{k}.jpg", quality=92) print(k, im.size) ims.append(im) W = 360; sheet = Image.new("RGB", (W * 5, 420), "white") for i, im in enumerate(ims): t = im.copy(); t.thumbnail((W - 10, 410)); sheet.paste(t, (i * W + 5, 5)) sheet.save("/tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/photos.png") EOFturing (675, 900) perceptron (900, 729) shakey (675, 900) deepblue (400, 601) stanley (900, 586)
读取 photos.png
{ "file_path": "/tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/photos.png" }[图片]
思考：Designing a polaroid-style photo layout with crop sizing. Ve…
Designing a polaroid-style photo layout with crop sizing.
Verifying each photo's orientation, focus point, and sourcing.
Mapping the teaser's rewind and title timing in scene time.
Designing a readout that displays how fast scene time flows.
Sketching a speed gauge icon and calibrating the second scene's camera.
Laying out the scrolling note feed and counter cards.
Resolving overlaps between term cards, polaroids, and feed placement.
Finalizing the layout plan and designing winter foliage effects.
Choreographing bloom, chat-era bursts, and layered sound design.
Aligning music tempo grid with timeline event timestamps.
Designing the day-clock funnel visual linking real time and video time.
Planning the readout layout, photo framing, and remaining production tasks.思考：Outlining v2 revision fixes from the v1 video review
Outlining v2 revision fixes from the v1 video review
运行 cd ~/Movies/ai-tree-video/voice && mkdir -p v1 && cp s02.mp3 s07.mp3 timing.jso…
cd ~/Movies/ai-tree-video/voice && mkdir -p v1 && cp s02.mp3 s07.mp3 timing.json v1/ && python3 - <<'EOF' import re src = open("tts.py").read() src = src.replace('("s02", "1958年，有人造出了模仿大脑的“感知机”，这是神经网络的第一颗芽。可没过几年，它就被一本书泼了冷水，冻住了。"),', '("s02", "1958年，有人造出了模仿大脑的“感知机”，这是神经网络的第一颗芽。可十来年后，它被一本书泼了冷水，冻住了。"),') src = src.replace('("s07", "接下来的三年，时间好像变慢了——因为几乎每个月都有大事。"),', '("s07", "如果把这七十多年压缩成一天，最近这三年，只是最后的五十多分钟。可接下来，我要用一半的时间来讲它——因为几乎每个星期，都有大事。"),') assert "十来年后" in src and "压缩成一天" in src open("tts.py", "w").write(src) EOF ../.venv/bin/python tts.py && for f in s02 s07; do printf "%s %s\n" $f $(ffprobe -v error -show_entries format=duration -of csv=p=0 $f.mp3); doneProcess started with PID 76154 (shell: /bin/zsh) Initial output: s02 30 boundaries s07 36 boundaries s02 9.960000 s07 11.304000 ✅ Process 76154 has finished execution
准备从你电脑传回文件
{ "paths": [ "~/Movies/ai-tree-video/voice/s02.mp3", "~/Movies/ai-tree-video/voice/s07.mp3", "~/Movies/ai-tree-video/voice/timing.json" ] }{"staged":[{"devicePath":"~/Movies/ai-tree-video/voice/s02.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s02.mp3","mtimeMs":1792030314295,"bytes":59760,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/s07.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s07.mp3","mtimeMs":1792030317000,"bytes":67824,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/timing.json","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/timing.json","mtimeMs":1792030317014,"bytes":39500,"ok":true,"dispatched":true}]}更新待办
{ "status": "completed", "taskId": "8" }Updated task #8 status
更新待办
{ "status": "completed", "taskId": "9" }Updated task #9 status
更新待办
{ "status": "completed", "taskId": "10" }Updated task #10 status
更新待办
{ "status": "completed", "taskId": "11" }Updated task #11 status
更新待办
{ "status": "in_progress", "taskId": "12" }Updated task #12 status
运行 cd ~/ai-tree && cp /mnt/user-data/uploads/ai-tree-video/voice/s02.mp…
cd ~/ai-tree && cp /mnt/user-data/uploads/ai-tree-video/voice/s02.mp3 /mnt/user-data/uploads/ai-tree-video/voice/s07.mp3 /mnt/user-data/uploads/ai-tree-video/voice/timing.json audio/voice/ && python3 -c " import json d = json.load(open('audio/voice/timing.json')) for k in ['s02','s07','s00']: print(k, d[k]['text']); print(' ', ' '.join(f\"{w['w']}@{w['t']}\" for w in d[k]['words'])) "s02 1958年，有人造出了模仿大脑的“感知机”，这是神经网络的第一颗芽。可十来年后，它被一本书泼了冷水，冻住了。 1958@0.098 年@0.657 有人@1.264 造出@1.514 了@1.788 模仿@1.883 大脑@2.181 的@2.455 感知机@2.55 这@3.502 是@3.645 神经@3.752 网络@4.121 的@4.383 第一@4.526 颗@4.776 芽@4.919 可@5.738 十来年@5.881 后@6.298 它@6.94 被@7.095 一本@7.19 书@7.44 泼@7.619 了@7.762 冷水@7.845 冻@8.607 住@8.81 了@8.952 s07 如果把这七十多年压缩成一天，最近这三年，只是最后的五十多分钟。可接下来，我要用一半的时间来讲它——因为几乎每个星期，都有大事。 如果@0.098 把@0.36 这@0.479 七十多@0.61 年@1.014 压缩@1.264 成@1.526 一天@1.693 最近@2.49 这@2.812 三@2.907 年@3.121 只是@3.621 最后@3.871 的@4.169 五十@4.264 多@4.514 分钟@4.621 可@5.56 接下来@5.726 我@6.548 要@6.679 用@6.774 一半@6.905 的@7.179 时间@7.262 来讲@7.5 它@7.798 因为@8.429 几乎@8.679 每@8.917 个@9.06 星期@9.143 都@9.786 有@9.952 大事@10.06 s00 这是一棵树的故事。它长了七十多年，最近这三年，长疯了。 这是@0.098 一@0.371 棵@0.479 树@0.598 的@0.788 故事@0.871 它@1.833 长@1.988 了@2.143 七十多@2.238 年@2.655 最近@3.31 这@3.619 三@3.726 年@3.952 长疯@4.44 了@4.905
思考：Building a timeline script for narrated video beats. Mapping…
Building a timeline script for narrated video beats.
Mapping out tempo changes across the later sections.读取 film.js
{ "file_path": "~/ai-tree/scene/film.js" }1 // The whole film as a pure function of time: window.seek(t) draws the frame at t seconds. 2 (() => { 3 const TL = window.TL; 4 const GROUND = 950; 5 6 // ================================================================ time ↔ calendar 7 const YK = TL.ykeys; 8 function yearAt(t) { 9 if (t <= YK[0][0]) return YK[0][1]; 10 for (let i = 1; i < YK.length; i++) { 11 const [ta, ya] = YK[i - 1], [tb, yb] = YK[i]; 12 if (t <= tb) return tb > ta ? ya + (yb - ya) * (t - ta) / (tb - ta) : yb; 13 } 14 return YK[YK.length - 1][1]; 15 } 16 const tCache = new Map(); 17 function timeOf(y) { // first moment the calendar reaches y 18 if (tCache.has(y)) return tCache.get(y); 19 let r = Infinity; 20 if (y <= YK[0][1]) r = YK[0][0]; 21 else for (let i = 1; i < YK.length; i++) { 22 const [ta, ya] = YK[i - 1], [tb, yb] = YK[i]; 23 if (y <= yb && yb > ya) { r = ta + (y - ya) / (yb - ya) * (tb - ta); break; } 24 } 25 tCache.set(y, r); return r; 26 } 27 const beat = (id) => TL.beats.find(b => b.id === id); 28 const PART2 = beat('b7').t0, ENDT = beat('end').t0; 29 30 // ================================================================ tree layout 31 const TRUNK = [[1000, 958], [985, 760], [1022, 480], [1000, 200]]; 32 const FRAC = [[1950, 0], [1956, 0.12], [1966, 0.20], [1975, 0.26], [1980, 0.30], [1993, 0.40], [1997, 0.43], [2011, 0.53], [2012, 0.54], 33 [2017, 0.62], [2020, 0.67], [2023.99, 0.80], [2025, 0.90], [2026.8, 1.0]]; 34 function fracOf(y) { …（中间省略 42729 字）… d(`700 24px ${HAND}`, all), document.fonts.load(`150px ${BRUSH}`, 'AI这棵树0123456789')]); 644 await loadPhotos(['turing', 'perceptron', 'shakey', 'deepblue', 'stanley']); 645 buildPaper(); 646 window.seek = seek; window.TOTAL = TL.total; window.__ready = true; 647 window.timeSeek = (t) => { const a = performance.now(); seek(t); return performance.now() - a; }; 648 } 649 init(); 650 })(); 651思考：Rewriting film.js and updating the timeline build script. De…
Rewriting film.js and updating the timeline build script.
Detailing beat timing, marks, and tempo section changes.
Designing camera, seasons, and overlay effects for the film script.
Mapping the intro teaser's seed, fast-forward, and rewind phases.
Syncing the rewind glyph and planning the camera pull-back.
Working out seasonal snow, falling leaves, and blossom effects.
Choreographing bloom, crown pulse, and overlay card effects.
Building the day-compressed timeline overlay card and markers.
Calculating the time-rate readout and vocab layout.
Positioning the feed counters, note cards, and term cards.
Designing polaroid frames, developing effects, and ending credits.
调整宝丽来照片位置以避免遮挡分支标签。
重新规划宝丽来照片与词汇表、术语卡的版面布局。
确定日期与简述新闻条目的具体内容。
继续列出 2024 至 2026 年的新闻条目内容。
核实 2025 至 2026 年新闻条目的具体日期与描述。
Starting on v2. First the timeline: the new opening, the new transition section, a tempo map, and snapping the sticky notes to the beat.
干了不到 1 分钟，3 步
读 1 个文件，运行 2 条命令
读取 build_timeline.py
{ "file_path": "~/ai-tree/tools/build_timeline.py", "limit": 60, "offset": 1 }1 """Build the film timeline from the narration timings. 2 3 Inputs : audio/voice/timing.json + audio/voice/sNN.mp3 (synthesized on the user's Mac) 4 Outputs: scene/timeline.js (window.TL = {...}, read by the renderer) 5 audio/narration.wav (all narration segments placed at their start times) 6 """ 7 import json, subprocess, datetime 8 from pathlib import Path 9 10 ROOT = Path(__file__).resolve().parent.parent 11 VOICE = ROOT / "audio" / "voice" 12 TIMING = json.loads((VOICE / "timing.json").read_text()) 13 14 15 def dur(path): 16 return float(subprocess.check_output(["ffprobe", "-v", "error", "-show_entries", "format=duration", "-of", "csv=p=0", str(path)]).decode()) 17 18 19 def Y(iso): # calendar date -> fractional year 20 d = datetime.date.fromisoformat(iso) 21 start = datetime.date(d.year, 1, 1) 22 days = (datetime.date(d.year + 1, 1, 1) - start).days 23 return d.year + (d - start).days / days 24 25 26 # ---------------------------------------------------------------- beats 27 # anchors: (word to match in the narration, calendar year at the moment that word is spoken) 28 BEATS = [ 29 dict(id="intro", seg="s00", pre=2.2, post=1.0, y0=1950.0, y1=1950.0, anchors=[]), 30 dict(id="b1", seg="s01", pre=0.2, post=0.9, y0=1950.0, y1=1957.6, anchors=[("1950", 1950.0), ("六年", 1955.4), ("人工智能", 1956.5)]), 31 dict(id="b2", seg="s02", pre=0.2, post=1.0, y0=1957.6, y1=1970.0, anchors=[("1958", 1958.0), ("没过", 1964.0), ("冻住", 1969.4)]) …（中间省略 1965 字）… ("expert", "b3", "专家系统"), 54 ("backprop", "b4", "后来"), ("ml", "b4", "机器学习"), ("dl", "b5", "深度"), 55 ("rl", "b5", "自己"), ("transformer", "b5", "Transformer"), ("llm", "b6", "越聪明"), 56 ("reason", "b8", "先"), ("agent", "b9", "智能体"), ("embodied", "b10", "多模态"), 57 ] 58 59 # polaroids: (id, beat, trigger word, seconds on screen); "photo:" ids use real photos when available 60 POLAROIDS = [运行 python3 - <<'EOF'
python3 - <<'EOF' from pathlib import Path p = Path("tools/build_timeline.py"); s = p.read_text() def rep(a, b, count=1): global s assert s.count(a) == count, (a[:60], s.count(a)) s = s.replace(a, b) # ---- beats rep(''' dict(id="intro", seg="s00", pre=2.2, post=1.0, y0=1950.0, y1=1950.0, anchors=[]),''', ''' dict(id="intro", seg="s00", pre=0.8, post=4.4, y0=1950.0, y1=1950.0, anchors=[]),''') rep('''anchors=[("1958", 1958.0), ("没过", 1964.0), ("冻住", 1969.4)]),''', '''anchors=[("1958", 1958.0), ("十来年", 1966.5), ("冻住", 1969.4)]),''') rep(''' dict(id="b7", seg="s07", pre=0.6, post=0.9, y0=2024.0, y1=2024.0, anchors=[]),''', ''' dict(id="b7", seg="s07", pre=0.6, post=1.5, y0=2024.0, y1=2024.0, anchors=[]),''') # ---- notes (v2: shorter, one Nobel note) a = s.index("NOTES = [") b = s.index("]\n", a) + 2 s = s[:a] + '''NOTES = [ ("2024-02-15", "一行字生成电影般的视频", "film", "paint"), ("2024-05-13", "AI 能实时语音聊天，随时打断", "talk", "speech"), ("2024-06-18", "英伟达市值全球第一", "chip", None), ("2024-07-10", "武汉无人驾驶出租车火了", "car", "robot"), ("2024-07-17", "AI 答错：9.11 比 9.9 大？", "cross …（中间省略 5218 字）… ptions": captions, "cards": cards, "polaroids": polaroids, "notes": notes, "counters": counters, "segs": segs, "marks": marks, "tempo": tempo}''') p.write_text(s) print("patched") EOF grep -n "def t_of_y" -A6 tools/build_timeline.py | head -8; grep -n "^TOTAL\|TOTAL = " tools/build_timeline.pypatched 256:def t_of_y(yv): 257- for (ta, ya), (tb, yb) in zip(mono, mono[1:]): 258- if ya <= yv <= yb and yb > ya: 259- return ta + (yv - ya) / (yb - ya) * (tb - ta) 260- return None 261- 262- 237:TOTAL = round(t, 3)
运行 python3 tools/build_timeline.py && python3 -c "
python3 tools/build_timeline.py && python3 -c " import json s=open('scene/timeline.js').read(); d=json.loads(s[len('window.TL = '):].rstrip().rstrip(';')) print('marks', json.dumps(d['marks'], ensure_ascii=False)) print('tempo'); [print(' ', x) for x in d['tempo']] print('notes', len(d['notes']), [n['t'] for n in d['notes']][:12]) print('intro captions', [(c['t0'], c['text']) for c in d['captions'][:4]]) "total 258.3s | beats 12 | captions 77 | cards 12 | polaroids 16 | notes 40 intro 0.00 → 11.10 b1 11.10 → 22.67 b2 22.67 → 33.83 b3 33.83 → 55.89 b4 55.89 → 72.61 b5 72.61 → 96.52 b6 96.52 → 111.25 b7 111.25 → 124.66 b8 124.66 → 149.69 b9 149.69 → 184.44 b10 184.44 → 231.01 end 231.01 → 258.26 marks {"ff0": 2.183, "ff1": 4.89, "rw0": 6.854, "rw1": 8.054, "intro_end": 11.104, "seed_T": 11.154, "crazy_T0": 213.166, "crazy_T1": 221.13, "day_bar": 112.766, "sliver": 115.573, "half": 118.607, "weekly": 120.795, "day_out": 123.956, "bloom": 72.873, "chatgpt": 102.874, "winter1": 40.783, "spring1": 44.132, "winter2": 51.434, "spring2": 55.609, "wither": 51.259} tempo {'t0': 0.0, 't1': 11.104, 'bpm': 92, 'style': 'intro'} {'t0': 11.104, 't1': 40.783, 'bpm': 84, 'style': 'calm'} {'t0': 40.783, 't1': 44.132, 'bpm': 70, 'style': 'winter'} {'t0': 44.132, 't1': 51.434, 'bpm': 84, 'style': 'calm'} {'t0': 51.434, 't1': 55.609, 'bpm': 70, 'style': 'winter'} {'t0': 55.609, 't1': 72.873, 'bpm': 84, 'style': 'calm'} {'t0': 72.873, 't1': 111.252, 'bpm': 90, 'style': 'spring'} {'t0': 111.252, 't1': 124.656, 'bpm': 60, 'style': 'clock'} {'t0': 124.656, 't1': 149.688, 'bpm': 100, 'style': 'drive'} {'t0': 149.688, 't1': 184.44, 'bpm': 106, 'style': 'drive'} {'t0': 184.44, 't1': 231.008, 'bpm': 112, 'style': 'drive2'} {'t0': 231.008, 't1': 258.256, 'bpm': 76, 'style': 'end'} notes 40 [127.056, 128.856, 129.756, 132.456, 133.056, 136.956, 140.256, 147.456, 148.056, 149.556, 151.952, 152.801] intro captions [(0.898, '这是一棵树的故事'), (2.633, '它长了七十多年，最近这三年，长疯了'), (11.402, '1950年，图灵问：机器能思考吗'), (15.125, '六年后，一群科学家在达特茅斯开会')]思考：Timing the winter segments in the video. Mapping scene time …
Timing the winter segments in the video.
Mapping scene time to real playback speed changes.
Adding snow-capped limbs to the tree animation.
Scattering falling leaves tied to the winter transitions.
Choreographing a spring bloom with petals and flower sites.
Precomputing flower positions and animating a chat notification burst.
Building a timeline card compressing AI history into a day.
Adding the final-minutes marker and this-video comparison bars.
Resolving layout overlaps between vocabulary cards and photo frames.
Designing teaser overlays and a seed-sprout intro animation.
Now the main rewrite of the animation (film.js), covering the new opening, seasons, the bloom, the transition card, the time-speed readout, the event feed, and the photos.
干了 10 分钟，21 步
读 8 个文件，运行 12 条命令，写 1 个文件
运行 sed -n '1,40p' scene/art.js | head -5 && grep -n "^const ILL\|^const PHOTO_SKET…
sed -n '1,40p' scene/art.js | head -5 && grep -n "^const ILL\|^const PHOTO_SKETCH\|^function loadPhotos\|^const ICONS\|^const DOODLES" scene/art.js && grep -n "LEAF_PAL = {" -A4 scene/lib.js// Doodles: note icons, term-card pictures, polaroid illustrations and photo fallbacks. const INK = { stroke: C.ink, strokeWidth: 1.6, roughness: 0.8 }; const o = (seed, extra = {}) => Object.assign({}, INK, { seed: sd(seed) }, extra); // ---------------------------------------------------------------- tiny note icons (≈ 34px box centred on x,y) 6:const ICONS = { 43:const DOODLES = { 80:const ILL = { 142:const PHOTO_SKETCH = { 160:function loadPhotos(ids) { 81:const LEAF_PAL = { 82- old: ['#9ab57d', '#adc290', '#8aa870'], mid: ['#7fae5a', '#95bf68', '#6c9c4e'], 83- fresh: ['#8cc152', '#a8d15f', '#6fae46', '#c3d95a'], gold: ['#e3b84f', '#f0cf6a', '#d9a441', '#c3d95a'], 84- grey: ['#c4bdb0', '#b5ad9f', '#cbc3b4'], 85-};运行 python3 - <<'EOF'
python3 - <<'EOF' from pathlib import Path p = Path("scene/lib.js"); s = p.read_text() s = s.replace(""" grey: ['#c4bdb0', '#b5ad9f', '#cbc3b4'], };""", """ grey: ['#c4bdb0', '#b5ad9f', '#cbc3b4'], brown: ['#c9a36b', '#b88a55', '#d8b77e'], };""") p.write_text(s) EOF grep -n "brown:" scene/lib.js84: grey: ['#c4bdb0', '#b5ad9f', '#cbc3b4'], brown: ['#c9a36b', '#b88a55', '#d8b77e'],
写入 film.js
// AI 这棵树 — v2. The whole film is a pure function of time: window.seek(t) draws the frame at t seconds. // Scene elements run on "scene time" T (which differs from real time only during the teaser at the start); // overlays (captions, title, term cards, photos, the one-day card) run on real time t. (() => { const TL = window.TL, M = TL.marks; const GROUND = 950; // ================================================================ time ↔ calendar const YK = TL.ykeys; function yearAt(t) { if (t <= YK[0][0]) return YK[0][1]; for (let i = 1; i < YK.length; i++) { const [ta, ya] = YK[i - 1], [tb, yb] = YK[i]; if (t <= tb) return tb > ta ? ya + (yb - ya) * (t - ta) / (tb - ta) : yb; } return YK[YK.length - 1][1]; } const tCache = new Map(); function timeOf(y) { // first moment the calendar reaches y if (tCache.has(y)) return tCache.get(y); let r = Infinity; if (y <= YK[0][1]) r = YK[0][0]; else for (let i = 1; i < YK.length; i++) { const [ta, ya] = YK[i - 1], [tb, yb] = YK[i]; if (y <= yb && yb > ya) { r = ta + (y - ya) / (yb - ya) * (tb - ta); break; } } tCache.set(y, …（中间省略 57566 字）… 这棵树0123456789')]); await loadPhotos(['turing', 'perceptron', 'shakey', 'deepblue', 'stanley']); buildPaper(); window.seek = seek; window.TOTAL = TL.total; window.__ready = true; window.timeSeek = (t) => { const a = performance.now(); seek(t); return performance.now() - a; }; } init(); })();The file ~/ai-tree/scene/film.js has been updated successfully. (file state is current in your context — no need to Read it back)
思考：Debugging a stray rendering line in the day card function. P…
Debugging a stray rendering line in the day card function.
Positioning time labels and markers along the timeline.
Checking feed animation timing and overlapping layout elements.
Checking caption boxes and seed placement for overlap at key timestamps.运行 python3 - <<'EOF'
python3 - <<'EOF' from pathlib import Path p = Path("scene/film.js"); s = p.read_text() def rep(a, b): global s assert s.count(a) == 1, a[:70] s = s.replace(a, b) rep(""" rc.line(x, Yb - 12, x, Yb - lift + 14, { stroke: C.red, strokeWidth: 1.6, roughness: 0.6, seed: sd(8040 + i) }); rc.circle(x, Yb, 12, { fill: C.red, fillStyle: 'solid', stroke: C.red, roughness: 0.5, seed: sd(8050 + i) }); text(s, x, Yb - lift, { size: 24, weight: 700, align: i === 3 ? 'right' : 'center', yearBold: false }); text(hhmm(hourOf(y)), x, Yb - lift + 30 - (i % 2 ? 0 : 0) + 0, { size: 0.01, yearBold: false });""", """ rc.line(x, Yb - 12, x, Yb - lift + 30, { stroke: C.red, strokeWidth: 1.6, roughness: 0.6, seed: sd(8040 + i) }); rc.circle(x, Yb, 12, { fill: C.red, fillStyle: 'solid', stroke: C.red, roughness: 0.5, seed: sd(8050 + i) }); const al = i === 3 ? 'right' : 'center', xx = i === 3 ? x + 8 : x; text(s, xx, Yb - lift - 6, { size: 24, weight: 700, align: al, yearBold: false }); text(hhmm(hourOf(y)), xx, Yb - lift + 20, { size: 20, weight: 700, color: C.red, align: al, yearBold: f …（中间省略 1401 字）… ]); ctx.scale(cam.z, cam.z); ctx.translate(-CAM_A[0], -CAM_A[1]);""", """ ctx.save(); ctx.translate(CAM_A[0] + cam.dx, CAM_A[1] + CAM_DY); ctx.scale(cam.z, cam.z); ctx.translate(-CAM_A[0], -CAM_A[1]);""") p.write_text(s) print("ok") EOF python3 tools/frames.py 0.5 3.5 5.5 7.4 9.5 2>&1 | tail -6ok t=0.5: draw 45 ms, total 915 ms t=3.5: draw 123 ms, total 832 ms t=5.5: draw 42 ms, total 683 ms t=7.4: draw 21 ms, total 688 ms t=9.5: draw 3 ms, total 708 ms errors: none
运行 cd ~/ai-tree/out/frames && python3 -c "
cd ~/ai-tree/out/frames && python3 -c " from PIL import Image fs=['t0000.50.png','t0003.50.png','t0005.50.png','t0007.40.png','t0009.50.png'] ims=[Image.open(f) for f in fs]; W,H=960,540; sh=Image.new('RGB',(W*2,H*3),'white') for i,im in enumerate(ims): sh.paste(im.resize((W,H)),((i%2)*W,(i//2)*H)) sh.save('v2_teaser.png')"(Bash completed with no output)
读取 v2_teaser.png
{ "file_path": "~/ai-tree/out/frames/v2_teaser.png" }[图片]
思考：Animating a seed-growth scene frame by frame. Repositioning …
Animating a seed-growth scene frame by frame.
Repositioning overlapping labels and tuning zoom offsets.
Recalculating zoom and offset to clear more overlaps.运行 cd ~/ai-tree && python3 - <<'EOF'
cd ~/ai-tree && python3 - <<'EOF' from pathlib import Path p = Path("scene/film.js"); s = p.read_text() def rep(a, b): global s assert s.count(a) == 1, a[:70] s = s.replace(a, b) rep(""" let z = camZoom(Y) * lerp(1, 0.84, p2), dx = lerp(0, -70, p2);""", """ let z = camZoom(Y) * lerp(1, 0.8, p2), dx = lerp(0, -90, p2);""") # stamp: upper-left of the crown, clear of the branch names rep(""" ctx.save(); ctx.globalAlpha *= sa * 0.9; ctx.translate(cs.x + cs.r + 150, cs.y - cs.r * 0.35); ctx.rotate(-0.13); ctx.scale(sc, sc);""", """ ctx.save(); ctx.globalAlpha *= sa * 0.9; ctx.translate(cs.x - cs.r - 170, cs.y - cs.r - 40); ctx.rotate(-0.13); ctx.scale(sc, sc);""") # rewind caption gets a backing rep(""" text('倒回去，从头说起', 960, 650, { size: 40, weight: 700, color: C.ink, align: 'center', yearBold: false });""", """ ctx.fillStyle = 'rgba(255,250,240,0.9)'; ctx.fillRect(760, 612, 400, 56); text('倒回去，从头说起', 960, 652, { size: 40, weight: 700, color: C.ink, align: 'center', yearBold: false });""") # balanced note wrapping, no orphans a = s.index("const noteCache = new Map();") b = s. …（中间省略 1406 字）… from PIL import Image fs=['t0014.00.png','t0024.00.png','t0042.00.png','t0053.00.png','t0062.00.png','t0066.00.png'] ims=[Image.open(f) for f in fs]; W,H=960,540; sh=Image.new('RGB',(W*2,H*3),'white') for i,im in enumerate(ims): sh.paste(im.resize((W,H)),((i%2)*W,(i//2)*H)) sh.save('v2_part1a.png')"ok t=66: draw 12 ms, total 575 ms errors: none
读取 v2_part1a.png
{ "file_path": "~/ai-tree/out/frames/v2_part1a.png" }[图片]
思考：Checking each image frame for caption placement issues. Diag…
Checking each image frame for caption placement issues.
Diagnosing faint branch labels and an odd trunk-top shape.运行 cd ~/ai-tree && python3 - <<'EOF'
cd ~/ai-tree && python3 - <<'EOF' from pathlib import Path p = Path("scene/film.js"); s = p.read_text() a = """ const na = clamp((g - 0.55) / 0.3);""" assert s.count(a) == 1 s = s.replace(a, """ const na = clamp((g - 0.3) / 0.25);""") p.write_text(s) EOF python3 tools/frames.py 73.4 75 81 88 103.3 104.5 2>&1 | tail -1; cd out/frames && python3 -c " from PIL import Image fs=['t0073.40.png','t0075.00.png','t0081.00.png','t0088.00.png','t0103.30.png','t0104.50.png'] ims=[Image.open(f) for f in fs]; W,H=960,540; sh=Image.new('RGB',(W*2,H*3),'white') for i,im in enumerate(ims): sh.paste(im.resize((W,H)),((i%2)*W,(i//2)*H)) sh.save('v2_part1b.png')"errors: none
读取 v2_part1b.png
{ "file_path": "~/ai-tree/out/frames/v2_part1b.png" }[图片]
思考：Checking timing of on-screen term cards against the animatio…
Checking timing of on-screen term cards against the animation.
Checking label positions for overlap near the crown.运行 cd ~/ai-tree && python3 - <<'EOF'
cd ~/ai-tree && python3 - <<'EOF' from pathlib import Path p = Path("scene/film.js"); s = p.read_text() a = """ return Math.min(easeOut((t - b.tBorn) / 2.4), clamp(0.3 + (Y - b.born) * 0.03));""" assert s.count(a) == 1 s = s.replace(a, """ return Math.min(easeOut((t - b.tBorn) / 2.4), clamp(0.3 + (Y - b.born) * (b.born < 1990 ? 0.03 : 0.12)));""") p.write_text(s) EOF python3 tools/frames.py 113.6 116.5 119.5 122.3 2>&1 | tail -1; cd out/frames && python3 -c " from PIL import Image fs=['t0113.60.png','t0116.50.png','t0119.50.png','t0122.30.png'] ims=[Image.open(f) for f in fs]; W,H=960,540; sh=Image.new('RGB',(W*2,H*2),'white') for i,im in enumerate(ims): sh.paste(im.resize((W,H)),((i%2)*W,(i//2)*H)) sh.save('v2_day.png')"errors: none
读取 v2_day.png
{ "file_path": "~/ai-tree/out/frames/v2_day.png" }[图片]
思考：Fixing oversized watercolor washes in the day card's bottom …
Fixing oversized watercolor washes in the day card's bottom bar.
Fixing overlapping labels and fading the background panel behind the card.运行 cd ~/ai-tree && python3 - <<'EOF'
cd ~/ai-tree && python3 - <<'EOF' from pathlib import Path p = Path("scene/film.js"); s = p.read_text() def rep(a, b): global s assert s.count(a) == 1, a[:80] s = s.replace(a, b) rep(""" wc((a + b) / 2, Yb, (b - a) / 2 + 10, '#f2a65a', 8060, 0.6); rc.ellipse((a + b) / 2, Yb, (b - a) + 46, 60, { stroke: C.red, strokeWidth: 2.4, roughness: 1.3, seed: sd(8061) }); text('最后 52 分钟', b + 30, Yb + 74, { size: 30, weight: 700, color: C.red, align: 'right', yearBold: false }); text('= 最近三年', b + 30, Yb + 108, { size: 24, color: C.red, align: 'right', yearBold: false });""", """ ctx.fillStyle = 'rgba(242,150,70,0.75)'; ctx.fillRect(a, Yb - 13, b - a, 26); rc.ellipse((a + b) / 2, Yb, (b - a) + 40, 48, { stroke: C.red, strokeWidth: 2.4, roughness: 1.3, seed: sd(8061) }); text('最后 52 分钟', a - 24, Yb + 76, { size: 32, weight: 700, color: C.red, align: 'right', yearBold: false }); text('= 最近三年', a - 24, Yb + 110, { size: 25, color: C.red, align: 'right', yearBold: false });""") rep(""" wc((X0 + mid) / 2, Yv, (mid - X0) / 2 - 4, '#c9c3b8', 8072, 0.35); wc((mid + X1) / 2, Yv, …（中间省略 1063 字）… & python3 -c " from PIL import Image fs=['t0119.50.png','t0128.00.png','t0136.00.png','t0150.60.png','t0158.00.png'] ims=[Image.open(f) for f in fs]; W,H=960,540; sh=Image.new('RGB',(W*2,H*3),'white') for i,im in enumerate(ims): sh.paste(im.resize((W,H)),((i%2)*W,(i//2)*H)) sh.save('v2_part2a.png')"ok errors: none
读取 v2_part2a.png
{ "file_path": "~/ai-tree/out/frames/v2_part2a.png" }[图片]
思考：Reviewing layout spacing between feed and tree elements. Fix…
Reviewing layout spacing between feed and tree elements.
Fixing line breaks to avoid splitting numbers and words.运行 cd ~/ai-tree && python3 - <<'EOF'
cd ~/ai-tree && python3 - <<'EOF' from pathlib import Path import re p = Path("scene/film.js"); s = p.read_text() a = s.index("function noteLines(str) {") b = s.index("const FEED_X") s = s[:a] + r"""function noteLines(str) { // wrap to 284px at 24px; break at punctuation if possible, never inside a latin/number word if (noteCache.has(str)) return noteCache.get(str); const width = (x) => text(x, 0, 0, { size: 24, measureOnly: true, yearBold: false }).w, MAXW = 284; const chars = [...str]; const isWord = (c) => /[A-Za-z0-9.\-]/.test(c); const canBreakAfter = (i) => i < chars.length - 1 && !(isWord(chars[i]) && isWord(chars[i + 1])) && chars[i + 1] !== '，' && chars[i + 1] !== '：' && chars[i + 1] !== '、' && chars[i + 1] !== '）'; const punctAfter = (i) => '，：、；）'.includes(chars[i]) || chars[i + 1] === '（'; function split(cs) { const s2 = cs.join('').trim(); if (width(s2) <= MAXW) return [s2]; let best = -1, bestScore = 1e9; for (let i = 1; i < cs.length - 1; i++) { if (!canBreakAfterIn(cs, i)) continue; const l1 = cs.slice(0, i + 1).join('').trim(), l2 = cs.slice(i + 1) …（中间省略 1580 字）… fs=['t0133.00.png','t0140.00.png','t0147.00.png','t0165.00.png','t0172.00.png'] ims=[Image.open(f).crop((1490,0,1920,1080)) for f in fs]; sh=Image.new('RGB',(430*5,1080),'white') for i,im in enumerate(ims): sh.paste(im,(i*430,0)) sh=sh.resize((sh.width*2//3, sh.height*2//3)); sh.save('v2_feed.png')"ok 8 errors: none
读取 v2_feed.png
{ "file_path": "~/ai-tree/out/frames/v2_feed.png" }[图片]
思考：整理信息流的排版与换行效果。
整理信息流的排版与换行效果。
运行 cd ~/ai-tree && python3 tools/frames.py 186 192 205 215.5 229 236 2>…
cd ~/ai-tree && python3 tools/frames.py 186 192 205 215.5 229 236 2>&1 | tail -1; cd out/frames && python3 -c " from PIL import Image fs=['t0186.00.png','t0192.00.png','t0205.00.png','t0215.50.png','t0229.00.png','t0236.00.png'] ims=[Image.open(f) for f in fs]; W,H=960,540; sh=Image.new('RGB',(W*2,H*3),'white') for i,im in enumerate(ims): sh.paste(im.resize((W,H)),((i%2)*W,(i//2)*H)) sh.save('v2_part2b.png')"errors: none
读取 v2_part2b.png
{ "file_path": "~/ai-tree/out/frames/v2_part2b.png" }[图片]
思考：检查文案的换行是否自然流畅。 给换行点添加手动标记来优化排版。 逐条确认各条文案的换行标记。
检查文案的换行是否自然流畅。
给换行点添加手动标记来优化排版。
逐条确认各条文案的换行标记。运行 cd ~/ai-tree && python3 - <<'EOF'
cd ~/ai-tree && python3 - <<'EOF' from pathlib import Path p = Path("tools/build_timeline.py"); s = p.read_text() hints = { "AI 能实时语音聊天，随时打断": "AI 能实时语音聊天，|随时打断", "两天两个诺贝尔奖，都颁给了 AI 研究": "两天两个诺贝尔奖，|都颁给了 AI 研究", "一句话，手机替你下单 2000 杯饮品": "一句话，手机替你|下单 2000 杯饮品", "DeepSeek-V3：花小钱练出顶尖模型": "DeepSeek-V3：|花小钱练出顶尖模型", "“星际之门”：5000 亿美元建数据中心": "“星际之门”：|5000 亿美元建数据中心", "英伟达一天蒸发近 6000 亿美元": "英伟达一天蒸发|近 6000 亿美元", "说句话，AI 就替你写好程序": "说句话，|AI 就替你写好程序", "全网照片变动画风，“GPU 要化了”": "全网照片变动画风，|“GPU 要化了”", "一句话变出能走进去的世界": "一句话变出|能走进去的世界", "英伟达：史上首家 5 万亿美元公司": "英伟达：史上首家|5 万亿美元公司", "13 天里，5 个顶级模型接连发布": "13 天里，|5 个顶级模型接连发布", "《时代》年度人物：AI 的缔造者们": "《时代》年度人物：|AI 的缔造者们", "“养龙虾”：开源 AI 管家爆火": "“养龙虾”：|开源 AI 管家爆火", "AI 能干白领的活，软件股大跌": "AI 能干白领的活，|软件股大跌", "春节 AI 红包大战，超 45 亿元": "春节 AI 红包大战，|超 45 亿元", "两行字生成打斗大片，好莱坞抗议": "两行字生成打斗大片，|好莱坞抗议", "春晚机器人打醉拳、翻跟头": "春晚机器人|打醉拳、翻跟头", "OpenAI 一轮融资 1220 亿美元": "OpenAI 一轮融资|1220 亿美元", "DeepSeek V4 开源，价格只要零头": "DeepSeek V4 开源，|价格只要零头", "美国一度要求最强模型出口许可": "美国一度要求|最强模型出口许可", "Kimi K3 公开权重，最大的开源模型": "Kimi K3 公开权重，|最大的开源模型", "宇树上市，首日涨 4.6 倍": "宇树上市，|首日涨 4.6 倍", "Claude Opus 5.5：AI 写代码画出整支 MV": "Claude …（中间省略 737 字）… " from PIL import Image fs=['t0036.00.png','t0047.00.png','t0069.50.png','t0244.00.png','t0251.00.png','t0256.00.png'] ims=[Image.open(f) for f in fs]; W,H=960,540; sh=Image.new('RGB',(W*2,H*3),'white') for i,im in enumerate(ims): sh.paste(im.resize((W,H)),((i%2)*W,(i//2)*H)) sh.save('v2_misc.png')"ok total 258.3s | beats 12 | captions 77 | cards 12 | polaroids 16 | notes 40 errors: none
读取 v2_misc.png
{ "file_path": "~/ai-tree/out/frames/v2_misc.png" }[图片]
思考：排查标签显示问题与元素重叠情况。 调整词汇表位置以避免与分支标签重叠。 重新设计分支标签与标签栈的布局顺序。
排查标签显示问题与元素重叠情况。
调整词汇表位置以避免与分支标签重叠。
重新设计分支标签与标签栈的布局顺序。运行 cd ~/ai-tree && python3 - <<'EOF'
cd ~/ai-tree && python3 - <<'EOF' from pathlib import Path p = Path("scene/film.js"); s = p.read_text() def rep(a, b): global s assert s.count(a) == 1, a[:80] s = s.replace(a, b) rep(""" if (na > 0.01) labels.push({ s: b.name, x: tipPos[0] + b.side * 44, y: tipPos[1] - 4, align: b.side < 0 ? 'right' : 'left', size: 32, weight: 700, color: wk > 0.5 ? C.greyDark : C.ink, alpha: na, yearBold: false, name: true });""", """ const above = b.side < 0 && !b.below; // left-hand branches carry their name above the tip, clear of the left panel if (na > 0.01) labels.push({ s: b.name, x: above ? tipPos[0] - 6 : tipPos[0] + b.side * 44, y: above ? tipPos[1] - 80 : tipPos[1] - 4, align: above ? 'center' : (b.side < 0 ? 'right' : 'left'), size: 32, weight: 700, color: wk > 0.5 ? C.greyDark : C.ink, alpha: na, yearBold: false, name: true });""") rep(""" [[430, 1026, '导航'], [620, 1056, '垃圾邮件过滤'], [1380, 1056, '购物推荐'], [1570, 1026, '规则引擎']].forEach(([x, y, s], i) => {""", """ [[330, 1030, '导航'], [500, 1062, '垃圾邮件过滤'], [1500, 1062, '购物推荐'], [1670, 1030, '规则引擎']].forEach(([x, y, s], i) => {""") p.write_text(s) print("ok") EOF python3 tools/frames.py 66 92 160 2>&1 | tail -1; cd out/frames && python3 -c " from PIL import Image fs=['t0066.00.png','t0092.00.png','t0160.00.png'] ims=[Image.open(f) for f in fs]; W,H=960,540; sh=Image.new('RGB',(W*2,H*2),'white') for i,im in enumerate(ims): sh.paste(im.resize((W,H)),((i%2)*W,(i//2)*H)) sh.save('v2_labels.png')"ok errors: none
还有看一下我朋友的反馈，看一下是否能顺便优化？
（1 张附图未收录）
- Claude 问你
你朋友说得对，v1 的字在手机上确实太小。v2 的字号我无论如何都会按手机标准放大。还剩一个问题：同事主要怎么在手机上看？
- 横屏 + 竖屏两版（推荐）
- 只做横屏放大版
- 只做竖屏版
横屏 + 竖屏两版（推荐）
干了 3 分钟，2 步
读 2 个文件
思考：Designing landscape and vertical layouts for phone-scale typ…
Designing landscape and vertical layouts for phone-scale typography.
Sizing text and trimming clutter for phone-scale screens.
Planning a layout system to adapt coordinates per screen size.
Defining per-layout camera math and phone-scale font sizes.
Checking card placements and label overlaps against crowded screen zones.
Recalculating label widths and positions for the pulled-back landscape view.
Setting font sizes and visibility rules for tree node tags.
Reworking portrait layout to fix label overflow and collisions.
Juggling counter cards, feed notes, and polaroids for crowded lower zones.
Balancing narrow term cards against wide polaroids in portrait zones.
Restructuring the camera and zoom math across layouts.
Finalizing font sizes and planning a full layout rewrite.
Wrapping caption lengths and placing top-band elements without overlap.
Positioning day-card headings and bars across landscape and portrait layouts.
Spacing stacked timeline markers and sizing portrait ending cards.
Preparing to rewrite the layout file with full coordinate math.
Writing coordinate tables for portrait camera, cards, and sun placement.
Resolving label and tag placement collisions in crowded tree clusters.
Converting label anchors to screen-space offsets for tag and name placement.
Mapping day-card headings, bars, and labels for each layout.思考：Building landscape and portrait layouts into one animation
Building landscape and portrait layouts into one animation
读取 lib.js
{ "limit": 12, "file_path": "~/ai-tree/scene/lib.js" }1 // Shared drawing helpers for the hand-drawn film. 2 const W = 1920, H = 1080; 3 const cv = document.getElementById('c'); 4 const ctx = cv.getContext('2d'); 5 const rc = rough.canvas(cv); 6 const HAND = '"LXGW WenKai"', BRUSH = '"Ma Shan Zheng"'; 7 8 const C = { 9 paper: '#f3ead6', ink: '#3a2e27', inkSoft: '#7a6858', red: '#c8432f', 10 bark: '#b8875f', barkDark: '#7a4e32', grey: '#a39d93', greyDark: '#7d776e', 11 nn: '#7f9e5a', nnDark: '#4f6e34', llm: '#d9a441', llmDark: '#9a6a1f', 12 sun: '#f2b84b', winter: '#8fb8d6', snow: '#ffffff', ice: '#7fb3d9',读取 v2_labels.png
{ "file_path": "~/ai-tree/out/frames/v2_labels.png" }[图片]
你先继续你自查的 review 以及修正吧。你要尽量从视频科普的专业人士的角度来审视这个视频
干了 2 分钟，2 步
运行 1 条命令，写 1 个文件
思考：以专业科普视频审核标准复查并修正视频内容。 借鉴专业科普视频制作标准审视叙事节奏与认知负荷。 检查节奏控制与数据准确性以…
以专业科普视频审核标准复查并修正视频内容。
借鉴专业科普视频制作标准审视叙事节奏与认知负荷。
检查节奏控制与数据准确性以优化观看体验。
为分类配色、音效设计与无障碍细节制定方案。
简化专业术语标签为通俗易懂的说明文字。
逐条核实关键数据点与时间细节的准确性。
为手机竖屏布局与分类标签系统制定实现方案。
为横屏布局细化具体坐标参数设计。
为竖屏布局细化坐标与元素间距安排。
核对标题区与结尾卡片的文字排版方案。
计算标签偏移量以避免画面元素重叠。
为树干换路的关键转折点设计标注与回调动画。
调整树干材质过渡的标注位置与触发时机。
设计提示标签样式与关键节点的触发文案。
为年份标记与元素分类配色设计视觉方案。思考：Compiling a full critique of the v2 video draft
Compiling a full critique of the v2 video draft
修改 lib.js
{ "file_path": "~/ai-tree/scene/lib.js", "new_string": "const PORTRAIT = new URLSearchParams(location.search).get('v') === 'portrait';\nconst W = PORTRAIT ? 1080 : 1920, H = PORTRAIT ? 1920 : 1080;\nconst cv = document.getElementById('c');\ncv.width = W; cv.height = H;", "old_string": "const W = 1920, H = 1080;\nconst cv = document.getElementById('c');", "replace_all": false }The file ~/ai-tree/scene/lib.js has been updated successfully.
运行 cd ~/ai-tree && python3 - <<'EOF'
cd ~/ai-tree && python3 - <<'EOF' from pathlib import Path p = Path("tools/build_timeline.py"); s = p.read_text() def rep(a, b): global s assert s.count(a) == 1, a[:80] s = s.replace(a, b) rep(''' "wither": round(t_of_y(1987.6), 3), }''', ''' "wither": round(t_of_y(1987.6), 3), "method1": bw("b4", "后来", -0.1), "method2": bw("b5", "神经", -0.1), "method3": bw("b6", "人们", -0.1), "year2024": round(B["b8"]["ts"] - 0.15, 3), "year2025": round(B["b9"]["ts"] - 0.15, 3), "year2026": round(B["b10"]["ts"] - 0.15, 3), }''') p.write_text(s) print("ok") EOF python3 tools/build_timeline.py | head -1 && python3 -c " import json s=open('~/ai-tree/scene/timeline.js').read(); d=json.loads(s[len('window.TL = '):].rstrip().rstrip(';')) print({k: d['marks'][k] for k in ['method1','method2','method3','year2024','year2025','year2026']})"ok total 258.3s | beats 12 | captions 77 | cards 12 | polaroids 16 | notes 40 Traceback (most recent call last): File "~/ai-tree/tools/build_timeline.py", line 338, in <module> print(f" {b['id']:6s} {b['t0']:7.2f} → {b['t1']:7.2f}") ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ BrokenPipeError: [Errno 32] Broken pipe {'method1': 56.09, 'method2': 78.098, 'method3': 96.722, 'year2024': 124.706, 'year2025': 149.738, 'year2026': 184.49}
全世界都疯了，这个用词也不好
干了不到 1 分钟，3 步
运行 2 条命令，其他操作 1 次
思考：检查时间标签用词是否恰当。 寻找更贴切的替代说法。
检查时间标签用词是否恰当。
寻找更贴切的替代说法。思考：Revising era label and opening line wording
Revising era label and opening line wording
运行 cd ~/Movies/ai-tree-video/voice && cp s00.mp3 v1/s00.mp3 && python3 - <<'EOF'
cd ~/Movies/ai-tree-video/voice && cp s00.mp3 v1/s00.mp3 && python3 - <<'EOF' src = open("tts.py").read() a = '("s00", "这是一棵树的故事。它长了七十多年，最近这三年，长疯了。"),' assert a in src src = src.replace(a, '("s00", "这是一棵树的故事。它长了七十多年，最近这三年，突然爆发了。"),') open("tts.py", "w").write(src) EOF ../.venv/bin/python tts.py && ffprobe -v error -show_entries format=duration -of csv=p=0 s00.mp3Process started with PID 8163 (shell: /bin/zsh) Initial output: s00 18 boundaries 6.168000 ✅ Process 8163 has finished execution
准备从你电脑传回文件
{ "paths": [ "~/Movies/ai-tree-video/voice/s00.mp3", "~/Movies/ai-tree-video/voice/timing.json" ] }{"staged":[{"devicePath":"~/Movies/ai-tree-video/voice/s00.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s00.mp3","mtimeMs":1792031581450,"bytes":37008,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/timing.json","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/timing.json","mtimeMs":1792031581455,"bytes":39567,"ok":true,"dispatched":true}]}运行 cp /mnt/user-data/uploads/ai-tree-video/voice/s00.mp3 /mnt/user-data/uploads/ai…
cp /mnt/user-data/uploads/ai-tree-video/voice/s00.mp3 /mnt/user-data/uploads/ai-tree-video/voice/timing.json audio/voice/ && python3 -c " import json; d=json.load(open('audio/voice/timing.json')); print(' '.join(f\"{w['w']}@{w['t']}\" for w in d['s00']['words']))" && python3 - <<'EOF' from pathlib import Path p = Path("tools/build_timeline.py"); s = p.read_text() a = '''"ff1": bw("intro", "长疯", -0.35)''' assert s.count(a) == 1 s = s.replace(a, '''"ff1": bw("intro", "突然", -0.2)''') p.write_text(s) EOF python3 tools/build_timeline.py > /dev/null && python3 -c " import json s=open('~/ai-tree/scene/timeline.js').read(); d=json.loads(s[len('window.TL = '):].rstrip().rstrip(';')) print({k: d['marks'][k] for k in ['ff0','ff1','rw0','rw1','intro_end']}, d['captions'][:2])"这是@0.098 一@0.371 棵@0.479 树@0.598 的@0.788 故事@0.871 它@1.833 长@1.988 了@2.119 七十多@2.214 年@2.631 最近@3.298 这@3.607 三@3.702 年@3.917 突然@4.417 爆发@4.81 了@5.19 {'ff0': 2.183, 'ff1': 5.017, 'rw0': 7.118, 'rw1': 8.318, 'intro_end': 11.368} [{'text': '这是一棵树的故事', 't0': 0.898, 't1': 2.338}, {'text': '它长了七十多年，最近这三年，突然爆发了', 't0': 2.633, 't1': 6.383}]思考：Rebuilding the film module with a full layout system. 为树上的时间…
Rebuilding the film module with a full layout system.
为树上的时间点标注年份与事件。
推敲历史事件对应的真实年代与出处。
确定标签位置与偏移量的具体规则。
设计日期面板与速率图标的布局规则。
设定卡片与宝丽来照片的尺寸与排版细节。
计算信息流卡片与图标的排布间距。
Now the rewrite. One animation file will support both layouts (landscape / portrait) and include all the review fixes.
干了 41 分钟，113 步
读 58 个文件，查找 1 次，运行 22 条命令，写 30 个文件，其他操作 2 次
写入 film.js
// AI 这棵树 — v2. Landscape 1920×1080 by default, portrait 1080×1920 with ?v=portrait. // The film is a pure function of time: window.seek(t) draws the frame at t seconds. // Scene elements run on "scene time" (differs from real time only during the opening teaser); // overlays (captions, title, term cards, photos, the one-day card) run on real time. (() => { const TL = window.TL, M = TL.marks; const GROUND = 950; // ================================================================ time ↔ calendar const YK = TL.ykeys; function yearAt(t) { if (t <= YK[0][0]) return YK[0][1]; for (let i = 1; i < YK.length; i++) { const [ta, ya] = YK[i - 1], [tb, yb] = YK[i]; if (t <= tb) return tb > ta ? ya + (yb - ya) * (t - ta) / (tb - ta) : yb; } return YK[YK.length - 1][1]; } const tCache = new Map(); function timeOf(y) { if (tCache.has(y)) return tCache.get(y); let r = Infinity; if (y <= YK[0][1]) r = YK[0][0]; else for (let i = 1; i < YK.length; i++) { const [ta, ya] = YK[i - 1], [tb, yb] = YK[i]; if (y <= yb && yb > ya) { r = ta + (y - ya) / (yb - ya) * (tb - ta); break; } } tCache.set( …（中间省略 69248 字）… 这棵树0123456789')]); await loadPhotos(['turing', 'perceptron', 'shakey', 'deepblue', 'stanley']); buildPaper(); window.seek = seek; window.TOTAL = TL.total; window.__ready = true; window.timeSeek = (t) => { const a = performance.now(); seek(t); return performance.now() - a; }; } init(); })();<tool_use_error>File has not been read yet. Read it first before writing to it.</tool_use_error>
读取 film.js
{ "file_path": "~/ai-tree/scene/film.js", "limit": 5 }1 // AI 这棵树 — v2. The whole film is a pure function of time: window.seek(t) draws the frame at t seconds. 2 // Scene elements run on "scene time" T (which differs from real time only during the teaser at the start); 3 // overlays (captions, title, term cards, photos, the one-day card) run on real time t. 4 (() => { 5 const TL = window.TL, M = TL.marks;写入 film.js
// AI 这棵树 — v2. Landscape 1920×1080 by default, portrait 1080×1920 with ?v=portrait. // The film is a pure function of time: window.seek(t) draws the frame at t seconds. // Scene elements run on "scene time" (differs from real time only during the opening teaser); // overlays (captions, title, term cards, photos, the one-day card) run on real time. (() => { const TL = window.TL, M = TL.marks; const GROUND = 950; // ================================================================ time ↔ calendar const YK = TL.ykeys; function yearAt(t) { if (t <= YK[0][0]) return YK[0][1]; for (let i = 1; i < YK.length; i++) { const [ta, ya] = YK[i - 1], [tb, yb] = YK[i]; if (t <= tb) return tb > ta ? ya + (yb - ya) * (t - ta) / (tb - ta) : yb; } return YK[YK.length - 1][1]; } const tCache = new Map(); function timeOf(y) { if (tCache.has(y)) return tCache.get(y); let r = Infinity; if (y <= YK[0][1]) r = YK[0][0]; else for (let i = 1; i < YK.length; i++) { const [ta, ya] = YK[i - 1], [tb, yb] = YK[i]; if (y <= yb && yb > ya) { r = ta + (y - ya) / (yb - ya) * (tb - ta); break; } } tCache.set( …（中间省略 68973 字）… 这棵树0123456789')]); await loadPhotos(['turing', 'perceptron', 'shakey', 'deepblue', 'stanley']); buildPaper(); window.seek = seek; window.TOTAL = TL.total; window.__ready = true; window.timeSeek = (t) => { const a = performance.now(); seek(t); return performance.now() - a; }; } init(); })();The file ~/ai-tree/scene/film.js has been updated successfully. (file state is current in your context — no need to Read it back)
查找工具
{ "max_results": 3, "query": "select:SendUserMessage,TaskList,TaskGet" }运行 cat tools/frames.py && echo ---- && cat tools/render.py && echo ---- && ls -la …
cat tools/frames.py && echo ---- && cat tools/render.py && echo ---- && ls -la scene tools audio out && git log --oneline | head
"""Render stills of the film at given times: python3 tools/frames.py 12.5 80 150 ...""" import sys, time from pathlib import Path from playwright.sync_api import sync_playwright ROOT = Path(__file__).resolve().parent.parent out = ROOT / "out" / "frames"; out.mkdir(parents=True, exist_ok=True) with sync_playwright() as p: b = p.chromium.launch() page = b.new_page(viewport={"width": 1920, "height": 1080}) errs = [] page.on("pageerror", lambda e: errs.append(str(e))) page.on("console", lambda m: m.type == "error" and errs.append(m.text)) page.goto((ROOT / "scene" / "film.html").as_uri()) page.wait_for_function("window.__ready === true", timeout=60000) for ts in sys.argv[1:]: t0 = time.time() ms = page.evaluate(f"timeSeek({float(ts)})") page.screenshot(path=str(out / f"t{float(ts):07.2f}.png")) print(f"t={ts}: draw {ms:.0f} ms, total {1000*(time.time()-t0):.0f} ms") b.close() print("errors:", errs or "none") ---- """Render the film: frames from the HTML scene (canvas -> JPEG), encoded in parallel chunks, then muxed with audio. usage: python3 tools/render.py [--fps 24] [--workers 2] [--start 0] [--end TOTAL] [--audio audio/mix.wav] [--out out/film.mp4] """ import argparse, base64, json, subprocess, sys, time from multiprocessing import Process from pathlib import Path ROOT = Path(__file__).resolve().parent.parent def total_duration(): s = (ROOT / "scene" / "timeline.js").read_text() return json.loads(s[ …（中间省略 4602 字）… root root 18004 Oct 15 02:33 build_timeline.py -rw-r--r-- 1 root root 986 Oct 14 09:17 frames.py -rw-r--r-- 1 root root 6656 Oct 14 09:25 music.py -rw-r--r-- 1 root root 3669 Oct 14 09:23 render.py -rw-r--r-- 1 root root 745 Oct 14 06:53 still.py 7e5a868 v1.1: cap young branch length; fix two note dates 52f7100 v1: first full preview (hand-drawn tree, 4:10, narration + procedural music)查看待办
{}#1 [completed] 核实 2024–2026 的 AI 大事件 #2 [completed] 重排结构：前半段 1950–2023，后半段 2024–2026 越走越慢 #3 [completed] 写大白话旁白脚本，给你过目 #4 [completed] 在你电脑上合成旁白配音 #5 [completed] 做动画：树的生长、寒冬、拍立得、时间变慢 #6 [completed] 配乐、合成成片 #8 [completed] 保存 v1（源码 + 视频），后续改动都在 v2 上做 #9 [completed] 下载 5 张历史照片并核对授权 #10 [completed] 调研同类视频的设计手法 #11 [completed] 自查 v1，列出问题清单 #12 [in_progress] 制作 v2：照片、开头钩子、时间压缩转场、第二部分重排、大时刻、音效 #13 [pending] 渲染 v2 并检查（事实、画面、音画同步），交付到你电脑
运行 cat > tools/frames.py <<'EOF'
cat > tools/frames.py <<'EOF' """Render stills of the film at given times. usage: python3 tools/frames.py [--portrait] 12.5 80 150 ... Writes out/frames/tXXXX.XX.png (landscape) or out/frames/pXXXX.XX.png (portrait). """ import sys, time from pathlib import Path from playwright.sync_api import sync_playwright ROOT = Path(__file__).resolve().parent.parent args = sys.argv[1:] portrait = "--portrait" in args times = [a for a in args if not a.startswith("--")] W, H = (1080, 1920) if portrait else (1920, 1080) out = ROOT / "out" / "frames"; out.mkdir(parents=True, exist_ok=True) with sync_playwright() as p: b = p.chromium.launch() page = b.new_page(viewport={"width": W, "height": H}) errs = [] page.on("pageerror", lambda e: errs.append(str(e))) page.on("console", lambda m: m.type == "error" and errs.append(m.text)) page.goto((ROOT / "scene" / "film.html").as_uri() + ("?v=portrait" if portrait else "")) page.wait_for_function("window.__ready === true", timeout=60000) for ts in times: t0 = time.time() ms = page.evaluate(f"timeSeek({float(ts)})") page.screensho …（中间省略 1339 字）… arts[k], a.scale))', 'args=(k, chunks[k], a.fps, parts[k], a.scale, a.portrait))') s = s.replace('tmp = ROOT / "out" / "parts"; tmp.mkdir', 'tmp = ROOT / "out" / ("parts-p" if a.portrait else "parts"); tmp.mkdir') p.write_text(s) EOF grep -n "portrait\|W, H" tools/render.py; nproc; free -g | head -22:usage: python3 tools/render.py [--portrait] [--fps 24] [--workers 2] [--start 0] [--end TOTAL] [--audio audio/mix.wav] [--out out/film.mp4] 16:def worker(k, frames, fps, part, scale, portrait): 18: W, H = (1080, 1920) if portrait else (1920, 1080) 26: page.goto((ROOT / "scene" / "film.html").as_uri() + ("?v=portrait" if portrait else "")) 40: ap.add_argument("--portrait", action="store_true") 53: tmp = ROOT / "out" / ("parts-p" if a.portrait else "parts"); tmp.mkdir(parents=True, exist_ok=True) 56: procs = [Process(target=worker, args=(k, chunks[k], a.fps, parts[k], a.scale, a.portrait)) for k in range(a.workers)] 2 total used free shared buff/cache available Mem: 7 0 7 0 0 7运行 python3 - <<'EOF'
python3 - <<'EOF' import json s = open('scene/timeline.js').read() TL = json.loads(s[len("window.TL = "):].rstrip().rstrip(";")) print("total", TL["total"]) for b in TL["beats"]: print(b["id"], round(b["t0"],2), round(b["ts"],2), round(b["te"],2), round(b["t1"],2)) print({k: round(v,2) for k,v in TL["marks"].items()}) print("cards", [(c["id"], round(c["t"],2)) for c in TL["cards"]]) print("polas", [(p["id"], round(p["t"],2), round(p["dur"],2)) for p in TL["polaroids"]]) print("notes", len(TL["notes"]), [(round(n["t"],1)) for n in TL["notes"]][:50]) EOFtotal 258.52 intro 0.0 0.8 6.97 11.37 b1 11.37 11.57 22.03 22.93 b2 22.93 23.13 33.09 34.09 b3 34.09 34.29 53.96 56.16 b4 56.16 56.36 69.68 72.88 b5 72.88 73.08 95.79 96.79 b6 96.79 96.99 110.12 111.52 b7 111.52 112.12 123.42 124.92 b8 124.92 125.12 148.95 149.95 b9 149.95 150.15 183.7 184.7 b10 184.7 184.9 227.07 231.27 end 231.27 232.87 251.52 258.52 {'ff0': 2.18, 'ff1': 5.02, 'rw0': 7.12, 'rw1': 8.32, 'intro_end': 11.37, 'seed_T': 11.42, 'crazy_T0': 213.43, 'crazy_T1': 221.39, 'day_bar': 113.03, 'sliver': 115.84, 'half': 118.87, 'weekly': 121.06, 'day_out': 124.22, 'bloom': 73.14, 'chatgpt': 103.14, 'winter1': 41.05, 'spring1': 44.4, 'winter2': 51.7, 'spring2': 55.87, 'wither': 51.52, 'method1': 56.35, 'method2': 78.36, 'method3': 96.99, 'year2024': 124.97, 'year2025': 150.0, 'year2026': 184.75} cards [('ai', 20.23), ('nn', 26.73), ('expert', 45.22), ('backprop', 56.3), ('ml', 62.05), ('dl', 83.32), ('rl', 87.83), ('transformer', 91.2), ('llm', 100.27), ('reason', 138.65), ('agent', 167.54), ('embodied', 210.82)] polas [('photo:turing', 12.76, 4.2), ('photo:perceptron', 25.58, 4.4), ('photo:shakey', 34.29, 4.0), ('photo:deepblue', 66.1, 4.2), ('photo:stanley', 69.83, 3.0), ('ill:go', 85.76, 4.6), ('ill:chat', 102.42, 4.0), ('ill:projector', 126.26, 3.0), ('ill:shovel', 128.84, 3.6), ('ill:nobel', 141.58, 4.4), ('ill:boat', 151.71, 3.2), ('ill:gala1', 159.78, 3.2), ('ill:melt', 168.61, 3.4), ('ill:lobster', 189.27, 3.6), ('ill:gala2', 201.48, 3.6), ('ill:animator', 219.72, 4.4)] notes 40 [127.3, 129.1, 130.0, 132.7, 133.3, 137.2, 140.5, 147.7, 148.0, 149.8, 152.2, 153.1, 157.0, 159.9, 167.8, 170.0, 171.2, 172.9, 174.3, 174.9, 175.4, 176.0, 176.6, 181.9, 182.8, 183.6, 192.5, 193.5, 195.2, 195.7, 197.6, 201.6, 208.3, 211.0, 212.3, 213.6, 214.7, 216.3, 218.2, 226.0]思考：Rendering test stills across key video moments.
Rendering test stills across key video moments.
运行 rm -f out/frames/*.png 2>/dev/null; T="3.5 6.0 7.7 9.6 14.0 21.5 28.0 31.5 36.0…
rm -f out/frames/*.png 2>/dev/null; T="3.5 6.0 7.7 9.6 14.0 21.5 28.0 31.5 36.0 42.5 47.0 53.0 57.5 63.0 67.5 71.0 74.5 79.5 84.5 88.0 92.0 98.5 101.5 104.0 108.0 114.5 117.0 120.0 122.8 125.4 131.0 139.5 145.0 150.5 160.5 168.5 176.8 185.2 196.0 212.0 222.0 229.0 238.0 246.0 253.0 257.5"; (timeout 600 python3 tools/frames.py $T > out/frames-land.log 2>&1 &); timeout 600 python3 tools/frames.py --portrait $T > out/frames-port.log 2>&1; sleep 2; tail -3 out/frames-land.log out/frames-port.log; ls out/frames | wc -l
tail: option used in invalid context -- 3 92
运行 for f in out/frames-land.log out/frames-port.log; do echo "== $f"; tail -n 4 $f…
for f in out/frames-land.log out/frames-port.log; do echo "== $f"; tail -n 4 $f; done; grep -h "draw" out/frames-land.log | awk '{print $3}' | sort -n | tail -n 3== out/frames-land.log t=246.0: draw 22 ms, total 723 ms t=253.0: draw 23 ms, total 520 ms t=257.5: draw 20 ms, total 646 ms errors: none == out/frames-port.log t=246.0: draw 20 ms, total 843 ms t=253.0: draw 22 ms, total 474 ms t=257.5: draw 16 ms, total 495 ms errors: none 35 46 72
思考：Organizing landscape frames into reduced-size contact sheets…
Organizing landscape frames into reduced-size contact sheets.
运行 mkdir -p out/sheets && python3 - <<'EOF'
mkdir -p out/sheets && python3 - <<'EOF' from PIL import Image, ImageDraw, ImageFont from pathlib import Path fr = Path('out/frames'); sh = Path('out/sheets') for f in sh.glob('*.png'): f.unlink() def sheet(prefix, cols, tw, th, per, name): files = sorted(fr.glob(prefix + '*.png')) for k in range(0, len(files), per): grp = files[k:k+per]; rows = (len(grp) + cols - 1) // cols S = Image.new('RGB', (cols * tw, rows * (th + 28)), 'white'); d = ImageDraw.Draw(S) for i, f in enumerate(grp): im = Image.open(f).convert('RGB').resize((tw, th), Image.LANCZOS) x, y = (i % cols) * tw, (i // cols) * (th + 28) S.paste(im, (x, y + 28)); d.text((x + 6, y + 6), f.stem, fill='red') S.save(sh / f'{name}{k // per:02d}.png') sheet('t', 2, 960, 540, 4, 'L') sheet('p', 3, 600, 1067, 3, 'P') print(sorted(p.name for p in sh.glob('*.png'))) EOF['L00.png', 'L01.png', 'L02.png', 'L03.png', 'L04.png', 'L05.png', 'L06.png', 'L07.png', 'L08.png', 'L09.png', 'L10.png', 'L11.png', 'P00.png', 'P01.png', 'P02.png', 'P03.png', 'P04.png', 'P05.png', 'P06.png', 'P07.png', 'P08.png', 'P09.png', 'P10.png', 'P11.png', 'P12.png', 'P13.png', 'P14.png', 'P15.png']
读取 L00.png
{ "file_path": "~/ai-tree/out/sheets/L00.png" }[图片]
思考：Reviewing landscape teaser issues for a fast-forward timing …
Reviewing landscape teaser issues for a fast-forward timing glitch.
Weighing how long event tags should linger before fading.
Checking crown label overlaps in the 2026 fast-forward view.
Redesigning crown label positions to avoid overlap outside the ring.
Untangling crowded tip labels colliding near the crown.
Repositioning the science branch tip to dodge the crown labels.
Hand-tuning crown label angles to avoid overlapping names.
Nudging branch tip labels apart to clear new positions.读取 L01.png
{ "file_path": "~/ai-tree/out/sheets/L01.png" }[图片]
思考：核对图灵照片与年份说明是否一致。
核对图灵照片与年份说明是否一致。
读取 L02.png
{ "file_path": "~/ai-tree/out/sheets/L02.png" }[图片]
思考：Fixing a photo caption that overflows its frame. Spotting a …
Fixing a photo caption that overflows its frame.
Spotting a timing bug in when career tags appear.
Adjusting branch growth rates so milestones land on time.
Tuning growth speed so each career tag lands correctly.读取 L03.png
{ "file_path": "~/ai-tree/out/sheets/L03.png" }[图片]
思考：Untangling overlapping timeline labels in a life chart. Fixi…
Untangling overlapping timeline labels in a life chart.
Fixing truncated trunk rendering and stale tag lifetimes.
Adding halo backgrounds to fix text readability over branches.读取 L04.png
{ "file_path": "~/ai-tree/out/sheets/L04.png" }[图片]
思考：Checking a timeline of key milestones and related tags. Chec…
Checking a timeline of key milestones and related tags.
Checking card placement against overlapping branch labels.
Searching for empty space to relocate the overlapping card.
Weighing alternate corners to tuck the overlapping card.
Checking other tag positions for similar overlap conflicts.读取 L05.png
{ "file_path": "~/ai-tree/out/sheets/L05.png" }[图片]
思考：Checking whether the tree graphic gets cut off at the top. C…
Checking whether the tree graphic gets cut off at the top.
Checking label and callout overlaps across timeline frames.读取 L06.png
{ "file_path": "~/ai-tree/out/sheets/L06.png" }[图片]
思考：Reviewing a day-view card layout for cropped time labels. Re…
Reviewing a day-view card layout for cropped time labels.
Resizing fonts and spacing to fix cramped labels and clipping.
Checking overlapping tags near the timeline ruler labels.读取 L07.png
{ "file_path": "~/ai-tree/out/sheets/L07.png" }[图片]
思考：Reviewing landscape footage for timing and overlay issues. C…
Reviewing landscape footage for timing and overlay issues.
Checking caption overflow and polaroid placement against tree labels.
Checking crown label spacing for overlapping text near timestamps.读取 L08.png
{ "file_path": "~/ai-tree/out/sheets/L08.png" }[图片]
思考：Checking captions for duplicated text in the images.
Checking captions for duplicated text in the images.
读取 L09.png
{ "file_path": "~/ai-tree/out/sheets/L09.png" }[图片]
思考：Fixing overlapping crown labels in the diagram. Reworking th…
Fixing overlapping crown labels in the diagram.
Reworking the crown's size and label placement for drama.
Arranging crown node labels into balanced rows.
Choosing node positions inside the crown for each label.读取 L10.png
{ "file_path": "~/ai-tree/out/sheets/L10.png" }[图片]
思考：Checking the rate's end-of-sequence consistency. Repositioni…
Checking the rate's end-of-sequence consistency.
Repositioning root labels to avoid overlapping the caption box.读取 L11.png
{ "file_path": "~/ai-tree/out/sheets/L11.png" }[图片]
思考：Checking the end card timing and credit sizing.
Checking the end card timing and credit sizing.
读取 P00.png
{ "file_path": "~/ai-tree/out/sheets/P00.png" }[图片]
思考：Reviewing the portrait layout design for the UI.
Reviewing the portrait layout design for the UI.
读取 P01.png
{ "file_path": "~/ai-tree/out/sheets/P01.png" }[图片]
思考：Debugging screen scaling for a portrait-mode game animation.…
Debugging screen scaling for a portrait-mode game animation.
Rethinking polaroid and card placement in portrait layout.读取 P02.png
{ "file_path": "~/ai-tree/out/sheets/P02.png" }[图片]
读取 P03.png
{ "file_path": "~/ai-tree/out/sheets/P03.png" }[图片]
思考：正在修复肖像图中文字被裁切的问题。 正在调整肖像模式下树木缩放与名字位置的方案。
正在修复肖像图中文字被裁切的问题。
正在调整肖像模式下树木缩放与名字位置的方案。读取 P04.png
{ "file_path": "~/ai-tree/out/sheets/P04.png" }[图片]
读取 P05.png
{ "file_path": "~/ai-tree/out/sheets/P05.png" }[图片]
思考：Diagnosing layout gaps in a crowded portrait tree design. Ca…
Diagnosing layout gaps in a crowded portrait tree design.
Calculating zoom levels to fit the tree in portrait mode.
Weighing where to place photos and ground for better composition.读取 P07.png
{ "file_path": "~/ai-tree/out/sheets/P07.png" }[图片]
读取 P09.png
{ "file_path": "~/ai-tree/out/sheets/P09.png" }[图片]
读取 P10.png
{ "file_path": "~/ai-tree/out/sheets/P10.png" }[图片]
思考：Analyzing layout details across portrait image variations. D…
Analyzing layout details across portrait image variations.
Drafting fixes for labels, crowns, captions, and overlays.
Positioning cards and polaroids within portrait layouts.
Checking tag placement against overlapping tree branches.查找 text\(
{ "output_mode": "content", "path": "~/ai-tree/scene/art.js", "pattern": "text\\(" }45: text('?', x, y + 6, { size: 40, weight: 700, align: 'center', yearBold: false }); }, 50: text('如果…', x - 26, y - 12, { size: 17, yearBold: false }); text('就…', x - 18, y + 14, { size: 17, yearBold: false }); 62: text('+1', x + 30, y + 22, { size: 22, weight: 700, color: C.red, align: 'center', yearBold: false }); }, 64: ws.forEach((w, i) => text(w, x - 44 + i * 22, y + 26, { size: 20, align: 'center', yearBold: false })); 98: text('全球第一', 66, -66, { size: 16, weight: 700, color: C.red, align: 'center', yearBold: false }); }, 102: text(i ? '化学' : '物理', mx, my + 6, { size: 17, weight: 700, color: '#6b4f12', align: 'center', yearBold: false }); }); 103: text('2024 · 两天两个诺奖', 0, 72, { size: 15, align: 'center', yearBold: false }); }, 118: text('GPU 要化了', 0, 80, { size: 16, weight: 700, color: C.red, align: 'center', yearBold: false }); },读取 art.js
{ "file_path": "~/ai-tree/scene/art.js", "limit": 50, "offset": 85 }85 rc.circle(0, 0, 34, o(s + 60, { stroke: C.red, strokeWidth: 2.4 })); }, 86 chat: (s) => { sky(s, '#eef3e2'); rc.rectangle(-46, -78, 92, 156, o(s, { fill: '#3a3f44', fillStyle: 'solid' })); ctx.fillStyle = '#fdfcf7'; ctx.fillRect(-38, -66, 76, 128); 87 [[-30, -54, 52, '#d6ecff'], [-18, -26, 48, '#dff3c9'], [-30, 2, 58, '#d6ecff'], [-14, 32, 44, '#dff3c9']].forEach(([x0, y0, w, f], i) => rc.rectangle(x0, y0, w, 20, o(s + i + 1, { fill: f, fillStyle: 'solid', strokeWidth: 1 }))); }, 88 projector: (s) => { ctx.fillStyle = '#2e2a28'; ctx.fillRect(-100, -90, 200, 180); 89 ctx.save(); ctx.globalAlpha = 0.35; ctx.fillStyle = '#fff6c9'; ctx.beginPath(); ctx.moveTo(-60, 40); ctx.lineTo(90, -70); ctx.lineTo(90, 30); ctx.closePath(); ctx.fill(); ctx.restore(); 90 rc.rectangle(20, -72, 72, 66, o(s, { fill: '#f4f0e0', fillStyle: 'solid', stroke: '#c9c0a8' })); 91 rc.ellipse(56, -36, 40, 22, o(s + 1, { fill: '#8a6a4a', fillStyle: 'hachure', hachureGap: 3 })); [[44, -26], [68, -26]].forEach(([lx, ly], i) => rc.line(lx, ly, lx, ly + 10, o(s + 2 + i))); 92 rc.rectangle(-86, 26, 46, 30, o(s + 5, { fill: '#7a8288', fillStyle: 'solid', stroke: '#d0d0d0' })); rc.circle(-72, 18, 18, o(s + 6, { stroke: '#d0d0d0' })); rc.circle(-52, 18, 18, o(s + 7, { stroke: '#d0d0d0' })); 93 rc.rectangle(-96, 62, 40, 16, o(s + 8, { fill: '#ffffff', fillStyle: 'solid', stroke: '#d0d0d0' })); }, 94 shovel: (s) => { sky(s); 95 [[-70, 60, 22, 30], [-40, 30, 24, 60], [-8, 0, 26, …（中间省略 6036 字）… c.rectangle(-90, 30, 180, 12, o(s, { fill: '#b8875f', fillStyle: 'solid' })); 133 rc.rectangle(-78, -26, 30, 24, o(s + 1, { fill: '#cfd8dc', fillStyle: 'solid' })); rc.rectangle(-82, -2, 38, 32, o(s + 2, { fill: '#cfd8dc', fillStyle: 'solid' })); 134 rc.circle(-68, -15, 5, o(s + 3, { fill: C.ink, fillStyle: 'solid' })); rc.circle(-58, -15, 5, o(s + 4, { fill: C.ink, fillStyle: 'solid' }));思考：Adjusting the Nobel illustration's layout and text. Coding t…
Adjusting the Nobel illustration's layout and text.
Coding the halo text rendering and branch growth logic.
Fine-tuning tag fading, label alignment, and withered-branch styling.
Positioning crown cluster labels and checking their overlap timing.
Adjusting portrait-mode card positions and width checks.
Checking label spacing and card overlap in portrait mode.运行 python3 - <<'EOF'
python3 - <<'EOF' import json s = open('scene/timeline.js').read() TL = json.loads(s[len("window.TL = "):].rstrip().rstrip(";")) for c in TL["captions"]: if c["t0"] > 225 or c["t0"] < 12: print(round(c["t0"],2), round(c["t1"],2), c["text"]) EOF0.9 2.34 这是一棵树的故事 2.63 6.38 它长了七十多年，最近这三年，突然爆发了 11.67 15.11 1950年，图灵问：机器能思考吗 225.15 226.48 已经超过十二亿 232.97 236.8 七十多年，这棵树从写规则到会学习 236.81 238.5 从会说话到会干活 238.79 242.57 那些退场的老枝也没有白长，它们变成了导航 242.49 245.24 垃圾邮件过滤这些看不见的地基 245.54 248.22 最后说一句：这支视频的每一帧 248.18 250.94 也是AI写代码画出来的
思考：Locating where the timeline root aligns with a key phrase.
Locating where the timeline root aligns with a key phrase.
运行 grep -n "marks\[\|marks =\|def bw\|\"wither\"\|intro_end\|tempo" tools/build_ti…
grep -n "marks\[\|marks =\|def bw\|\"wither\"\|intro_end\|tempo" tools/build_timeline.py | head -60
267:def bw(beat_id, word, off=0.0): 270:marks = { 272: "rw0": round(intro["te"] + 0.15, 3), "rw1": round(intro["te"] + 1.35, 3), "intro_end": intro["t1"], 278: "wither": round(t_of_y(1987.6), 3), 283:# tempo map: one section per mood; the music generator and the note snapping share it 284:tempo = [ 286: (intro["t1"], marks["winter1"], 84, "calm"), (marks["winter1"], marks["spring1"], 70, "winter"), 287: (marks["spring1"], marks["winter2"], 84, "calm"), (marks["winter2"], marks["spring2"], 70, "winter"), 288: (marks["spring2"], marks["bloom"], 84, "calm"), (marks["bloom"], B["b7"]["t0"], 90, "spring"), 293:tempo = [{"t0": round(a, 3), "t1": round(b, 3), "bpm": bpm, "style": st} for a, b, bpm, st in tempo] 298: sec = next((x for x in tempo if x["t0"] <= tq < x["t1"]), None) 323: "marks": marks, "tempo": tempo}读取 build_timeline.py
{ "file_path": "~/ai-tree/tools/build_timeline.py", "limit": 75, "offset": 255 }255 256 def t_of_y(yv): 257 for (ta, ya), (tb, yb) in zip(mono, mono[1:]): 258 if ya <= yv <= yb and yb > ya: 259 return ta + (yv - ya) / (yb - ya) * (tb - ta) 260 return None 261 262 263 cards = [{"id": cid, "t": round(word_time(bid, w) - 0.15, 3)} for cid, bid, w in CARDS] 264 polaroids = [{"id": pid, "t": round(word_time(bid, w) - 0.1, 3), "dur": dd} for pid, bid, w, dd in POLAROIDS] 265 # ---- named moments the renderer and the sound design both key on 266 B = {b["id"]: b for b in beats} 267 def bw(beat_id, word, off=0.0): 268 return round(word_time(beat_id, word) + off, 3) 269 intro = B["intro"] 270 marks = { 271 "ff0": bw("intro", "它", -0.45), "ff1": bw("intro", "突然", -0.2), 272 "rw0": round(intro["te"] + 0.15, 3), "rw1": round(intro["te"] + 1.35, 3), "intro_end": intro["t1"], 273 "seed_T": round(B["b1"]["t0"] + 0.05, 3), "crazy_T0": round(t_of_y(Y("2026-07-20")), 3), "crazy_T1": round(t_of_y(Y("2026-09-25")), 3), 274 "day_bar": bw("b7", "压缩", -0.35), "sliver": bw("b7", "最后", -0.15), "half": bw("b7", "一半", -0.15), "weekly": bw("b7", "星期", -0.2), 275 "day_out": round(B["b7"]["t1"] - 0.7, 3), 276 "bloom": round(t_of_y(2011.95), 3), "chatgpt": bw("b6", "上线", -0.1), 277 "winter1": round(t_of_y(1974.4), 3), "spring1": round(t_of_y(1980.2), 3), "winter2": round(t_of_y(1987.8), 3), "spring2": round(t_of_y(1993.6), 3), 278 "wither": round(t_of_y(1987.6), 3), 279 "method1": bw("b4", "后来", -0.1), "method2": bw …（中间省略 2185 字）… rs": counters, "segs": segs, 323 "marks": marks, "tempo": tempo} 324 (ROOT / "scene" / "timeline.js").write_text("window.TL = " + json.dumps(tl, ensure_ascii=False, indent=1) + ";\n") 325 326 # ---------------------------------------------------------------- narration track 327 inputs, filters = [], [] 328 for i, s in enumerate(segs): 329 inputs += ["-i", str(VOICE / f"{s['seg']}.mp3")]思考：Assigning weighted scoring marks for key text segments. Rewo…
Assigning weighted scoring marks for key text segments.
Reworking the hour-timeline layout and label positions.
Berechnet die Positionen für das Hochformat-Zeitleisten-Layout.
Prüft Überlappungen der Zeitachsen-Labels und Verbindungslinien.
Berechnet die Kartengrößen und Beschriftungspositionen im Hochformat.
Plant Schriftgrößen für bessere Lesbarkeit auf Mobilgeräten.修改 lib.js
{ "file_path": "~/ai-tree/scene/lib.js", "new_string": "// text with an optional bold leading year (\"1997 深蓝\"); `halo` draws a paper-coloured outline so labels stay legible over branches\nfunction text(str, x, y, { size = 24, weight = 400, color = C.ink, align = 'left', font = HAND, yearBold = true, alpha = 1, measureOnly = false, halo = 0 } = {}) {\n ctx.save(); ctx.fillStyle = color; ctx.textBaseline = 'alphabetic'; ctx.globalAlpha *= alpha;\n const m = yearBold && /^(\\d{4}s?(\\.\\d\\d)*)\\s/.exec(str);\n const parts = m ? [[m[0], 700], [str.slice(m[0].length), weight]] : [[str, weight]];\n const widths = parts.map(([s, w]) => { ctx.font = `${w} ${size}px ${font}`; return ctx.measureText(s).width; });\n const total = widths.reduce((a, b) => a + b, 0);\n const x0 = align === 'left' ? x : align === 'right' ? x - total : x - total / 2;\n if (!measureOnly) {\n ctx.textAlign = 'left';\n if (halo) {\n ctx.save(); ctx.strokeStyle = 'rgba(247,241,226,0.92)'; ctx.lineWidth = halo; ctx.lineJoin = 'round';\n let hx = x0; parts.forEach(([s, w], i) => { ctx.font = `${w} ${size}px ${font}`; …（中间省略 961 字）… 'right' ? x - total : x - total / 2;\n if (!measureOnly) {\n let cx = x0;\n parts.forEach(([s, w], i) => { ctx.font = `${w} ${size}px ${font}`; ctx.textAlign = 'left'; ctx.fillText(s, cx, y); cx += widths[i]; });\n }\n ctx.restore();\n return { x0, w: total };\n}", "replace_all": false }The file ~/ai-tree/scene/lib.js has been updated successfully. (file state is current in your context — no need to Read it back)
修改 art.js
{ "file_path": "~/ai-tree/scene/art.js", "new_string": " text(i ? '化学' : '物理', mx, my + 6, { size: 17, weight: 700, color: '#6b4f12', align: 'center', yearBold: false }); }); },", "old_string": " text(i ? '化学' : '物理', mx, my + 6, { size: 17, weight: 700, color: '#6b4f12', align: 'center', yearBold: false }); });\n text('2024 · 两天两个诺奖', 0, 72, { size: 15, align: 'center', yearBold: false }); },", "replace_all": false }The file ~/ai-tree/scene/art.js has been updated successfully.
修改 build_timeline.py
{ "file_path": "~/ai-tree/tools/build_timeline.py", "new_string": " \"year2024\": round(B[\"b8\"][\"ts\"] - 0.15, 3), \"year2025\": round(B[\"b9\"][\"ts\"] - 0.15, 3), \"year2026\": round(B[\"b10\"][\"ts\"] - 0.15, 3),\n \"roots\": bw(\"end\", \"导航\", -0.4), \"bud\": bw(\"end\", \"这支\", -0.1),\n}", "old_string": " \"year2024\": round(B[\"b8\"][\"ts\"] - 0.15, 3), \"year2025\": round(B[\"b9\"][\"ts\"] - 0.15, 3), \"year2026\": round(B[\"b10\"][\"ts\"] - 0.15, 3),\n}", "replace_all": false }The file ~/ai-tree/tools/build_timeline.py has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " cam: { ax: 1000, ay: 950, sx: 1000, sy: 932, base: 1 }, p2: { z: 0.78, dx: -190, dy: 0 }, fin: { z: 0.9, dx: 0, dy: -40 }, zoomCap: 1.2,\n f: { name: 42, nameP2: 34, tag: 32, small: 28, crown: 36, callout: 36 },", "old_string": " cam: { ax: 1000, ay: 950, sx: 1000, sy: 932, base: 1 }, p2: { z: 0.78, dx: -190, dy: 0 }, fin: { z: 0.9, dx: 0, dy: 0 }, zoomCap: 1.22,\n f: { name: 40, nameP2: 32, tag: 30, small: 26, crown: 34 },", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " card: { w: 580, h: 290, p1: [1300, 22], p2: [450, 600] },\n pola: { cx: 262, bottom: 948 },", "old_string": " card: { w: 580, h: 300, p1: [1300, 30], p2: [450, 600] },\n pola: { cx: 262, bottom: 948 },", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " roots: [[250, 1000, '导航'], [440, 1044, '垃圾邮件过滤'], [1500, 1044, '购物推荐'], [1690, 1000, '规则引擎']],\n end: { card: [300, 190, 1320, 660], lines: [['这支视频的每一帧', 320, 64], ['都是 AI 写代码画出来的', 410, 64]], date: 478, question: 572, qsize: 52,", "old_string": " roots: [[330, 1030, '导航'], [500, 1062, '垃圾邮件过滤'], [1500, 1062, '购物推荐'], [1670, 1030, '规则引擎']],\n end: { card: [300, 200, 1320, 640], lines: [['这支视频的每一帧', 330, 64], ['都是 AI 写代码画出来的', 420, 64]], date: 490, question: 566,", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " day: { card: [90, 50, 1740, 880], head: [['如果把 AI 的七十多年，压缩成一天……', 170, 152]], head2: 60, X0: 330, X1: 1610, Yb: 410, Yv: 740,\n lab: 36, small: 30, lifts: [72, 152, 72, 152], aligns: ['center', 'center', 'center', 'right'], dayLabel: 'left', sliver: 'below-left' },\n};", "old_string": " day: { card: [90, 60, 1740, 860], head: [['如果把 AI 的七十多年，压缩成一天……', 170, 148]], head2: 52, X0: 300, X1: 1620, Yb: 380, Yv: 690,\n lab: 30, small: 26, lifts: [56, 112, 56, 112], dayLabel: 'left', sliver: 'below-left' },\n};", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " cam: { ax: 1000, ay: 950, sx: 540, sy: 1290, base: 0.8 }, p2: { z: 1, dx: 0, dy: 0 }, fin: { z: 1, dx: 0, dy: 0 }, zoomCap: 1.15,\n f: { name: 42, nameP2: 38, tag: 32, small: 28, crown: 36, callout: 36 },", "old_string": " cam: { ax: 1000, ay: 950, sx: 540, sy: 1290, base: 0.72 }, p2: { z: 1, dx: 0, dy: 0 }, fin: { z: 1, dx: 0, dy: 0 }, zoomCap: 1.18,\n f: { name: 40, nameP2: 36, tag: 30, small: 28, crown: 34 },", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " card: { w: 620, h: 300, p1: [430, 1366], p2: [40, 900] },\n pola: { cx: 228, bottom: 1712 }, pola2: { cx: 862, bottom: 1262 },", "old_string": " card: { w: 620, h: 310, p1: [430, 1366], p2: [430, 1366] },\n pola: { cx: 228, bottom: 1712 },", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " end: { card: [50, 420, 980, 1060], lines: [['这支视频的每一帧', 580, 64], ['都是 AI 写代码', 664, 64], ['画出来的', 748, 64]], date: 824, question: 916, qsize: 50,", "old_string": " end: { card: [50, 420, 980, 1060], lines: [['这支视频的每一帧', 580, 64], ['都是 AI 写代码', 664, 64], ['画出来的', 748, 64]], date: 824, question: 910,", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " day: { card: [30, 300, 1020, 1340], head: [['如果把 AI 的七十多年，', 80, 420], ['压缩成一天……', 80, 494]], head2: 56, X0: 110, X1: 970, Yb: 900, Yv: 1460,\n lab: 32, small: 28, lifts: [64, 140, 64, 216], aligns: ['center', 'center', 'right', 'right'], dayLabel: 'below', sliver: 'below-right' },\n};", "old_string": " day: { card: [30, 330, 1020, 1390], head: [['如果把 AI 的七十多年，', 90, 450], ['压缩成一天……', 90, 520]], head2: 52, X0: 120, X1: 980, Yb: 860, Yv: 1340,\n lab: 30, small: 26, lifts: [60, 122, 184, 60], dayLabel: 'below', sliver: 'below-right' },\n};", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " mkBranch({ id: 'expert', name: '当专家', born: 1971, end: [690, 842], c1: [-110, 0], c2: [110, -40], w: [22, 4], wither: 1987.6, below: true, rate: 0.08,", "old_string": " mkBranch({ id: 'expert', name: '当专家', born: 1971, end: [690, 842], c1: [-110, 0], c2: [110, -40], w: [22, 4], wither: 1987.6, below: true,", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " mkBranch({ id: 'vision', name: '看图', born: 1997.8, end: [1290, 360], c1: [60, -70], c2: [-60, 110], w: [24, 5], decor: [[0.6, 0.6, 26]], below: true,", "old_string": " mkBranch({ id: 'vision', name: '看图', born: 1997.8, end: [1290, 360], c1: [60, -70], c2: [-60, 110], w: [24, 5], decor: [[0.6, 0.6, 26]],", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " mkBranch({ id: 'science', name: '搞科研', born: 2019.8, end: [740, 280], c1: [-80, -30], c2: [80, 70], w: [20, 5], rate: 0.65,", "old_string": " mkBranch({ id: 'science', name: '搞科研', born: 2019.8, end: [740, 280], c1: [-80, -30], c2: [80, 70], w: [20, 5],", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": "// the crown's milestones sit inside the crown like fruit (x, y as fractions of the crown radius)\nconst CROWN_NODES = [\n { y: 2022.91, tag: 'ChatGPT', p: [-0.42, -0.12] }, { y: 2024.70, tag: '推理模型', p: [0.45, -0.12] },\n { y: 2025.053, tag: 'DeepSeek', p: [0.0, -0.62] }, { y: 2025.15, tag: '智能体', p: [-0.36, 0.42] }, { y: 2026.30, tag: '多模态', p: [0.38, 0.42] },\n];\nconst TAG_LIFE = 9; // seconds a milestone label stays up before it gives way to newer ones", "old_string": "const CROWN_NODES = [\n { y: 2022.91, tag: 'ChatGPT', ang: -2.3, r: 0.72 }, { y: 2024.70, tag: '推理模型', ang: -0.75, r: 0.78 },\n { y: 2025.053, tag: 'DeepSeek', ang: -1.75, r: 0.86 }, { y: 2025.15, tag: '智能体', ang: -2.75, r: 0.9 }, { y: 2026.30, tag: '多模态', ang: -0.25, r: 0.95 },\n];", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": "function branchGrowth(b, t) {\n const Y = yearAt(t);\n return Math.min(easeOut((t - b.tBorn) / 2.4), clamp(0.3 + (Y - b.born) * (b.rate || (b.born < 1990 ? 0.03 : 0.12))));\n}", "old_string": "function branchGrowth(b, t) {\n const Y = yearAt(t);\n return Math.min(easeOut((t - b.tBorn) / 2.4), clamp(0.3 + (Y - b.born) * (b.born < 1990 ? 0.03 : 0.12)));\n}", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
思考：Adjusting foliage rendering for tag timing and fade effects.…
Adjusting foliage rendering for tag timing and fade effects.
Reworking name fade timing for withered branches.修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " if (n.tag) { // a milestone is labelled for a few seconds; it gives way early when the next one on its branch arrives\n const gone = Math.max(nextTag ? clamp((t - timeOf(nextTag.y) - 1.2) / 0.8) : 0, clamp((t - tn - TAG_LIFE) / 0.8), clamp(wk * 3));\n const a = clamp((t - tn) / 0.5) * (1 - gone) * p2fade;", "old_string": " if (n.tag) { // only the newest milestone keeps its label; older ones fade away\n const gone = nextTag ? clamp((t - timeOf(nextTag.y) - 1.2) / 0.8) : 0;\n const a = clamp((t - tn) / 0.5) * (1 - gone) * p2fade * (wk > 0 ? 0.85 : 1);", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " const tipPos = bz(...b.P, Math.min(g, 1)), na = clamp((g - 0.42) / 0.22) * (wk > 0.5 ? lerp(1, 0.55, clamp((t - PART2) / 2)) : 1);\n if (na > 0.01) {\n const dir = L.nameDir[b.id], r = 30 * z, fs = lerp(F.name, F.nameP2, easeInOut((t - PART2 - 1.5) / 3));\n const o = { right: [r + 12, fs * 0.36, 'left'], left: [-(r + 12), fs * 0.36, 'right'], up: [0, -(r + F.tag + 24), 'center'], down: [0, r + fs + 6, 'center'] }[dir];\n labels.push({ s: b.name, x: tipPos[0], y: tipPos[1], ox: o[0], oy: o[1], align: o[2], size: fs, weight: 700, color: wk > 0.5 ? C.greyDark : C.ink, alpha: na, yearBold: false });\n const said = b.wither ? clamp((wk - 0.3) / 0.4) * (1 - clamp((t - timeOf(b.wither) - 8) / 1.0)) : 0;\n if (said > 0.01) labels.push({ s: '曾经风光，后来退场', x: tipPos[0], y: tipPos[1], ox: o[0], oy: o[1] + F.small + 12, size: F.small, align: o[2], color: C.greyDark, alpha: said, yearBold: false });\n }", "old_string": " const tipPos = bz(...b.P, Math.min(g, 1)), na = clamp((g - 0.3) / 0.25);\n if (na > 0.01) {\n co …（中间省略 394 字）… k : C.ink, alpha: na, yearBold: false });\n if (b.wither && wk > 0.3) labels.push({ s: '曾经风光，后来退场', x: tipPos[0], y: tipPos[1], ox: o[0], oy: o[1] + F.small + 12, size: F.small, align: o[2], color: C.greyDark, alpha: clamp((wk - 0.3) / 0.4), yearBold: false });\n }", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
思考：Working out how crown labels get drawn. Smoothing the color …
Working out how crown labels get drawn.
Smoothing the color shift between fresh and aged crown labels.
Checking whether the chat stamp overlaps nearby label positions.
Finding a clear spot for the stamp above the crown.
Adjusting polaroid captions and slide-in directions for layout.
Fixing overlap between the sliver label and funnel edge.修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " CROWN_NODES.forEach((cn, i) => {\n if (Y < cn.y) return;\n const tn = timeOf(cn.y), sc = backOut((t - tn) / 0.6), x = cs.x + cn.p[0] * cs.r, y = cs.y + cn.p[1] * cs.r * 0.8;\n cluster(x, y, 22, 'gold', 1900 + i * 7, sc);\n const fresh = 1 - clamp((t - tn - 2.5) / 0.8); // a new milestone shows in red for a moment\n labels.push({ s: cn.tag, x, y, oy: L.f.crown * 0.36, size: L.f.crown, weight: 700, align: 'center', color: lerpColor(C.ink, C.red, fresh), alpha: clamp((t - tn) / 0.4), yearBold: false });\n });\n}", "old_string": " for (const cn of CROWN_NODES) {\n if (Y < cn.y) continue;\n const tn = timeOf(cn.y), sc = backOut((t - tn) / 0.6), x = cs.x + Math.cos(cn.ang) * cs.r * cn.r, y = cs.y + Math.sin(cn.ang) * cs.r * cn.r * 0.8;\n cluster(x, y, 24, 'gold', 1900 + CROWN_NODES.indexOf(cn) * 7, sc);\n const left = Math.cos(cn.ang) < 0;\n labels.push({ s: cn.tag, x, y, ox: (left ? -1 : 1) * (24 * cam.z + 8), oy: -6, align: left ? 'right' : 'left', size: L.f.crown, weight: 700, alpha: clamp((t - tn) / 0.4), yearBold: false });\n }\n}", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": "}\n// \"two months, 100 million users\" — stamped above the crown, on top of every label\nfunction drawChatStamp(t, Y, cam) {\n const tc = M.chatgpt; if (t < tc || t > tc + 4.6) return;\n const cs = crownState(t, Y); if (!cs) return;\n const sa = clamp((t - tc - 0.3) / 0.1) * (1 - clamp((t - tc - 3.9) / 0.6));\n if (sa <= 0.01) return;\n const [sx, sy] = toScreen(cs.x, cs.y - cs.r * 0.8 - 70, cam);\n const sc = lerp(1.8, 1, easeOut((t - tc - 0.3) / 0.16)) * (PORTRAIT ? 1.3 : 1.2);\n ctx.save(); ctx.globalAlpha = sa * 0.95; ctx.translate(sx, Math.max(80, sy)); ctx.rotate(-0.1); ctx.scale(sc, sc);\n ctx.fillStyle = 'rgba(255,250,240,0.92)'; ctx.beginPath(); ctx.roundRect(-118, -54, 236, 108, 14); ctx.fill();\n ctx.strokeStyle = C.red; ctx.lineWidth = 4; ctx.beginPath(); ctx.roundRect(-118, -54, 236, 108, 14); ctx.stroke();\n ctx.lineWidth = 1.5; ctx.beginPath(); ctx.roundRect(-110, -46, 220, 92, 10); ctx.stroke();\n text('两个月', 0, -10, { size: 28, weight: 700, color: C.red, align: 'center', yearBold: false });\n text('1 亿人在用', 0, 32, { si …（中间省略 726 字）… ct(-110, -46, 220, 92, 10); ctx.stroke();\n text('两个月', 0, -10, { size: 28, weight: 700, color: C.red, align: 'center', yearBold: false });\n text('1 亿人在用', 0, 32, { size: 34, weight: 700, color: C.red, align: 'center', yearBold: false });\n ctx.restore();\n }\n}", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": "function drawLabels(labels, cam) {\n const k = clamp(Math.pow(cam.z / L.cam.base, 0.3), 0.85, L.zoomCap);\n for (const l of labels) {\n const [x, y] = toScreen(l.x, l.y, cam);\n const sz = l.size * k, opt = { size: sz, weight: l.weight || 400, yearBold: l.yearBold !== false };\n const w = text(l.s, 0, 0, { ...opt, measureOnly: true }).w;\n let ax = x + (l.ox || 0);\n const left = l.align === 'right' ? ax - w : l.align === 'center' ? ax - w / 2 : ax;\n ax += Math.max(0, 14 - left) - Math.max(0, left + w - (W - 14)); // never let a label run off the frame\n text(l.s, ax, y + (l.oy || 0), { ...opt, color: l.color || C.ink, align: l.align, alpha: l.alpha, halo: Math.max(4, sz * 0.22) });\n }\n}", "old_string": "function drawLabels(labels, cam) {\n const k = clamp(Math.pow(cam.z / L.cam.base, 0.3), 0.85, L.zoomCap);\n for (const l of labels) {\n const [x, y] = toScreen(l.x, l.y, cam);\n const sz = l.size * k;\n text(l.s, x + (l.ox || 0), y + (l.oy || 0), { size: sz, weight: l.weight || 400, color: l.color || C.ink, align: l.align, alpha: l.alpha, yearBold: l.yearBold !== false });\n }\n}", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " const bx = tx + L.callout.dx, by = ty + L.callout.dy, s = L.f.callout;", "old_string": " const bx = tx + L.callout.dx, by = ty + L.callout.dy, s = L.f.tag;", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " const inK = easeOut((t - t0) / 0.5), outK = easeInOut((t - t1) / 0.5);\n const P = t0 > PART2 && L.pola2 ? L.pola2 : L.pola, side = P.cx > W / 2 ? 1 : -1; // slides in from the nearest edge\n const cx = P.cx + side * (460 * (1 - inK) + 520 * outK), cy = P.bottom - fh / 2, rot = (i % 2 ? 0.045 : -0.04) - side * 0.18 * (1 - inK);", "old_string": " const inK = easeOut((t - t0) / 0.5), outK = easeInOut((t - t1) / 0.5);\n const cx = L.pola.cx - 460 * (1 - inK) - 520 * outK, cy = L.pola.bottom - fh / 2, rot = (i % 2 ? 0.045 : -0.04) - 0.18 * (1 - inK);", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " let capS = 28; while (capS > 20 && text(POLA[p.id], 0, 0, { size: capS, measureOnly: true, yearBold: false }).w > fw - 22) capS--;\n text(POLA[p.id], 0, fh / 2 - 32, { size: capS, align: 'center', yearBold: false });", "old_string": " text(POLA[p.id], 0, fh / 2 - 32, { size: 28, align: 'center', yearBold: false });", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
运行 grep -n "^function drawDayCard\|^// ===========================================…
grep -n "^function drawDayCard\|^// ================================================================ captions" scene/film.js
823:function drawDayCard(t) { 894:// ================================================================ captions, teaser, title, ending运行 cat > /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpa…
cat > /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/daycard.js <<'EOF' function drawDayCard(t) { const Dd = L.day, t0 = M.day_bar - 0.45, t1 = M.day_out + 0.7; if (t < t0 || t > t1) return; const inK = easeOut((t - t0) / 0.6), outK = easeInOut((t - M.day_out) / 0.65); ctx.save(); ctx.translate(0, (1 - inK) * H * 0.8 + outK * H); ctx.fillStyle = `rgba(243,234,214,${0.6 * (1 - outK)})`; ctx.fillRect(0, -H, W, H * 3); const [cx0, cy0, cw, ch] = Dd.card; paperCard(cx0, cy0, cw, ch, { seed: 8000, fill: '#fffaf0' }); tape(cx0 + cw / 2, cy0 - 4, 170, 32, -0.02); Dd.head.forEach(([s, x, y]) => text(s, x, y, { size: Dd.head2, weight: 700, yearBold: false })); const X0 = Dd.X0, X1 = Dd.X1, Yb = Dd.Yb, Yv = Dd.Yv, hx = (h) => X0 + (X1 - X0) * h / 24, lab = Dd.lab, sm = Dd.small; const dk = easeInOut((t - M.day_bar) / 0.9); // the day: a 24-hour axis that draws itself if (Dd.dayLabel === 'left') text('一天', X0 - 30, Yb + 12, { size: lab, weight: 700, color: C.inkSoft, align: 'right', yearBold: false }); else text('一天（24 小时）', X0, Yb + lab + 62, { size: lab, weight: …（中间省略 4862 字）… with('function drawDayCard')) e = next(i for i, l in enumerate(lines) if l.startswith('// ================================================================ captions')) lines[s:e] = new + [''] p.write_text('\n'.join(lines)) print(s, e) EOF grep -n "function drawDayCard\|captions, teaser" scene/film.js822 893 823:function drawDayCard(t) { 902:// ================================================================ captions, teaser, title, ending思考：Coding the ending animation's timing and sizing logic.
Coding the ending animation's timing and sizing logic.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " const ra = clamp((t - M.roots) / 1.0) * (1 - clamp((t - b.te - 0.2) / 0.8));", "old_string": " const ra = clamp((t - (b.ts + 9.6)) / 1.0) * (1 - clamp((t - b.te - 0.2) / 0.8));", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " text('下一根树枝，会长向哪里？', W / 2, E.question, { size: E.qsize, weight: 700, color: C.ink, align: 'center', yearBold: false, alpha: qa });", "old_string": " text('下一根树枝，会长向哪里？', W / 2, E.question, { size: 44, color: C.ink, align: 'center', yearBold: false, alpha: qa });", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": "function budAlpha(t) { const b = beat('end'); const fa = clamp((t - (b.te + 0.4)) / 1.0); return clamp((t - M.bud) / 0.8) * (1 - fa); }", "old_string": "function budAlpha(t) { const b = beat('end'); const fa = clamp((t - (b.te + 0.4)) / 1.0); return clamp((t - (b.ts + 13.4)) / 0.8) * (1 - fa); }", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
修改 film.js
{ "file_path": "~/ai-tree/scene/film.js", "new_string": " ctx.restore();\n drawLabels(labels, cam);\n drawChatStamp(t, Y, cam);\n drawCallouts(rt, t, Y, cam);", "old_string": " ctx.restore();\n drawLabels(labels, cam);\n drawCallouts(rt, t, Y, cam);", "replace_all": false }The file ~/ai-tree/scene/film.js has been updated successfully.
运行 python3 tools/build_timeline.py 2>&1 | tail -5 && python3 -c "
python3 tools/build_timeline.py 2>&1 | tail -5 && python3 -c " import json; s=open('scene/timeline.js').read(); TL=json.loads(s[len('window.TL = '):].rstrip().rstrip(';')); print(TL['total'], TL['marks']['roots'], TL['marks']['bud'])"b7 111.52 → 124.92 b8 124.92 → 149.95 b9 149.95 → 184.70 b10 184.70 → 231.27 end 231.27 → 258.52 258.52 241.412 246.605
运行 rm -f out/frames/*.png; T="6.0 14.0 36.0 42.5 47.0 53.0 57.5 63.0 67.5 71.0 74.…
rm -f out/frames/*.png; T="6.0 14.0 36.0 42.5 47.0 53.0 57.5 63.0 67.5 71.0 74.5 79.5 84.5 88.0 92.0 98.5 101.5 104.0 108.0 117.0 120.5 122.8 131.0 139.5 145.0 160.5 168.5 176.8 196.0 222.0 238.0 243.5 248.0 257.5"; (timeout 900 python3 tools/frames.py $T > out/frames-land.log 2>&1 &); timeout 900 python3 tools/frames.py --portrait $T > out/frames-port.log 2>&1; sleep 3; tail -n 1 out/frames-land.log out/frames-port.log 2>/dev/null || (tail -n1 out/frames-land.log; tail -n1 out/frames-port.log); ls out/frames | wc -l
==> out/frames-land.log <== errors: none ==> out/frames-port.log <== errors: none 68
运行 cat > tools/sheets.py <<'EOF'
cat > tools/sheets.py <<'EOF' """Contact sheets of the rendered stills: python3 tools/sheets.py -> out/sheets/L*.png, P*.png""" from PIL import Image, ImageDraw from pathlib import Path ROOT = Path(__file__).resolve().parent.parent fr = ROOT / 'out' / 'frames'; sh = ROOT / 'out' / 'sheets'; sh.mkdir(parents=True, exist_ok=True) for f in sh.glob('*.png'): f.unlink() def sheet(prefix, cols, tw, th, per, name): files = sorted(fr.glob(prefix + '*.png')) for k in range(0, len(files), per): grp = files[k:k + per]; rows = (len(grp) + cols - 1) // cols S = Image.new('RGB', (cols * tw, rows * (th + 28)), 'white'); d = ImageDraw.Draw(S) for i, f in enumerate(grp): im = Image.open(f).convert('RGB').resize((tw, th), Image.LANCZOS) x, y = (i % cols) * tw, (i // cols) * (th + 28) S.paste(im, (x, y + 28)); d.text((x + 6, y + 6), f.stem, fill='red') S.save(sh / f'{name}{k // per:02d}.png') sheet('t', 2, 960, 540, 4, 'L') sheet('p', 3, 600, 1067, 3, 'P') print(sorted(p.name for p in sh.glob('*.png'))) EOF python3 tools/sheets.py['L00.png', 'L01.png', 'L02.png', 'L03.png', 'L04.png', 'L05.png', 'L06.png', 'L07.png', 'L08.png', 'P00.png', 'P01.png', 'P02.png', 'P03.png', 'P04.png', 'P05.png', 'P06.png', 'P07.png', 'P08.png', 'P09.png', 'P10.png', 'P11.png']
读取 L00.png
{ "file_path": "~/ai-tree/out/sheets/L00.png" }[图片]
读取 L01.png
{ "file_path": "~/ai-tree/out/sheets/L01.png" }[图片]
思考：Decoding the crown labels in the teaser image. Recalculating…
Decoding the crown labels in the teaser image.
Recalculating label spacing to avoid overlap at different crown sizes.
Fixing label offsets to scale independently of crown zoom.
Checking label positions against other elements for overlap.
Checking how branch name fade-in timing lines up with historical events.读取 L02.png
{ "file_path": "~/ai-tree/out/sheets/L02.png" }[图片]
读取 L03.png
{ "file_path": "~/ai-tree/out/sheets/L03.png" }[图片]
读取 L04.png
{ "file_path": "~/ai-tree/out/sheets/L04.png" }[图片]
思考：排查时间轴图片中文字标注的细节问题。 调整年份标签位置以避免与标尺文字重叠。 调整画画分支标签位置以适配不同屏幕布局。 …
排查时间轴图片中文字标注的细节问题。
调整年份标签位置以避免与标尺文字重叠。
调整画画分支标签位置以适配不同屏幕布局。
检查不同年份节点下标签与元素间的重叠情况。读取 L05.png
{ "file_path": "~/ai-tree/out/sheets/L05.png" }[图片]
读取 L06.png
{ "file_path": "~/ai-tree/out/sheets/L06.png" }[图片]
读取 L07.png
{ "file_path": "~/ai-tree/out/sheets/L07.png" }[图片]
读取 L08.png
{ "file_path": "~/ai-tree/out/sheets/L08.png" }[图片]
思考：Fixing overlapping label text on a chart layout. Resizing cr…
Fixing overlapping label text on a chart layout.
Resizing crowns and recalculating label offsets to avoid overlap.读取 P00.png
{ "file_path": "~/ai-tree/out/sheets/P00.png" }[图片]
读取 P03.png
{ "file_path": "~/ai-tree/out/sheets/P03.png" }[图片]
读取 P04.png
{ "file_path": "~/ai-tree/out/sheets/P04.png" }[图片]
读取 P05.png
{ "file_path": "~/ai-tree/out/sheets/P05.png" }[图片]
读取 P06.png
{ "file_path": "~/ai-tree/out/sheets/P06.png" }[图片]
思考：Reviewing proportions and spacing in a portrait layout. Repo…
Reviewing proportions and spacing in a portrait layout.
Repositioning the ground line to balance sky and foliage.
Checking card and photo placement against overlapping elements.
Resolving overlapping labels near the tree's crown and branches.
Separating conflicting tags to avoid timeline collisions.
Adjusting label positions to stop twig and name tags colliding.
Simplifying the twig label and planning portrait coordinate shifts.运行 grep -n "nameDir\|b.below\|tw.below\|twigs:\|（后来退场）\|function crownState" scene…
grep -n "nameDir\|b.below\|tw.below\|twigs:\|（后来退场）\|function crownState" scene/film.js | head -20
46: nameDir: { chat: 'right', robot: 'right', expert: 'left', chess: 'right', speech: 'up', vision: 'right', paint: 'up', science: 'up' }, 72: nameDir: { chat: 'down', robot: 'up', expert: 'left', chess: 'up', speech: 'up', vision: 'up', paint: 'up', science: 'up' }, 118: twigs: [{ y: 2011.1, t: 0.8, ang: 1.2, len: 44, tag: '2011 问答节目夺冠', wither: 2022.0, below: true }] }), 378: const r = size * z, dir = L.nameDir[b.id]; 379: const below = (pos.below || (pos.tip && b.below && dir !== 'down')); 398: if (a > 0.01) labels.push({ s: tw.tag + (wt > 0.6 ? '（后来退场）' : ''), x: e[0], y: e[1], ox: 10, oy: tw.below ? 16 * z + F.small + 6 : -(16 * z + 6), size: F.small, align: 'left', color: wt > 0.5 ? C.greyDark : C.ink, alpha: clamp(a) * p2fade }); 403: const dir = L.nameDir[b.id], r = 30 * z, fs = lerp(F.name, F.nameP2, easeInOut((t - PART2 - 1.5) / 3)); 468:function crownState(t, Y) {读取 film.js
{ "file_path": "~/ai-tree/scene/film.js", "limit": 32, "offset": 370 }370 for (let i = 0; i < 4; i++) { const fall = clamp((t - timeOf(b.wither) - i * 0.25) / 1.8); 371 const x = pos.at[0] + (R() - 0.5) * 30 + Math.sin(fall * 6 + i) * 14, y = lerp(pos.at[1], GROUND - 6 - R() * 8, easeInOut(fall)); 372 leafShape(x, y, 14, R() * 6.28 + fall * 3, seed + 20 + i, 'rgba(110,100,85,0.8)', '#c5b79e'); } 373 } 374 if (n.tag) { // a milestone is labelled for a few seconds; it gives way early when the next one on its branch arrives 375 const gone = Math.max(nextTag ? clamp((t - timeOf(nextTag.y) - 1.2) / 0.8) : 0, clamp((t - tn - TAG_LIFE) / 0.8), clamp(wk * 3)); 376 const a = clamp((t - tn) / 0.5) * (1 - gone) * p2fade; 377 if (a > 0.01) { 378 const r = size * z, dir = L.nameDir[b.id]; 379 const below = (pos.below || (pos.tip && b.below && dir !== 'down')); 380 labels.push({ s: n.tag, x: pos.at[0], y: pos.at[1] - 4, oy: below ? r + F.tag + 4 : -(r + 6), size: F.tag, align: 'center', color: C.ink, alpha: a }); 381 } 382 } 383 }); 384 (b.decor || []).forEach(([dt, ang, len], k) => { 385 if (g < dt + 0.05) return; 386 const p = bz(...b.P, dt), gk = clamp((g - dt - 0.05) / 0.2), e = [p[0] + Math.cos(ang) * len * gk, p[1] + Math.sin(ang) * len * gk]; 387 rc.line(p[0], p[1], e[0], e[1], { stroke: wk > 0.5 ? C.greyDark : C.ink, strokeWidth: 1.8, roughness: 0.8, seed: sd(1450 + BR.indexOf(b) * 7 + k) }); 388 if (wk < 0.8 & …（中间省略 1146 字）… els.push({ s: tw.tag + (wt > 0.6 ? '（后来退场）' : ''), x: e[0], y: e[1], ox: 10, oy: tw.below ? 16 * z + F.small + 6 : -(16 * z + 6), size: F.small, align: 'left', color: wt > 0.5 ? C.greyDark : C.ink, alpha: clamp(a) * p2fade }); 399 }); 400 // branch name 401 const tipPos = bz(...b.P, Math.min(g, 1)), na = clamp((g - 0.42) / 0.22) * (wk > 0.5 ? lerp(1, 0.55, clamp((t - PART2) / 2)) : 1);思考：Fixing tag placement and twig label calculations in code.
Fixing tag placement and twig label calculations in code.
运行 python3 - <<'EOF'
python3 - <<'EOF' import json s = open('scene/timeline.js').read() TL = json.loads(s[len("window.TL = "):].rstrip().rstrip(";")) for c in TL["captions"]: if 56 < c["t0"] < 112: print(round(c["t0"],2), round(c["t1"],2), c["text"]) EOF56.45 58.81 后来换了条路：不写规则了 58.95 61.81 让电脑从大量例子里自己找规律 61.92 63.17 这叫“机器学习” 63.46 66.95 1997年，每秒能算两亿步的深蓝 66.96 69.07 赢了国际象棋世界冠军 73.17 77.01 2012年，那颗冻了几十年的芽突然开花 77.91 80.88 很深的神经网络，喂进海量图片 80.9 83.06 用两块打游戏的显卡来算 83.07 84.46 这就是“深度学习” 84.76 87.9 2016年，AlphaGo先学人类棋谱 87.82 90.09 再自己跟自己下，赢了李世石 90.39 92.67 2017年，Transformer诞生 92.71 95.2 后来GPT里的T，就是它 97.09 101.16 人们发现，模型越大、数据越多，它就越聪明 101.44 103.93 2022年底，ChatGPT上线 104.05 106.49 据估计两个月就有一亿人在用 106.77 109.51 各条树枝，开始长进同一个树冠
思考：Deciding where to place a small visual detail across layouts…
Deciding where to place a small visual detail across layouts.
运行 python3 - <<'EOF'
python3 - <<'EOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new, count=1): global s n = s.count(old) assert n == count, (n, old[:80]) s = s.replace(old, new) # per-layout tag placement, twig label, name directions rep(" nameDir: { chat: 'right', robot: 'right', expert: 'left', chess: 'right', speech: 'up', vision: 'right', paint: 'up', science: 'up' },", " nameDir: { chat: 'right', robot: 'right', expert: 'left', chess: 'right', speech: 'up', vision: 'right', paint: 'up', science: 'up' },\n tagBelow: ['chat', 'expert', 'vision'], twigLabel: true,") rep(" nameDir: { chat: 'down', robot: 'up', expert: 'left', chess: 'up', speech: 'up', vision: 'up', paint: 'up', science: 'up' },", " nameDir: { chat: 'down', robot: 'up', expert: 'left', chess: 'right', speech: 'up', vision: 'up', paint: 'up', science: 'up' },\n tagBelow: ['expert'], twigLabel: false,") rep(""" const r = size * z, dir = L.nameDir[b.id]; const below = (pos.below || (pos.tip && b.below && dir !== 'down')); labels.push({ s: n.tag, x: pos.at[0], y: p …（中间省略 3474 字）… .85, L.zoomCap) / 34 / cam.z; // screen units -> world const tn = timeOf(cn.y), sc = backOut((t - tn) / 0.6), x = cs.x + cn.o[0] * u, y = cs.y + cn.o[1] * u;""") p.write_text(s) print("ok") EOF grep -n "crownState(t, Y)\b" scene/film.js | head; grep -n "^function crownState" -A 9 scene/film.jsok 469:function crownState(t, Y) { 470- const f = fracOf(Y), c = trunkAt(f); 471- const ts = timeOf(2020.3); if (t < ts) return null; 472- let r = 50 + 75 * easeOut((Y - 2020.3) / 3.7); 473- if (Y > 2024) r = 125 + 75 * clamp((Y - 2024) / 2.78); 474- r *= 1 + 0.16 * crownPulse(t); 475- return { x: c[0], y: c[1] + r * 0.12, r: r * easeOut((t - ts) / 1.5) }; 476-} 477-function drawCrown(t, Y, labels, cam) { 478- const cs = crownState(t, Y); if (!cs) return;思考：Working out how the crown's height scales with its radius. C…
Working out how the crown's height scales with its radius.
Checking tree layouts against panels across portrait and landscape views.运行 rm -f out/frames/*.png; T="6.0 9.6 14.0 28.0 36.0 53.0 67.5 71.0 74.5 84.5 88.0…
rm -f out/frames/*.png; T="6.0 9.6 14.0 28.0 36.0 53.0 67.5 71.0 74.5 84.5 88.0 101.5 104.0 108.0 131.0 145.0 160.5 168.5 196.0 222.0 243.5 248.0"; (timeout 900 python3 tools/frames.py $T > out/frames-land.log 2>&1 &); timeout 900 python3 tools/frames.py --portrait $T > out/frames-port.log 2>&1; sleep 4; tail -n1 out/frames-land.log; tail -n1 out/frames-port.log; python3 tools/sheets.py
errors: none errors: none ['L00.png', 'L01.png', 'L02.png', 'L03.png', 'L04.png', 'L05.png', 'P00.png', 'P01.png', 'P02.png', 'P03.png', 'P04.png', 'P05.png', 'P06.png', 'P07.png']
读取 P00.png
{ "file_path": "~/ai-tree/out/sheets/P00.png" }[图片]
读取 P01.png
{ "file_path": "~/ai-tree/out/sheets/P01.png" }[图片]
读取 P02.png
{ "file_path": "~/ai-tree/out/sheets/P02.png" }[图片]
读取 P03.png
{ "file_path": "~/ai-tree/out/sheets/P03.png" }[图片]
读取 P04.png
{ "file_path": "~/ai-tree/out/sheets/P04.png" }[图片]
读取 P05.png
{ "file_path": "~/ai-tree/out/sheets/P05.png" }[图片]
读取 P06.png
{ "file_path": "~/ai-tree/out/sheets/P06.png" }[图片]
思考：Resolving layout collisions between labeled portrait element…
Resolving layout collisions between labeled portrait elements.
Recalculating label positions to clear overlapping tags.读取 L00.png
{ "file_path": "~/ai-tree/out/sheets/L00.png" }[图片]
读取 L02.png
{ "file_path": "~/ai-tree/out/sheets/L02.png" }[图片]
读取 L03.png
{ "file_path": "~/ai-tree/out/sheets/L03.png" }[图片]
读取 L05.png
{ "file_path": "~/ai-tree/out/sheets/L05.png" }[图片]
思考：Reviewing remaining layout details in the landscape design. …
Reviewing remaining layout details in the landscape design.
Checking label spacing to avoid overlapping tags.
Verifying timing offsets so tags never overlap on screen.运行 python3 - <<'EOF'
python3 - <<'EOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s assert s.count(old) == 1, old[:70] s = s.replace(old, new) rep(" nameDir: { chat: 'right', robot: 'right', expert: 'left', chess: 'right', speech: 'up', vision: 'right', paint: 'up', science: 'up' },\n tagBelow: ['chat', 'expert', 'vision'], twigLabel: true,", " nameDir: { chat: 'right', robot: 'right', expert: 'left', chess: 'right', speech: 'left', vision: 'right', paint: 'up', science: 'up' },\n tagBelow: ['chat', 'expert', 'vision', 'paint'], twigLabel: true,") rep(" nameDir: { chat: 'down', robot: 'up', expert: 'left', chess: 'right', speech: 'up', vision: 'up', paint: 'up', science: 'up' },", " nameDir: { chat: 'down', robot: 'up', expert: 'left', chess: 'up', speech: 'up', vision: 'up', paint: 'up', science: 'up' },") p.write_text(s); print('ok') EOF rm -f out/frames/*.png; T="6.0 67.5 74.5 84.5 88.0 101.5 104.0 131.0"; (timeout 600 python3 tools/frames.py $T > out/frames-land.log 2>&1 &); timeout 600 python3 tools/frames.py --portrait $T > out/frames-port.log 2>&1; sleep 3; tail -n1 out/frames-land.log; tail -n1 out/frames-port.log; python3 tools/sheets.pyok errors: none errors: none ['L00.png', 'L01.png', 'P00.png', 'P01.png', 'P02.png']
读取 L01.png
{ "file_path": "~/ai-tree/out/sheets/L01.png" }[图片]
读取 P00.png
{ "file_path": "~/ai-tree/out/sheets/P00.png" }[图片]
读取 P01.png
{ "file_path": "~/ai-tree/out/sheets/P01.png" }[图片]
读取 P02.png
{ "file_path": "~/ai-tree/out/sheets/P02.png" }[图片]
思考：Fixing overlapping text issues in a portrait layout. Weighin…
Fixing overlapping text issues in a portrait layout.
Weighing alternate label placements for the robot and chess tags.
Checking whether a downward robot label clears the chat branch.
Designing a generic nudge algorithm to avoid label jitter.
Repositioning timestamp stamps to avoid overlapping tag labels.
Repositioning a portrait stamp to clear a science tag.
Testing stamp placements against the crown and timeline tags.
Dimming node tags while the stamp overlay appears.
Reordering label fade logic to resolve overlapping timeline tags.
Adjusting a landscape label to avoid overlapping the vision tag.
Designing two-line label blocks for right-aligned timeline tags.
Devising a per-node nudge table to shift a specific overlapping tag.
Devising a priority rule to fade older tags over newer ones.
Smoothing label suppression to avoid abrupt fading pops.
Prioritizing narrated tags to keep key labels from fading early.
Building a diagnostic tool to detect label overlaps automatically.
Scanning transitional animations for brief, acceptable label overlaps.
Checking whether a shifted robot label still clears the branch tip.
Crossfading suppression weights to prevent sudden label priority swaps.
Suppressing labels hidden beneath overlapping panels and stamps.
Computing panel rectangles before suppressing labels beneath them.
Designing weighted collision scores for labels sharing screen space.
Tuning recency weighting to keep fresh tags readable.
Timing the tag-to-name weight crossover precisely.
Laying out two-line tag blocks for differing label directions.
Checking tag spans against screen edges and the date panel region.
Refactoring overlay code into shared state and rectangle functions.
Extracting shared geometry logic for cards and polaroids.
Positioning a landscape stamp to clear a crown label.
Verifying portrait and landscape stamp placements against nearby tags.
Computing panel rectangles and drafting shared label-drawing helpers.
Suppressing labels covered by overlapping panel rectangles.运行 grep -n "^function \|^const \|^let \|^// ====" scene/film.js | sed -n '1,200p'
grep -n "^function \|^const \|^let \|^// ====" scene/film.js | sed -n '1,200p'
6:const TL = window.TL, M = TL.marks; 7:const GROUND = 950; 9:// ================================================================ time ↔ calendar 10:const YK = TL.ykeys; 11:function yearAt(t) { 19:const tCache = new Map(); 20:function timeOf(y) { 30:const beat = (id) => TL.beats.find(b => b.id === id); 31:const PART2 = beat('b7').t0, ENDT = beat('end').t0; 33:function sceneTime(t) { // teaser: fast-forward through the story, hold on the 2026 tree, rewind to the seed 42:// ================================================================ layouts (screen space) 43:const LAND = { 70:const PORT = { 96:const L = PORTRAIT ? PORT : LAND; 98:// ================================================================ tree layout (world space) 99:const TRUNK = [[1000, 958], [985, 760], [1022, 480], [1000, 200]]; 100:const FRAC = [[1950, 0], [1956, 0.12], [1966, 0.20], [1975, 0.26], [1980, 0.30], [1993, 0.40], [1997, 0.43], [2011, 0.53], [2012, 0.54], 102:function fracOf(y) { 107:const trunkAt = (f) => bz(...TRUNK, f); 108:const trunkK = (f) => Math.pow(Math.min(1, f / 0.6), 1.1); 109:const trunkW = (f) => 150 + (36 - 150) * f; 110:function mkBranch(o) { 117:const BR = [ 136:const BRMAP = Object.fromEntries(BR.map(b => [b.id, b])); 137:const SPROUT = { fr: 0.07, born: 1958, freeze: 1969.2, thaw: 1986, bloom: 2011.95 }; 138:function sproutP(Y) { 143:const CROWN_NODES = [ 147:const TAG_LIFE = 9; // seconds a milestone label stays up before it gives way to newer ones 148:const VINES = ['chat', …（中间省略 4965 字）… Caption(t) { 935:function speedTriangles(cx, cy, s, dir, seed) { 941:function drawTeaser(t) { 961:function drawTitle(t) { 973:function drawEnding(t, cam) { 1000:function budAlpha(t) { const b = beat('end'); const fa = clamp((t - (b.te + 0.4)) / 1.0); return clamp((t - M.bud) / 0.8) * (1 - fa); } 1002:// ================================================================ frame 1003:function seek(rt) {读取 film.js
{ "file_path": "~/ai-tree/scene/film.js", "limit": 120, "offset": 296 }296 if (span < need) return; const kk = clamp((t - timeOf(appear)) / 1.0); if (kk <= 0) return; 297 const fm = (fa + top) / 2, [x, y] = trunkAt(fm), h = str.length * size * 1.08; 298 ctx.save(); ctx.globalAlpha *= kk; ctx.textAlign = 'center'; ctx.textBaseline = 'middle'; ctx.font = `700 ${size}px ${HAND}`; 299 [...str].forEach((ch, i) => { const cy = y - h / 2 + (i + 0.5) * size * 1.08; ctx.fillStyle = 'rgba(70,40,20,0.55)'; ctx.fillText(ch, x + 1.5, cy + 1.5); ctx.fillStyle = '#fbf1d8'; ctx.fillText(ch, x, cy); }); 300 ctx.restore(); 301 }; 302 carve('写规则', 0.10, 0.40, 26, 1958); carve('找规律', 0.40, 0.54, 24, 1994); carve('神经网络', 0.54, 0.67, 21, 2012.0); carve('大模型', 0.67, 0.86, 22, 2020.3); 303 } 304 305 function drawSprout(t, Y, labels, cam) { 306 if (Y < SPROUT.born) return; 307 const g = easeOut((t - timeOf(SPROUT.born)) / 1.6); 308 const frozen = Y >= SPROUT.freeze && Y < SPROUT.thaw ? 1 : 0; 309 const thaw = clamp((t - timeOf(SPROUT.thaw)) / 2); 310 const SP = sproutP(Y); 311 drawLimb(SP, 9, 3, g, { fill: '#8fae68', hatch: C.nnDark, seed: 700 }); 312 const tip = bz(...SP, g), z = cam.z; 313 if (Y < SPROUT.bloom) cluster(tip[0] - 4, tip[1] - 6, 12, 'fresh', 710, g, 1 - 0.5 * frozen); 314 if (t < timeOf(SPROUT.born) + 4.2) labels.push({ s: '感知机', x: tip[0], y: tip[1], ox: -16 * z, oy: -18 * z, size: L.f.tag, weight: 700, align: 'right', alpha: win(t, timeOf(SPROUT.born) + 0.6, timeOf(SPROUT.born) + 4.2), yearBold: false }); 315 …（中间省略 7913 字）… 410 } 411 const ex = BRMAP.expert; if (t > timeOf(ex.wither) + 1.8) { const R = rng(1530); for (let i = 0; i < 9; i++) leafShape(690 + R() * 260, GROUND - 6 + R() * 12, 13 + R() * 6, R() * 6.28, 1540 + i, 'rgba(110,100,85,0.75)', i % 2 ? '#bfb29a' : '#cbbfa6'); } 412 } 413 414 function drawWinterLeaves(t, Y) { 415 [[M.winter1, M.spring1], [M.winter2, M.spring2]].forEach(([ws, we], wi) => {思考：Repositioning tree node tags based on branch direction. Alig…
Repositioning tree node tags based on branch direction.
Aligning tag and name anchor points for tip labels.
Fine-tuning spacing and overlap thresholds for stacked labels.
Assigning weight rules for label freshness and permanence.
Collecting rectangle geometry from each panel into overlay checks.读取 film.js
{ "file_path": "~/ai-tree/scene/film.js", "limit": 135, "offset": 468 }468 function crownPulse(t) { const tc = M.chatgpt; return clamp((t - tc) / 0.22) * (1 - clamp((t - tc - 0.22) / 0.9)); } 469 function crownState(t, Y) { 470 const f = fracOf(Y), c = trunkAt(f); 471 const ts = timeOf(2020.3); if (t < ts) return null; 472 let r = 50 + 75 * easeOut((Y - 2020.3) / 3.7); 473 if (Y > 2024) r = 125 + 75 * clamp((Y - 2024) / 2.78); 474 r *= 1 + 0.16 * crownPulse(t); 475 return { x: c[0], y: c[1] + r * 0.12, r: r * easeOut((t - ts) / 1.5) }; 476 } 477 function drawCrown(t, Y, labels, cam) { 478 const cs = crownState(t, Y); if (!cs) return; 479 const R = rng(1800), n = Math.round(6 + cs.r / 12); 480 for (let i = 0; i < n; i++) { const a = R() * 6.283, d = cs.r * 0.65 * Math.sqrt(R()); wc(cs.x + Math.cos(a) * d, cs.y + Math.sin(a) * d * 0.8, cs.r * (0.28 + 0.18 * R()), LEAF_PAL.gold[i % 4], 1810 + i, 0.32); } 481 for (let i = 0; i < n + 6; i++) { const a = R() * 6.283, d = cs.r * 0.85 * Math.sqrt(R()); leafShape(cs.x + Math.cos(a) * d, cs.y + Math.sin(a) * d * 0.8, 12 + R() * 10, R() * 6.283, 1850 + i); } 482 CROWN_NODES.forEach((cn, i) => { 483 if (Y < cn.y) return; 484 const u = L.f.crown * clamp(Math.pow(cam.z / L.cam.base, 0.3), 0.85, L.zoomCap) / 34 / cam.z; // screen units -> world 485 const tn = timeOf(cn.y), sc = backOut((t - tn) / 0.6), x = cs.x + cn.o[0] * u, y = cs.y + cn.o[1] * u; 486 cluster(x, y, 22, 'gold', 1900 + i * 7, sc); 487 const fresh = 1 - clamp((t - tn - 2.5) / 0.8); // a new mileston …（中间省略 8488 字）… ruler, time speed, words learned 599 function fmtDate(Y) { 600 const yr = Math.floor(Y + 1e-6); if (Y < 2024) return [String(yr), '']; 601 const start = Date.UTC(yr, 0, 1), days = (Date.UTC(yr + 1, 0, 1) - start) / 864e5, d = new Date(start + Math.floor((Y - yr) * days + 1e-6) * 864e5); 602 const mm = String(d.getUTCMonth() + 1).padStart(2, '0'), dd = String(d.getUTCDate()).padStart(2, '0');思考：Refactoring foliage drawing code to use a shared offset.
Refactoring foliage drawing code to use a shared offset.
读取 film.js
{ "file_path": "~/ai-tree/scene/film.js", "limit": 160, "offset": 664 }664 ctx.restore(); 665 } 666 667 // ================================================================ term cards 668 function drawCards(t) { 669 TL.cards.forEach((c, i) => { 670 const hold = cardHold(i), t0 = c.t, t1 = c.t + hold; if (t < t0 || t > t1 + 0.7) return; 671 const T = TERMS[c.id], inK = backOut((t - t0) / 0.55), outK = easeInOut((t - t1) / 0.6); 672 const w = L.card.w, h = L.card.h, [px, py] = t0 > PART2 ? L.card.p2 : L.card.p1; 673 const cx = px + w / 2, cy = py + h / 2, Ch = L.chip; 674 const vx = Ch.align === 'left' ? Ch.x + 18 : Ch.x - 200, vy = Ch.y - 10; 675 ctx.save(); 676 const tx = lerp(cx, vx, outK), ty = lerp(cy + (py > H / 2 ? 160 : -140) * (1 - inK), vy, outK), s = lerp(1, 0.1, outK), rot = lerp(-0.1 * (1 - inK) + 0.015, 0, outK); 677 ctx.globalAlpha = clamp((t - t0) / 0.25) * (1 - clamp((outK - 0.75) / 0.25)) * (1 - dayCover(t)); 678 ctx.translate(tx, ty); ctx.rotate(rot); ctx.scale(s, s); ctx.translate(-w / 2, -h / 2); 679 paperCard(0, 0, w, h, { seed: 3200 + i }); 680 const ML = 170; 681 for (let k = 0; k < 4; k++) rc.line(14, 100 + k * 48, w - 14, 100 + k * 48, { stroke: '#cfe0ef', strokeWidth: 1, roughness: 0.3, seed: sd(3210 + k) }); 682 rc.line(ML, 12, ML, h - 12, { stroke: '#f0b8b0', strokeWidth: 1, roughness: 0.3, seed: sd(3220) }); 683 tape(w / 2, -4, 100, 26, -0.04); 684 ctx.save(); ctx.translate(ML / 2, h * 0.56); ctx.scale(1.3, 1.3); DOODLES[c.id](0, 0, 3300 + i * 20); ctx.re …（中间省略 9136 字）… 12, -h / 2 + 12, w - 24, h - 24); 816 text(s, 0, size * 0.36, { size, font: BRUSH, color: C.red, align: 'center', yearBold: false }); 817 ctx.restore(); 818 }); 819 } 820 821 // ================================================================ the one-day card (an inverted cosmic calendar) 822 const DAY0 = 1950.0, DAY1 = 2026.79; 823 const hourOf = (y) => (y - DAY0) / (DAY1 - DAY0) * 24;读取 film.js
{ "file_path": "~/ai-tree/scene/film.js", "limit": 160, "offset": 904 }904 // ================================================================ captions, teaser, title, ending 905 const capCache = new Map(); 906 function captionLines(s) { 907 const C0 = L.cap; if (capCache.has(s)) return capCache.get(s); 908 const width = (x) => text(x, 0, 0, { size: C0.size, measureOnly: true, yearBold: false }).w; 909 let out = [s]; 910 if (width(s) > C0.maxW) { 911 const cs = [...s]; let best = -1, sc = 1e9; 912 for (let i = 2; i < cs.length - 2; i++) { 913 if (/[A-Za-z0-9.]/.test(cs[i]) && /[A-Za-z0-9.]/.test(cs[i + 1])) continue; 914 if ('，：、；”）'.includes(cs[i + 1])) continue; 915 const a = cs.slice(0, i + 1).join(''), b = cs.slice(i + 1).join(''); 916 const v = Math.abs(width(a) - width(b)) + ('，：、；”'.includes(cs[i]) ? 0 : 120); 917 if (v < sc) { sc = v; best = i; } 918 } 919 out = [cs.slice(0, best + 1).join(''), cs.slice(best + 1).join('')]; 920 } 921 capCache.set(s, out); return out; 922 } 923 function drawCaption(t) { 924 const c = TL.captions.find(c => t >= c.t0 - 0.05 && t <= c.t1); if (!c) return; 925 const a = clamp(Math.min((t - c.t0 + 0.05) / 0.15, (c.t1 - t) / 0.15)); 926 const C0 = L.cap, lines = captionLines(c.text), lh = C0.size * 1.3; 927 const tw = Math.max(...lines.map(l => text(l, 0, 0, { size: C0.size, measureOnly: true, yearBold: false }).w)); 928 const w = tw + 76, h = lines.length * lh + 24, x = W / 2 - w / 2, y = C0.y - h; 929 ctx.save(); ctx.globalAlpha = a; 930 …（中间省略 7083 字）… 4px ${HAND}`, all), document.fonts.load(`150px ${BRUSH}`, 'AI这棵树0123456789')]); 1050 await loadPhotos(['turing', 'perceptron', 'shakey', 'deepblue', 'stanley']); 1051 buildPaper(); 1052 window.seek = seek; window.TOTAL = TL.total; window.__ready = true; 1053 window.timeSeek = (t) => { const a = performance.now(); seek(t); return performance.now() - a; }; 1054 } 1055 init(); 1056 })(); 1057运行 cp scene/film.js /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c…
cp scene/film.js /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/film.before-layout.js && python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old) assert n == 1, (n, old[:90]) s = s.replace(old, new) # ---------- milestones per branch (node tags + twigs), name geometry shared by names and tip tags rep("const BRMAP = Object.fromEntries(BR.map(b => [b.id, b]));", """const BRMAP = Object.fromEntries(BR.map(b => [b.id, b])); BR.forEach(b => { b.milestones = [...b.nodes.filter(n => n.tag).map(n => n.y), ...(b.twigs || []).map(x => x.y)].sort((p, q) => p - q); }); const nameSize = (t) => lerp(L.f.name, L.f.nameP2, easeInOut((t - PART2 - 1.5) / 3)); function nameOffset(dir, z, fs) { // where a branch name sits relative to the branch tip (screen px) const r = 30 * z; return { right: [r + 12, fs * 0.36, 'left'], left: [-(r + 12), fs * 0.36, 'right'], up: [0, -(r + L.f.tag + 30), 'center'], down: [0, r + fs + 6, 'center'] }[dir]; }""") # ---------- sprout labels carry their birth time (used …（中间省略 20011 字）… 186, size: 170, sizeP2: 150, md: 60 }, era: { x: 60, y: 268, size: 52 },", " date: { x: 56, y: 186, size: 170, sizeP2: 150, md: 60 }, era: { x: 60, y: 268, size: 52 }, panel: [20, 40, 1060, 356],") p.write_text(s) print("refactor ok") PYEOF grep -n "nextTag\|L.tagBelow\|tagBelow" scene/film.jsrefactor ok 47: tagBelow: ['chat', 'expert', 'vision', 'paint'], twigLabel: true, 74: tagBelow: ['expert'], twigLabel: false, 373: const size = pos.tip ? 30 : 19, nextTag = tagged[tagged.indexOf(n) + 1]; 374: const isLatest = n.tag && !(nextTag && Y >= nextTag.y);
思考：Adjusting the calendar layout code for panel labels. Tracing…
Adjusting the calendar layout code for panel labels.
Tracing the drawCallouts and drawCards rendering code paths.运行 cat > tools/qa_layout.py <<'EOF'
cat > tools/qa_layout.py <<'EOF' """Layout QA: samples the film and reports label collisions, labels hidden by the collision rules, and overlapping overlays. usage: python3 tools/qa_layout.py [--portrait] [--step 0.25] [--start 0] [--end TOTAL] """ import argparse, json from pathlib import Path from collections import defaultdict from playwright.sync_api import sync_playwright ROOT = Path(__file__).resolve().parent.parent ap = argparse.ArgumentParser() ap.add_argument("--portrait", action="store_true"); ap.add_argument("--step", type=float, default=0.25) ap.add_argument("--start", type=float, default=0); ap.add_argument("--end", type=float, default=None) a = ap.parse_args() W, H = (1080, 1920) if a.portrait else (1920, 1080) def inter(p, q): return max(0, min(p[2], q[2]) - max(p[0], q[0])) * max(0, min(p[3], q[3]) - max(p[1], q[1])) def area(p): return max(0, p[2] - p[0]) * max(0, p[3] - p[1]) events = defaultdict(list) # key -> [times] with sync_playwright() as pw: b = pw.chromium.launch(); page = b.new_page(viewport={"width": W, "height": H}) page.goto((ROOT / "scene" / "film.html" …（中间省略 1597 字）… ) for x in key[1:])} @ {rr}") print("done", "portrait" if a.portrait else "landscape") EOF (timeout 900 python3 tools/qa_layout.py > out/qa-land.txt 2>&1 &); timeout 900 python3 tools/qa_layout.py --portrait > out/qa-port.txt 2>&1; sleep 5; wc -l out/qa-land.txt out/qa-port.txt; cat out/qa-land.txt45 out/qa-land.txt 37 out/qa-port.txt 82 total hidden 画画 · 创作 @ 3.75-7.50, 113.00-232.50, 252.75-258.50 hidden 看图 @ 7.25-7.50, 69.25-69.25, 83.75-83.75, 87.75-87.75, 88.25-88.25, 91.25-91.50, 95.75-95.75, 104.75-104.75, 113.00-124.50, 125.25-126.00, 150.25-151.00, 185.00-185.75, 252.75-258.50 hidden ChatGPT @ 7.25-7.75, 113.00-124.50, 125.25-126.00, 150.25-151.00, 185.00-185.75, 252.75-258.50 hidden 推理模型 @ 7.25-7.50, 150.25-151.00, 185.00-185.75, 252.75-258.50 hidden 智能体 @ 7.25-7.50, 185.00-185.75, 252.75-258.50 hidden 多模态 @ 7.25-7.25, 252.75-258.50 hidden DeepSeek @ 7.50-7.50, 252.75-258.50 panels (246, 368, 402, 464) | (30, 40, 500, 500) @ 61.00-61.00, 66.75-66.75 overlap 1997 深蓝胜棋王 | 看图 @ 68.75-68.75 hidden 1997 深蓝胜棋王 @ 69.00-69.00 hidden 听懂说话 @ 69.25-71.00, 97.25-100.00, 113.00-124.50, 126.50-132.50, 142.00-146.00, 152.00-155.00, 160.00-163.00, 169.00-172.00, 189.50-193.00, 201.75-205.25, 220.00-224.25, 252.75-258.50 panels (95, 486, 429, 962) | (-82, 566, 352, 962) @ 70.00-70.00 panels (95, 486, 429, 962) | (43, 566, 477, 962) @ 70.25-70.25 panels (-36, 486, 298, 962) | (45, 566, 479, 962) @ 70.50-70.50 hidden 2011 Siri @ 78.75-82.00 panels (309, 346, 492, 455) | (30, 40, 500, 500) @ 88.00-88.00 panels (361, 487, 891, 553) | (30, 40, 500, 500) @ 97.25-97.25 panels (361, 485, 891, 551) | (30, 40, 500, 500) @ 97.50-97.50 panels (362, 483, 891, 549) | (30, 40, 500, 500) @ 97.75-97.75 panels (3 …（中间省略 1247 字）… -65, 565, 289, 962) @ 129.00-129.00 panels (85, 565, 439, 962) | (82, 565, 436, 962) @ 129.25-129.25 panels (-145, 565, 209, 962) | (85, 565, 439, 962) @ 129.50-129.50 panels (245, 509, 577, 693) | (85, 565, 439, 962) @ 143.25-143.25 panels (411, 572, 966, 868) | (85, 565, 439, 962) @ 172.00-172.00 panels (133, 467, 318, 577) | (30, 40, 500, 500) @ 215.50-215.50 done landscape
思考：Working out pricing details hidden in a landscape listing. R…
Working out pricing details hidden in a landscape listing.
Recalculating overlap zones for the date panel region.
Checking label visibility against overlapping animation elements.
Weighing label placement against colliding timeline tags.
Repositioning labels to dodge polaroid overlap in portrait view.
Adjusting polaroid positions to resolve overlapping panels.运行 cat out/qa-port.txt
cat out/qa-port.txt
hidden 智能体 @ 7.25-7.50, 172.00-172.00, 252.75-258.50 hidden 多模态 @ 7.25-7.25, 252.75-258.50 hidden 看图 @ 7.75-7.75, 103.50-107.25, 113.00-124.50, 252.75-258.50 hidden 2022 一句话出图 @ 7.75-7.75 hidden 画画 · 创作 @ 7.75-7.75, 113.00-124.50, 252.75-258.50 hidden 2020 猜出蛋白质结构 @ 7.75-7.75 hidden 搞科研 @ 7.75-7.75, 113.00-124.50, 252.75-258.50 hidden ChatGPT @ 7.75-7.75, 113.00-124.50, 125.25-126.00, 252.75-258.50 panels (175, 326, 698, 597) | (20, 40, 1060, 356) @ 24.75-24.75 panels (163, 331, 695, 607) | (20, 40, 1060, 356) @ 31.25-31.25 overlap 1966 会找路的机器人 | 机器人 · 开车 @ 35.25-35.75 overlap 1970s 帮医生选药 | 1980 帮公司配电脑 @ 44.75-44.75 hidden 1970s 帮医生选药 @ 45.00-46.00 panels (190, 319, 702, 585) | (20, 40, 1060, 356) @ 49.75-49.75 panels (658, 110, 824, 208) | (20, 40, 1060, 356) @ 61.00-61.00, 66.75-66.75 overlap 机器人 · 开车 | 1997 深蓝胜棋王 @ 63.50-63.50 hidden 机器人 · 开车 @ 63.75-68.50, 113.00-124.50, 125.25-126.00, 126.50-132.50, 141.75-146.00, 150.25-151.00, 152.00-155.00, 160.00-163.00, 168.75-172.00, 185.00-185.75, 189.50-193.00, 201.75-205.25, 220.00-224.25, 252.75-258.50 overlap 1997 听写软件 | 听懂说话 @ 68.50-68.50 hidden 1997 深蓝胜棋王 @ 68.75-72.50 overlap 1998 认手写数字 | 看图 @ 68.75-68.75 panels (699, 350, 1033, 826) | (776, 430, 1210, 826) @ 70.00-70.00 panels (699, 350, 1033, 826) | (651, 430, 1085, 826) @ 70.25-70.25 panels (830, 350, 1164, 826) | (649, 430, 1083, 826) @ 70.50-70.50 panels (620, 127, 814, 239) | (20 …（中间省略 543 字）… 189, 1276) @ 129.00-129.00 panels (685, 879, 1039, 1276) | (688, 879, 1042, 1276) @ 129.25-129.25 panels (915, 879, 1269, 1276) | (685, 879, 1039, 1276) @ 129.50-129.50 panels (408, 466, 762, 656) | (56, 410, 1024, 534) @ 143.25-143.25 panels (620, 231, 816, 344) | (20, 40, 1060, 356) @ 215.50-215.50 hidden 推理模型 @ 252.75-258.50 hidden DeepSeek @ 252.75-258.50 done portrait
思考：Locating a hidden element within the portrait image. Checkin…
Locating a hidden element within the portrait image.
Checking for overlap between stamp placement and nearby labels.
Verifying stamp placement against timing of adjacent labels.
Searching for free space to reposition the polaroid.
Testing smaller polaroid sizes to clear nearby panels.
Shortening photo captions to fit smaller frames legibly.
Mapping out card positions across both portrait layout sections.
Resolving label collisions amid timeline tag transitions.
还有就是各种专业术语底层的关键概念也还是要有的，不然就很外行，我们应该要做到深入浅出？
对，要深入浅出。内行能看到真名词和原理，外行也能看懂大白话。我打算分三层来做：
① 树上的里程碑：真名 + 白话
比如「1966 ELIZA · 第一个聊天程序」「2012 AlexNet · 认图大跃进」「2020 AlphaFold · 猜出蛋白质结构」。之前为了好懂，我把真名删了，这一步改回来。② 词卡分两层：上面一句大白话，下面一行「原理」，旁边配一个会动的小示意图
- 神经网络：神经元 = 输入 × 权重，再加起来；学习 = 调这些权重
- 反向传播：误差从后往前传，每个权重都往"错得更少"的方向挪一点（梯度下降）
- 深度学习：一层认边缘 → 一层认形状 → 一层认出整只猫
- Transformer：注意力机制，每个词都回头看一遍所有词
- 大模型：说穿了只做一件事：预测下一个词。动画演示「今天天气真__」→ 好 62% / 不错 21%…
- 推理模型：思维链，先打草稿再作答
- 智能体：模型 + 工具 + 循环（想 → 做 → 看结果）
- 再补两个底层概念：预训练 + 人类反馈（RLHF），解释 ChatGPT 为什么"听话"；扩散模型，解释一句话出图是怎么从噪点里"去噪"画出来的
③ 旁白：在最关键的几处各加一句讲原理的话
这一层会改动你审过的旁白，视频也会变长，所以先问你一下。横竖屏排版和标签防重叠的问题我正在收尾，不受这个决定影响。
- Claude 问你
第③层「旁白加原理句」怎么处理？
- 画面+旁白都加（推荐）
- 只改画面，旁白不动
画面+旁白都加（推荐）
干了 6 分钟，7 步
读 2 个文件，运行 3 条命令，其他操作 2 次
思考：准备视频配音与画面合成方案。 整合旁白文稿并确认处理方式。 插入各时间节点的补充讲解文字。 核实各技术节点的真实名称与时…
准备视频配音与画面合成方案。
整合旁白文稿并确认处理方式。
插入各时间节点的补充讲解文字。
核实各技术节点的真实名称与时间。
设计年份标签的加粗与分层显示方案。
权衡扩散模型讲解的添加位置与时长。
撰写各技术卡片的浅层与深层说明。
补充动画示意与强化学习表述的措辞。
设计卡片布局与字号排版方案。
调整竖屏画面中卡片与照片的区域安排。
权衡卡片与照片在竖屏中的叠放方式。
设计横屏卡片位置并规避与树形图重叠。
为各知识点设计动态示意图的具体画面。
设计数字对比与推理过程的呼应画面。运行 cd ~/ai-tree && ls -la && ls audio/voice | head -30 && grep -rln "这是…
cd ~/ai-tree && ls -la && ls audio/voice | head -30 && grep -rln "这是一棵树的故事" --include=*.py --include=*.md --include=*.json . 2>/dev/null | head
total 52 drwxr-xr-x 10 root root 4096 Oct 15 02:07 . drwxr-xr-x 10 claude claude 4096 Oct 14 06:46 .. drwxr-xr-x 8 root root 4096 Oct 15 02:07 .git -rw-r--r-- 1 root root 38 Oct 15 02:07 .gitignore drwxr-xr-x 4 root root 4096 Oct 15 02:09 assets drwxr-xr-x 3 root root 4096 Oct 14 09:24 audio drwxr-xr-x 9 root root 4096 Oct 14 06:46 node_modules drwxr-xr-x 6 root root 4096 Oct 15 03:13 out -rw-r--r-- 1 root root 2842 Oct 14 06:46 package-lock.json -rw-r--r-- 1 root root 350 Oct 14 06:46 package.json drwxr-xr-x 2 root root 4096 Oct 15 02:58 scene drwxr-xr-x 2 root root 4096 Oct 14 08:54 script drwxr-xr-x 2 root root 4096 Oct 15 03:13 tools s00.mp3 s01.mp3 s02.mp3 s03.mp3 s04.mp3 s05.mp3 s06.mp3 s07.mp3 s08.mp3 s09.mp3 s10.mp3 s11.mp3 test-xiaoxiao.mp3 test-yunxi.mp3 timing.json ./audio/voice/timing.json ./script/script-v1.md
运行 cat script/script-v1.md | head -150 && python3 - <<'EOF'
cat script/script-v1.md | head -150 && python3 - <<'EOF' import json tm = json.load(open('audio/voice/timing.json')) print(type(tm), list(tm.keys())[:20] if isinstance(tm, dict) else len(tm)) for k in sorted(tm): v = tm[k] if isinstance(v, dict): print(k, v.get('text', '')[:300], v.get('dur')) else: words = ''.join(w[0] if isinstance(w, list) else w.get('text', '') for w in v) print(k, words[:400]) EOF# AI 这棵树 · 旁白脚本 v1（2026-10-14） 总长约 3 分半到 4 分钟。前半段 1950–2023（约 1 分 35 秒），后半段 2024–2026.10（约 1 分 50 秒），开头结尾约 25 秒。 标注：【名词卡】= 手绘名词卡片；【照片】= 拍立得真实照片（需授权）；【插画】= 拍立得里的手绘场景；（待核实）= 上线前再查一次。 ## 开场 画面：问号落在纸上变成种子；手写标题「AI 这棵树 · 1950—2026」 旁白：这是一棵树的故事。它长了七十多年，最近这三年，长疯了。 ## 第一部分：慢慢长大 ### ① 1950–1956 种子 画面：种子发芽，树干底部刻出「人工智能」。【名词卡①人工智能】【照片：图灵】 旁白：1950 年，图灵问：机器能思考吗？六年后，一群科学家在达特茅斯开会，给这件事起了个名字——人工智能。 ### ② 1958–1969 冻住的芽 画面：树干旁冒出嫩芽「感知机」；寒风吹过，芽上结冰。【名词卡②神经网络】【照片：感知机机器】 旁白：1958 年，有人造出了模仿大脑的"感知机"，这是神经网络的第一颗芽。可没过几年，它就被一本书泼了冷水，冻住了。 ### ③ 1960s–1993 写规则，两次寒冬 画面：主干「写规则」段生长；小叶 ELIZA、Shakey；第一次落雪；"当专家"枝条长出又变灰；第二次落雪。【名词卡③专家系统】【照片：Shakey 机器人】 旁白：那时的做法，是把规则一条条写给电脑。说得太多、做到太少，钱被砍了，第一个冬天来了。八十年代，"专家系统"把专家的经验写成"如果……就……"，风光一时；可规则越写越难维护，第二个冬天又来了。 ### ④ 1986–2009 找规律 画面：冻芽滴下一滴水【名词卡④反向传播】；主干换成「找规律」段：读支票、听写软件、谷歌翻译、无人车；下棋枝条：深蓝。【名词卡⑤机器学习】【照片：深蓝主机】【照片：斯坦利无人车】 旁白：后来换了条路：不写规则了，让电脑从大量例子里自己找规律，这叫"机器学习"。1997 年，每秒能算两亿步的深蓝，赢了国际象棋世界冠军。 ### ⑤ 2012–2017 春天 画面：冻芽开花，长成主干「神经网络」段；两块游戏显卡【名词卡⑥深度学习 + GPU】；看图枝条爆叶；AlphaGo【插画：围棋棋盘】【名词卡⑦强化学习】；【名词卡⑧Transformer】 旁白：2012 年，那颗冻了几十年的芽突然开花：很深的神经网络，喂进海量图片，用两块打游戏的显卡来算——这就是"深度学习"。2016 年，AlphaGo 先学人类棋谱，再自己跟自己下，赢了李世石。2017 年，Transformer 诞生，后来 GPT 里的 T，就是它。 ### ⑥ 2020–2023 汇合 画面：各条树枝汇进同一个树冠「大模型」【名词卡⑨大模型 · 规模定律】；ChatGPT 叶子爆开，计数牌"两个月，1 亿人"；画画枝条开花。 旁白：人们发现，模型越大、数据越多，它就越聪明。2022 年底，ChatGPT 上线，据估计两个月就有一亿人在用。各条树枝，开始长进同一个树冠。 ## 第二部分：全世界都疯了 ### 过渡 画面：年份从"年"变成"年·月"；季节尺放大到只剩 2024—2026；纸面开始变满。 旁白：接下来的三年，时间好像变慢了——因为几乎每个月都有大事。 ### 2024 画面（按月弹出日期戳）：02 一行字变电影【插画：纸条投进放映机】· 06 英伟达市值全球第一【插画：卖铲子的小摊长成最高楼】· 07 "9.1 …（中间省略 2622 字）… 网把照片变成动画风，服务器忙到冒烟；AI视频开口说话；奥数考出了金牌水平。十月，每周已有八亿人在用ChatGPT，英伟达成了史上第一家五万亿美元的公司。 None s10 2026年，日子是按天过的。年初，很多人在“养龙虾”——一个能替你回邮件、管日程的AI管家，AI们甚至开了只有AI能发帖的论坛。二月，两行字就能生成好莱坞级的打斗大片；春晚上，机器人从扭秧歌变成了打醉拳、翻跟头。AI会看、会听、会画，还开始有了身体，这叫“多模态”和“具身智能”。夏天，会翻跟头的宇树上市，第一天股价涨了4.6倍。九月，AI自己写代码，一帧一帧画出了整支MV。而每周用ChatGPT的人，已经超过十二亿。 None s11 七十多年，这棵树从写规则到会学习，从会说话到会干活。那些退场的老枝也没有白长，它们变成了导航、垃圾邮件过滤这些看不见的地基。最后说一句：这支视频的每一帧，也是AI写代码画出来的。 None
思考：Drafting a revised narration with integrated principle lines…
Drafting a revised narration with integrated principle lines.
插入反向传播概念于时间线中的位置。
调整反向传播出现的时间线位置。
结合第二次寒冬叙事权衡反向传播的时间锚点。
完善深度学习与注意力机制相关段落的叙述细节。
为2024年AI进展补充技术机制的通俗解释。
合并思维链重复表述并补充扩散模型通俗解释。
核算两部分时长占比并校验历史事实细节。
逐项核实历史事件日期并精简时间线标签文案。
设计卡片视觉布局与动画分区样式。
调整视觉卡片宽度以避免遮挡关键元素。
检查竖版卡片布局与树冠区域的位置关系。
权衡卡片与动态内容在竖版布局中的时序冲突。
重新规划卡片与照片在竖版第一部分的时序安排。
探索竖版照片缩放与树冠标签的避让方案。
根据卡片出现时段动态切换照片在竖版中的位置。
核算横版卡片文字大小与可读性权衡。
设计卡片内动画区域与出场时序方案。
为各概念卡片拟定浅层解释与深层机制文案。
为注意力机制卡片设计词语关联动画方案。
为大模型与扩散模型卡片规划动画呈现方式。读取 build_timeline.py
{ "file_path": "~/ai-tree/tools/build_timeline.py", "limit": 255 }1 """Build the film timeline from the narration timings. 2 3 Inputs : audio/voice/timing.json + audio/voice/sNN.mp3 (synthesized on the user's Mac) 4 Outputs: scene/timeline.js (window.TL = {...}, read by the renderer) 5 audio/narration.wav (all narration segments placed at their start times) 6 """ 7 import json, subprocess, datetime 8 from pathlib import Path 9 10 ROOT = Path(__file__).resolve().parent.parent 11 VOICE = ROOT / "audio" / "voice" 12 TIMING = json.loads((VOICE / "timing.json").read_text()) 13 14 15 def dur(path): 16 return float(subprocess.check_output(["ffprobe", "-v", "error", "-show_entries", "format=duration", "-of", "csv=p=0", str(path)]).decode()) 17 18 19 def Y(iso): # calendar date -> fractional year 20 d = datetime.date.fromisoformat(iso) 21 start = datetime.date(d.year, 1, 1) 22 days = (datetime.date(d.year + 1, 1, 1) - start).days 23 return d.year + (d - start).days / days 24 25 26 # ---------------------------------------------------------------- beats 27 # anchors: (word to match in the narration, calendar year at the moment that word is spoken) 28 BEATS = [ 29 dict(id="intro", seg="s00", pre=0.8, post=4.4, y0=1950.0, y1=1950.0, anchors=[]), 30 dict(id="b1", seg="s01", pre=0.2, post=0.9, y0=1950.0, y1=1957.6, anchors=[("1950", 1950.0), ("六年", 1955.4), ("人工智能", 1956.5)]), 31 dict(id="b2", seg="s02", pre=0.2, post=1.0, y0=1957.6, y1=1970.0, anchors=[("1958", 1958.0), ("十来年", 1966.5), ("冻住", 1969.4)] …（中间省略 11257 字）… yv = mono[-1][1] 243 mono.append((round(tk, 3), round(yv, 5))) 244 245 246 def word_time(beat_id, word): 247 b = next(x for x in beats if x["id"] == beat_id) 248 if word == "@post": 249 return b["te"] + 0.25 250 hit = find_word(b["words"], word) 251 if hit is None: 252 raise SystemExit(f"word '{word}' not found in {beat_id}") 253 return hit["t"] 254 255运行 ls $HOME/mnt/ && ls -la $HOME/mnt/*/ai-tree-video 2>/dev/null | head; ls $HOME/…
ls $HOME/mnt/ && ls -la $HOME/mnt/*/ai-tree-video 2>/dev/null | head; ls $HOME/mnt/ai-tree-video 2>/dev/null | head
Workspace still downloading. This device is downloading the isolated Linux environment, which can take several minutes. Until it finishes, use device_list_dir / device_stage_files / device_commit_files instead.
思考：Checking how text-to-speech ran on the Mac before.
Checking how text-to-speech ran on the Mac before.
查找工具
{ "max_results": 5, "query": "select:mcp__remote-devices__get_device_info,mcp__remote-devices__plugin_8d52a3a6-3a2d-4a54-8d1d-e08585a97642_desktop-commander__start_process,mcp__remote-devices__plugin_8d52a3a6-3a2d-4a54-8d1d-e08585a97642_desktop-commander__read_file,mcp__remote-devices__plugin_8d52a3a6-3a2d-4a54-8d1d-e08585a97642_desktop-commander__read_process_output,mcp__remote-devices__plugin_8d52a3a6-3a2d-4a54-8d1d-e08585a97642_desktop-commander__write_file" }查看你电脑的信息
{}（这一步读取了你电脑的名称和主目录列表，与本次无关，已略去）
读取 tts.py
{ "path": "~/Movies/ai-tree-video/voice/tts.py" }[Reading 44 lines from start (total: 44 lines, 0 remaining)] import asyncio, json, edge_tts VOICE, RATE = "zh-CN-YunxiNeural", "+5%" SEGS = [ ("s00", "这是一棵树的故事。它长了七十多年，最近这三年，突然爆发了。"), ("s01", "1950年，图灵问：机器能思考吗？六年后，一群科学家在达特茅斯开会，给这件事起了个名字——人工智能。"), ("s02", "1958年，有人造出了模仿大脑的“感知机”，这是神经网络的第一颗芽。可十来年后，它被一本书泼了冷水，冻住了。"), ("s03", "那时的做法，是把规则一条条写给电脑。说得太多、做到太少，钱被砍了，第一个冬天来了。八十年代，“专家系统”把专家的经验写成“如果……就……”，风光一时；可规则越写越难维护，第二个冬天又来了。"), ("s04", "后来换了条路：不写规则了，让电脑从大量例子里自己找规律，这叫“机器学习”。1997年，每秒能算两亿步的深蓝，赢了国际象棋世界冠军。"), ("s05", "2012年，那颗冻了几十年的芽突然开花：很深的神经网络，喂进海量图片，用两块打游戏的显卡来算——这就是“深度学习”。2016年，AlphaGo先学人类棋谱，再自己跟自己下，赢了李世石。2017年，Transformer诞生，后来GPT里的T，就是它。"), ("s06", "人们发现，模型越大、数据越多，它就越聪明。2022年底，ChatGPT上线，据估计两个月就有一亿人在用。各条树枝，开始长进同一个树冠。"), ("s07", "如果把这七十多年压缩成一天，最近这三年，只是最后的五十多分钟。可接下来，我要用一半的时间来讲它——因为几乎每个星期，都有大事。"), ("s08", "2024年，打一行字就能生成电影般的视频；卖AI芯片的英伟达，一度成了全世界最值钱的公司。夏天，AI还会把9.11和9.9比错；九月，它学会了先想一想再回答。十月，两个诺贝尔奖颁给了AI研究，其中一位得主，就是几十年来一直坚持神经网络的辛顿。"), ("s09", "2025年一开年，中国的DeepSeek免费又开源，一周就冲上美国下载榜第一，英伟达一天蒸发近六千亿美元。除夕夜，机器人在春晚扭起了秧歌。AI不只会聊天了，开始自己动手写程序、办事情，这叫“智能体”。全网把照片变成动画风，服务器忙到冒烟；AI视频开口说话；奥数考出了金牌水平。十月，每周已有八亿人在用ChatGPT，英伟达成了史上第一家五万亿美元的公司。"), ("s10", "2026年，日子是按天过的。年初，很多人在“养龙虾”——一个能替你回邮件、管日程的AI管家，AI们甚至开了只有AI能发帖的论坛。二月，两行字就能生成好莱坞级的打斗大片；春晚上，机器人从扭秧歌变成了打醉拳、翻跟头。AI会看、会听、会画，还开始有了身体，这叫“多模态”和“具身智能”。夏天，会翻跟头的宇树上市，第一天股价涨了4.6倍。九月，AI自己写代码，一帧一帧画出了整支MV。而每周用ChatGPT的人，已经超过十二亿。"), ("s11", "七十多年，这棵树从写规则到会学习，从会说话到会干活。那些退场的老枝也没有白长，它们变成了导航、垃圾邮件过滤这些看不见的地基。最后说一句：这支视频的每一帧，也是AI写代码画出来的。"), ] async def synth(sid, text): words = [] …（中间省略 782 字）… id, text)} break except Exception as e: print(sid, "retry", attempt + 1, type(e).__name__) await asyncio.sleep(2 + attempt * 3) else: print(sid, "FAILED"); continue print(sid, len(out[sid]["words"]), "boundaries") json.dump(out, open("timing.json", "w"), ensure_ascii=False, indent=1) asyncio.run(main())思考：Updating selected voice segments and backing up prior audio …
Updating selected voice segments and backing up prior audio files.
Refining narration text and syncing visual cues with timing.
为深度学习段落添加年份与视觉同步锚点。
为大模型与ChatGPT段落编排叙事顺序与年份锚点。
为扩散模型与思维链段落梳理年份与叙事节奏。
为诺贝尔奖与思维链段落设置触发词与年份锚点。
为智能体与多模态段落设置触发词与年份锚点。
为卡片触发与标注设置时间锁定规则。
原理句放进旁白后的样子如下，加粗的是新加的部分。只有这 6 段有改动，其他段保持你审过的原样：
s02 · 1958 1958年，有人造出了模仿大脑的"感知机"，这是神经网络的第一颗芽。每个"神经元"把输入乘上权重、加起来；所谓学习，就是不断调这些权重。可十来年后，它被一本书泼了冷水，冻住了。
s03 · 八十年代 ……"专家系统"把专家的经验写成"如果……就……"，风光一时。1986年，冻住的那颗芽也开始化冻："反向传播"出现了——答错了，就把误差从后往前传，算出每个权重该往哪边拧一点。而专家系统的规则越写越难维护，第二个冬天又来了。
（画面上，冰化成水的那一刻正好落在 1986 这句。）s05 · 2012–2017 ……这就是"深度学习"。前几层学会认边缘，中间几层认形状，最后几层认出整只猫。2016年，AlphaGo……2017年，Transformer诞生，后来GPT里的T，就是它。它的核心叫"注意力"：读每个词时，都回头看一遍句子里所有的词。
s06 · 2020–2022 人们发现，模型越大、数据越多，它就越聪明。说穿了，大模型只做一件事：根据前文，预测下一个词。可光会接话还不够，再用人类的反馈去调教，它才从"续写机器"变成了聊天助手。2022年底，ChatGPT上线……
s08 · 2024 2024年，打一行字就能生成电影般的视频，背后是"扩散模型"：从一片噪点开始，一步步"去噪"，把画面擦出来。卖AI芯片的英伟达……九月，它学会了先打一长段草稿，也就是"思维链"，想清楚了再回答。……
s09 · 2025 ……这叫"智能体"：给大模型配上工具，让它自己循环——想一步、做一步、看看结果。全网把照片变成动画风……
"扩散模型"这句是新加的，不在你刚才看到的 8 句里，对应我计划里补的那个概念。连接处我也补了几个字，比如"可光会接话还不够""而专家系统的规则"。我先做画面部分（词卡、示意动画、树上标签），最后再合成语音。哪句想改，直接告诉我就行，改一句只需要重新合成那一段。
干了 88 分钟，156 步
读 47 个文件，运行 75 条命令，写 7 个文件，派出 1 个子任务，其他操作 26 次
思考：Planning narration updates and a timeline rebuild task.
Planning narration updates and a timeline rebuild task.
新建待办
{ "activeForm": "Re-recording narration with principle lines", "description": "s02/s03/s05/s06/s08/s09 加入 9 句原理旁白（神经元权重、反向传播、层层抽象、注意力、预测下一个词、人类反馈、扩散模型、思维链、智能体循环），备份旧音频，edge-tts 重新合成，重建时间轴（新锚点、词卡触发词与原理句时刻）。", "subject": "给 6 段旁白加原理句，在你电脑上重新合成" }Task #14 created successfully: 给 6 段旁白加原理句，在你电脑上重新合成
新建待办
{ "activeForm": "Restoring real names on tree tags", "description": "ELIZA、Shakey、MYCIN、XCON、深蓝、Dragon、LeNet、Stanley、谷歌翻译、Watson、Siri、AlexNet、GAN、AlphaGo、GNMT、AlphaFold、扩散模型：年份和真名加粗，后接白话；逐条核对年份。", "subject": "树上标签改成「真名 + 白话」" }Task #15 created successfully: 树上标签改成「真名 + 白话」
新建待办
{ "activeForm": "Building two-layer term cards with mechanism animations", "description": "14 张词卡（新增人类反馈 RLHF、扩散模型）：名字/英文/年份，一行大白话，一行「原理」，左侧机制小动画（权重旋钮、误差回传、层层抽象、注意力连线、下一个词概率条、点赞调教、噪点去噪、草稿思维链、想-做-看循环等）。原理行在旁白说到时亮起。横竖屏各自的尺寸和位置。", "subject": "词卡改成两层（白话 + 原理）并配会动的示意图" }Task #16 created successfully: 词卡改成两层（白话 + 原理）并配会动的示意图
新建待办
{ "activeForm": "Fixing layout collisions in both orientations", "description": "标签按权重自动避让（新里程碑优先、旧的淡出），浮层下的标签淡出；用 qa_layout.py 扫描横竖屏全片，修掉重叠、出界和浮层互相遮挡；出对比截图自查。", "subject": "横竖屏排版：标签防重叠，扫描全片无冲突" }Task #17 created successfully: 横竖屏排版：标签防重叠，扫描全片无冲突
新建待办
{ "activeForm": "Composing music and sound effects", "description": "按 tempo 段落生成配乐（冬天稀疏、开花提亮、时钟段、越来越快的驱动段、柔和结尾），加音效（便签、快门、盖章、风雪、开花、翻页、倒带），旁白下自动压低音乐，响度 -16 LUFS。", "subject": "配乐与音效 v2" }Task #18 created successfully: 配乐与音效 v2
思考：Planning task dependencies and starting work on tagging. Ver…
Planning task dependencies and starting work on tagging.
Verifying historical dates and formatting bold tag styling.更新待办
{ "addBlockedBy": [ "14", "15", "16", "17", "18" ], "taskId": "13" }Updated task #13 blockedBy
更新待办
{ "status": "in_progress", "taskId": "15" }Updated task #15 status
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() pairs = [ ("tag: '1966 第一个聊天程序'", "tag: '1966 ELIZA · 第一个聊天程序'"), ("tag: '2006 谷歌翻译'", "tag: '2006 谷歌翻译 · 按统计规律翻译'"), ("tag: '2016 翻译变聪明'", "tag: '2016 GNMT · 神经网络翻译'"), ("tag: '2011 问答节目夺冠'", "tag: '2011 Watson · 问答节目夺冠'"), ("tag: '1966 会找路的机器人'", "tag: '1966 Shakey · 会自己找路'"), ("tag: '2005 无人车跑完沙漠'", "tag: '2005 Stanley · 无人车跑完沙漠'"), ("tag: '1970s 帮医生选药'", "tag: '1970s MYCIN · 帮医生选药'"), ("tag: '1980 帮公司配电脑'", "tag: '1980 XCON · 帮公司配电脑'"), ("tag: '1997 深蓝胜棋王'", "tag: '1997 深蓝 · 胜棋王卡斯帕罗夫'"), ("tag: '2016 AlphaGo 胜李世石'", "tag: '2016 AlphaGo · 胜李世石'"), ("tag: '1997 听写软件'", "tag: '1997 Dragon · 听写软件'"), ("tag: '2011 Siri'", "tag: '2011 Siri · 语音助手'"), ("tag: '1998 认手写数字'", "tag: '1998 LeNet · 认手写数字'"), ("tag: '2012 认图大跃进'", "tag: '2012 AlexNet · 认图大跃进'"), ("tag: '2014 学着画图'", "tag: '2014 GAN · 两个网络对练画画'"), ("tag: '2022 一句话出图'", "tag: '2022 扩散模型 · 一句话出图'"), ("tag: '2020 猜出蛋白质结构'", "tag: '2020 AlphaFold · 猜出蛋白质结构'"), ] for a, b in pairs: assert s.count(a) == 1, a s = s.replace(a, b) p.write_text(s) l …（中间省略 442 字）… \d)*)\\s/.exec(str); const parts = dot > 0 ? [[str.slice(0, dot), 700], [str.slice(dot), weight]] : m ? [[m[0], 700], [str.slice(m[0].length), weight]] : [[str, weight]];""" assert t.count(old) == 1 t = t.replace(old, new) l.write_text(t) print('ok') PYEOF grep -n "tag: '" scene/film.js | head -30ok 119: nodes: [{ y: 1966, t: 0.3, tag: '1966 ELIZA · 第一个聊天程序', tw: [6, 34] }, { y: 2006.3, t: 0.62, tag: '2006 谷歌翻译 · 按统计规律翻译', tw: [4, 36] }, { y: 2016.8, t: 1, tag: '2016 GNMT · 神经网络翻译' }], 120: twigs: [{ y: 2011.1, t: 0.8, ang: 1.2, len: 44, tag: '2011 Watson · 问答节目夺冠', wither: 2022.0, below: true }] }), 122: nodes: [{ y: 1966.9, t: 0.35, tag: '1966 Shakey · 会自己找路' }, { y: 2005.8, t: 1, tag: '2005 Stanley · 无人车跑完沙漠' }] }), 124: nodes: [{ y: 1974, t: 0.45, tag: '1970s MYCIN · 帮医生选药', tw: [0, 30] }, { y: 1980.3, t: 1, tag: '1980 XCON · 帮公司配电脑' }, { y: 1983, t: 0.7, tw: [-6, 28] }, { y: 1985, t: 0.85, tw: [6, 26] }] }), 126: nodes: [{ y: 1997.3, t: 0.5, tag: '1997 深蓝 · 胜棋王卡斯帕罗夫' }, { y: 2016.19, t: 1, tag: '2016 AlphaGo · 胜李世石' }] }), 128: nodes: [{ y: 1997.5, t: 0.5, tag: '1997 Dragon · 听写软件' }, { y: 2011.8, t: 1, tag: '2011 Siri · 语音助手' }] }), 130: nodes: [{ y: 1998.5, t: 0.45, tag: '1998 LeNet · 认手写数字' }, { y: 2012.75, t: 1, tag: '2012 AlexNet · 认图大跃进' }] }), 132: nodes: [{ y: 2014.4, t: 0.4, tag: '2014 GAN · 两个网络对练画画' }, { y: 2022.6, t: 1, tag: '2022 扩散模型 · 一句话出图' }] }), 134: nodes: [{ y: 2020.9, t: 1, tag: '2020 AlphaFold · 猜出蛋白质结构' }] }), 150: { y: 2022.91, tag: 'ChatGPT', o: [-82, -6] }, { y: 2024.70, tag: '推理模型', o: [88, -6] }, 151: { y: 2025.053, tag: 'DeepSeek', o: [0, -62] }, { y: 2025.15, tag: '智能体', o: [-66, 52] }, { y: 2026.30, tag: '多模态', o: [70, 52] },思考：核实谷歌翻译相关说法的准确性。
核实谷歌翻译相关说法的准确性。
读取 art.js
{ "file_path": "~/ai-tree/scene/art.js", "limit": 84 }1 // Doodles: note icons, term-card pictures, polaroid illustrations and photo fallbacks. 2 const INK = { stroke: C.ink, strokeWidth: 1.6, roughness: 0.8 }; 3 const o = (seed, extra = {}) => Object.assign({}, INK, { seed: sd(seed) }, extra); 4 5 // ---------------------------------------------------------------- tiny note icons (≈ 34px box centred on x,y) 6 const ICONS = { 7 film: (x, y, s) => { rc.rectangle(x - 16, y - 11, 32, 22, o(s, { fill: '#6d6d6d', fillStyle: 'hachure', hachureGap: 4 })); 8 for (let i = 0; i < 4; i++) { rc.rectangle(x - 13 + i * 8, y - 9, 4, 3, o(s + i + 1, { strokeWidth: 1 })); } }, 9 talk: (x, y, s) => { rc.path(`M ${x-15} ${y-11} L ${x+15} ${y-11} L ${x+15} ${y+6} L ${x} ${y+6} L ${x-8} ${y+14} L ${x-6} ${y+6} L ${x-15} ${y+6} Z`, o(s, { fill: '#9fc6dc', fillStyle: 'hachure', hachureGap: 4 })); }, 10 chip: (x, y, s) => { rc.rectangle(x - 11, y - 11, 22, 22, o(s, { fill: '#7fae5a', fillStyle: 'hachure', hachureGap: 3 })); 11 for (let i = -1; i <= 1; i++) { rc.line(x + i * 6, y - 11, x + i * 6, y - 16, o(s + 2 + i)); rc.line(x + i * 6, y + 11, x + i * 6, y + 16, o(s + 5 + i)); } }, 12 car: (x, y, s) => { rc.path(`M ${x-17} ${y+4} L ${x-17} ${y-3} L ${x-9} ${y-5} L ${x-4} ${y-13} L ${x+8} ${y-13} L ${x+12} ${y-5} L ${x+17} ${y-3} L ${x+17} ${y+4} Z`, o(s, { fill: '#e9a96a', fillStyle: 'hachure', hachureGap: 4 })); 13 rc.circle(x - 9, y + 6, 9, o(s + 1, { fill: C.ink, fillStyle: 'solid' })); rc.circle(x + 9, y + 6, 9, o(s + 2, { fil …（中间省略 9805 字）… 83 [[2, 2, 1], [6, 2, 0], [2, 6, 0], [6, 6, 1], [3, 3, 1], [5, 3, 0], [4, 5, 1], [5, 5, 0], [3, 6, 1], [6, 4, 1], [7, 5, 0], [4, 2, 0], [4, 4, 1]].forEach(([cx, cy, b], i) => 84 rc.circle(-80 + cx * 20, -80 + cy * 20, 17, b ? o(s + 40 + i, { fill: '#2b2420', fillStyle: 'solid', roughness: 0.5 }) : o(s + 40 + i, { fill: '#fffdf6', fillStyle: 'solid', strokeWidth: 1.2, roughness: 0.5 })));思考：Building animated mech sequences in a separate scene file. S…
Building animated mech sequences in a separate scene file.
Sketching neuron diagrams and expert-system rule cards.
Designing backprop, classification, and layered recognition animations.
设计注意力、语言模型和强化学习反馈动画。
设计扩散、推理、智能体和具身智能动画卡片。
为浅层文字排版规划行宽与分行。
为深层解释拟定逐条简明原理文案。
计算人像卡与宝丽来照片的布局与避让间距。
排查印章与卡片在横屏布局中的时间冲突。
调整印章位置以避开人像卡与科研标签。
检查皇冠标签与印章叠放的时机取舍。
为卡片动画机制编写辅助绘图函数。
为“人工智能”机制卡绘制机器人思考动画。
为神经网络卡设计可调权重连线动画。
为神经元信号点动画调整触发与求和效果。
根据触发词匹配规划各机制卡出现与讲解时机。
计算卡片出现与停留时间的触发逻辑。写入 mech.js
// Mechanism animations for the term cards: each one shows the idea *behind* a term, in a 220×220 box centred on 0,0. // u = seconds since the card appeared // ud = seconds since the narrator started explaining the principle (negative before that) const cyc = (u, P) => ((u % P) + P) % P; const mt = (s, x, y, size, opt = {}) => text(s, x, y, { size, align: 'center', yearBold: false, ...opt }); function mDot(x, y, r, color) { ctx.fillStyle = color; ctx.beginPath(); ctx.arc(x, y, r, 0, 6.283); ctx.fill(); } function mArrow(x0, y0, x1, y1, seed, color = C.ink, w = 2) { rc.line(x0, y0, x1, y1, o(seed, { stroke: color, strokeWidth: w })); const a = Math.atan2(y1 - y0, x1 - x0), L = 11; rc.linearPath([[x1 - L * Math.cos(a - 0.5), y1 - L * Math.sin(a - 0.5)], [x1, y1], [x1 - L * Math.cos(a + 0.5), y1 - L * Math.sin(a + 0.5)]], o(seed + 1, { stroke: color, strokeWidth: w })); } function mCheck(x, y, k, seed) { if (k <= 0) return; ctx.save(); ctx.translate(x, y); ctx.scale(k, k); rc.linearPath([[-12, 0], [-4, 9], [13, -11]], o(seed, { stroke: '#3f8f3a', strokeWidth: 4 })); ctx.restore(); } function mCross(x, …（中间省略 19118 字）… x.save(); ctx.translate(92, 8); ctx.scale(1.7, 1.7); ICONS.robot(0, 0, s + 32); ctx.restore(); const arm = -0.6 + Math.sin(u * 4) * 0.7; rc.line(108, 14, 108 + Math.cos(arm) * 22, 14 + Math.sin(arm) * 22, o(s + 40, { strokeWidth: 3 })); mt('还能动手', 92, 104, 20, { color: C.inkSoft }); }, };File created successfully at: ~/ai-tree/scene/mech.js (file state is current in your context — no need to Read it back)
思考：Tracking down a diffusion opacity bug in the code. Reworking…
Tracking down a diffusion opacity bug in the code.
Reworking the alpha fix with a save and restore block.
Verifying layout and positioning details across multiple visualization panels.修改 mech.js
{ "file_path": "~/ai-tree/scene/mech.js", "new_string": " ctx.save(); ctx.beginPath(); ctx.rect(-80, -86, 160, 150); ctx.clip();\n ctx.fillStyle = '#fffdf6'; ctx.fillRect(-80, -86, 160, 150);\n ctx.save(); ctx.globalAlpha *= p; // the picture emerges as the noise goes away\n ctx.fillStyle = '#cfe6f5'; ctx.fillRect(-80, -86, 160, 150);\n ctx.fillStyle = '#f2b84b'; ctx.beginPath(); ctx.arc(38, -46, 20, 0, 6.283); ctx.fill();\n ctx.fillStyle = '#7fae5a'; ctx.beginPath(); ctx.moveTo(-80, 64); ctx.lineTo(-30, -6); ctx.lineTo(14, 40); ctx.lineTo(40, 14); ctx.lineTo(80, 64); ctx.closePath(); ctx.fill();\n ctx.restore();\n const R = rng(s + Math.floor(u * 8)), n = Math.round(900 * (1 - p));", "old_string": " ctx.save(); ctx.beginPath(); ctx.rect(-80, -86, 160, 150); ctx.clip();\n ctx.fillStyle = '#fffdf6'; ctx.fillRect(-80, -86, 160, 150);\n ctx.globalAlpha *= p;\n ctx.fillStyle = '#cfe6f5'; ctx.fillRect(-80, -86, 160, 150);\n ctx.fillStyle = '#f2b84b'; ctx.beginPath(); ctx.arc(38, -46, 20, 0, 6.283); ctx.fill();\n ctx.fillStyle = '#7fae5a'; ctx.beginPath(); ctx.moveTo(-80, 64); ctx.lineTo(-30, -6); ctx.lineTo(14, 40); ctx.lineTo(40, 14); ctx.lineTo(80, 64); ctx.closePath(); ctx.fill();\n ctx.globalAlpha /= Math.max(p, 1e-3);\n const R = rng(s + Math.floor(u * 8)), n = Math.round(900 * (1 - p));", "replace_all": false }The file ~/ai-tree/scene/mech.js has been updated successfully. (file state is current in your context — no need to Read it back)
运行 cat scene/film.html && sed -i 's#<script src="art.js"></script>#<script src="ar…
cat scene/film.html && sed -i 's#<script src="art.js"></script>#<script src="art.js"></script>\n<script src="mech.js"></script>#' scene/film.html && grep -n "script" scene/film.html
<!doctype html> <html><head><meta charset="utf-8"> <link rel="stylesheet" href="../node_modules/lxgw-wenkai-webfont/lxgwwenkai-regular.css"> <link rel="stylesheet" href="../node_modules/lxgw-wenkai-webfont/lxgwwenkai-bold.css"> <link rel="stylesheet" href="../node_modules/@fontsource/ma-shan-zheng/index.css"> <style>html,body{margin:0;background:#000}canvas{display:block}</style> </head><body> <canvas id="c" width="1920" height="1080"></canvas> <script src="../node_modules/roughjs/bundled/rough.js"></script> <script src="lib.js"></script> <script src="art.js"></script> <script src="timeline.js"></script> <script src="film.js"></script> </body></html> 9:<script src="../node_modules/roughjs/bundled/rough.js"></script> 10:<script src="lib.js"></script> 11:<script src="art.js"></script> 12:<script src="mech.js"></script> 13:<script src="timeline.js"></script> 14:<script src="film.js"></script>思考：Building out film terminology cards and layout structure. 撰写…
Building out film terminology cards and layout structure.
撰写术语卡片的具体内容与说明文字。
调整名称与英文缩写的排版宽度。
重新规划卡片高度与年份标签的位置布局。
调整横版卡片的宽度与文字换行方案。
排查卡片位置与树枝元素的重叠冲突。
调整树枝末端位置以避免与卡片重叠。
细化横版与竖版卡片的具体排版尺寸。
编写文字换行与高亮显示的辅助函数。
计算卡片几何尺寸与名称字号的函数。
确定卡片内垂直布局与整体高度计算方式。
核对视觉与其他分支节点的坐标偏移。运行 grep -n "const TERMS" -A 14 scene/film.js | head -16 && grep -n "card: {" scene…
grep -n "const TERMS" -A 14 scene/film.js | head -16 && grep -n "card: {" scene/film.js && grep -n "function cardHold\|function learnedCount" scene/film.js && grep -n "^function drawCards" -A 30 scene/film.js | head -40161:const TERMS = { 162- ai: { name: '人工智能', en: 'AI', year: '1956', say: '让机器像人一样\n思考、学习' }, 163- nn: { name: '神经网络', year: '1958', say: '模仿大脑神经元连接的\n一张“开关网”' }, 164- expert: { name: '专家系统', year: '1980s', say: '把专家的经验，写成\n“如果…就…”的规则' }, 165- backprop: { name: '反向传播', year: '1986', say: '答错了，就倒回去\n一层层改。神经网络\n从此会学习' }, 166- ml: { name: '机器学习', year: '1990s', say: '不写规则，让电脑\n从大量例子里\n自己找规律' }, 167- dl: { name: '深度学习', en: '+ GPU', year: '2012', say: '层数很多的神经网络，\n用打游戏的显卡来算' }, 168- rl: { name: '强化学习', year: '2016', say: '自己跟自己练，\n做对了就给奖励' }, 169- transformer: { name: 'Transformer', year: '2017', say: '一次看完整句话，\n找出词和词之间的关系' }, 170- llm: { name: '大模型', en: '规模定律', year: '2020', say: '模型越大、数据越多，\n就越聪明' }, 171- reason: { name: '推理模型', year: '2024', say: '先打草稿，\n想一想再回答' }, 172- agent: { name: '智能体', en: 'Agent', year: '2025', say: '不光会说，\n还能自己动手干活' }, 173- embodied: { name: '多模态 · 具身智能', year: '2026', say: '能看、能听、能画，\n还有了“身体”' }, 174-}; 175-const POLA = { 53: card: { w: 580, h: 290, p1: [1300, 22], p2: [450, 600] }, 80: card: { w: 620, h: 300, p1: [30, 410], p2: [40, 900] }, 661:function cardHold(i) { const c = TL.cards[i], next = TL.cards[i + 1]; return Math.min(4.3, next ? next.t - c.t - 0.2 : 4.3); } 662:function learnedCount(t) { return TL.cards.filter((c, i) => t > c.t + cardHold(i) + 0.6).length; } 729:function drawCards(t) { 730- cardStates(t).forEach(({ c, i, w, h, tx, ty, s, rot, a }) => { 731- const T = TERMS[c.id]; 732- ctx.save(); ctx.globalAlpha = a; …（中间省略 1424 字）… [kind, id] = p.id.split(':'); 755- const photo = kind === 'photo' && PHOTOS[id]; 756- const [pw, ph] = photo ? PHOTO_FRAME[id] : [290, 261]; 757- const fw = pw + 36, fh = ph + 18 + 84; 758- const inK = easeOut((t - t0) / 0.5), outK = easeInOut((t - t1) / 0.5); 759- const P = t0 > PART2 && L.pola2 ? L.pola2 : L.pola, side = P.cx > W / 2 ? 1 : -1; // slides in from the nearest edge运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old) assert n == 1, (n, old[:90]) s = s.replace(old, new) # ---- term table: plain line + principle line (+ short English for narrow layouts) start = s.index("const TERMS = {"); end = s.index("};", start) + 2 s = s[:start] + """const TERMS = { // say = 大白话 (the plain line), deep = 原理 (the key mechanism underneath) ai: { name: '人工智能', en: 'Artificial Intelligence', enS: 'AI', year: '1956', label: '来历', say: '让机器像人一样思考、学习', deep: '1956 年达特茅斯会议上，第一次起了这个名字' }, nn: { name: '神经网络', en: 'Neural Network', year: '1958', say: '模仿大脑神经元连接的一张“开关网”', deep: '输入 × 权重，加起来；学习 = 不断调这些权重' }, expert: { name: '专家系统', en: 'Expert System', year: '1980s', say: '把专家的经验，写成“如果…就…”的规则', deep: '知识库（成千上万条规则）+ 推理机，自动把规则串起来' }, backprop: { name: '反向传播', en: 'Backpropagation', enS: 'Backprop', year: '1986', say: '答错了，就倒回去一层层改', deep: '误差从后往前传，每个权重都朝“错得更少”挪一点（梯度下降）' }, ml: { name: '机器学习', en: 'Machine Learning', enS: 'ML', year: '1990s', say: '不写规则，让电脑从大量例子里自己找规律', deep: '用数据“训练”出模型，再拿去 …（中间省略 10413 字）… ); ctx.scale(ps, ps); ctx.globalAlpha = a;""") p.write_text(s) print("ok") PYEOF node -e "require('fs'); new Function(require('fs').readFileSync('~/ai-tree/scene/film.js','utf8'))" && echo syntax-ok; node -e "new Function(require('fs').readFileSync('~/ai-tree/scene/mech.js','utf8'))" && echo mech-okok syntax-ok mech-ok
思考：Building a standalone preview tool for reviewing cards.
Building a standalone preview tool for reviewing cards.
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:80]); s = s.replace(old, new) rep("""function drawCards(t) { const F = L.cardF; cardStates(t).forEach(({ c, i, G, w, h, tx, ty, s, rot, a }) => { const T = TERMS[c.id], u = t - c.t, ud = t - (c.td === undefined ? c.t + 1.2 : c.td);""", """function drawCards(t) { cardStates(t).forEach(st => drawOneCard(st, t)); } function drawOneCard({ c, i, G, w, h, tx, ty, s, rot, a }, t) { const F = L.cardF, T = TERMS[c.id], u = t - c.t, ud = t - (c.td === undefined ? c.t + 1.2 : c.td);""") rep(""" text(ln, lx, G.yDeep[k], { size: F.deep, color: C.ink, yearBold: false }); ctx.restore(); }); } ctx.restore(); }); }""", """ text(ln, lx, G.yDeep[k], { size: F.deep, color: C.ink, yearBold: false }); ctx.restore(); }); } ctx.restore(); }""") rep(""" // QA hook: label boxes""", """ // QA hook: lay out term cards on a blank page at internal time u (seconds since the card appeared) window.car …（中间省略 1657 字）… aluate(f"cardPreview({ids[k:k + per]}, {u})") page.screenshot(path=str(out / f"cards-{'p' if portrait else 'l'}-{u:04.1f}-{k // per}.png")) b.close() print("errors:", errs or "none") EOF python3 tools/cards.py 2.5 && python3 tools/cards.py --portrait 2.5 && ls out/frames | grep cardsok errors: none errors: none cards-l-02.5-0.png cards-l-02.5-1.png cards-l-02.5-2.png cards-p-02.5-0.png cards-p-02.5-1.png cards-p-02.5-2.png cards-p-02.5-3.png
读取 cards-l-02.5-0.png
{ "file_path": "~/ai-tree/out/frames/cards-l-02.5-0.png" }[图片]
思考：检查卡片中的断行与排版问题。 改用词边界分词避免拆散常用词组。
检查卡片中的断行与排版问题。
改用词边界分词避免拆散常用词组。读取 cards-l-02.5-1.png
{ "file_path": "~/ai-tree/out/frames/cards-l-02.5-1.png" }[图片]
读取 cards-l-02.5-2.png
{ "file_path": "~/ai-tree/out/frames/cards-l-02.5-2.png" }[图片]
读取 cards-p-02.5-0.png
{ "file_path": "~/ai-tree/out/frames/cards-p-02.5-0.png" }[图片]
思考：检查卡片在横屏模式下的显示问题。 重新设计年份标签与动画区域的布局结构。 重新打磨较长说明文字的换行与措辞。 重新计算横…
检查卡片在横屏模式下的显示问题。
重新设计年份标签与动画区域的布局结构。
重新打磨较长说明文字的换行与措辞。
重新计算横屏文字可用宽度与换行上限。
逐条反复调整反向传播说明的字数与断行。
加宽横屏卡片以容纳更多文字内容。
逐字核对卡片文字是否超出可用宽度。
逐条核对各概念说明的字数与分行效果。
逐项核对精简版文案的断行与字数上限。运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:80]); s = s.replace(old, new) start = s.index("const TERMS = {"); end = s.index("};", start) + 2 s = s[:start] + """const TERMS = { // say = 大白话 (the plain line), deep = 原理 (the key mechanism underneath); '|' marks a preferred line break ai: { name: '人工智能', en: 'Artificial Intelligence', enS: 'AI', year: '1956', label: '来历', say: '让机器像人一样思考、学习', deep: '1956 年达特茅斯会议上，|第一次起了这个名字' }, nn: { name: '神经网络', en: 'Neural Network', year: '1958', say: '模仿大脑神经元连接的|一张“开关网”', deep: '输入 × 权重，加起来；|学习 = 不断调这些权重' }, expert: { name: '专家系统', en: 'Expert System', year: '1980s', say: '把专家的经验，|写成“如果…就…”的规则', deep: '知识库：成千上万条规则；|推理机：自动把规则串起来' }, backprop: { name: '反向传播', en: 'Backpropagation', enS: 'Backprop', year: '1986', say: '答错了，就倒回去一层层改', deep: '误差从后往前传，每个权重|朝错得更少的方向挪：梯度下降' }, ml: { name: '机器学习', en: 'Machine Learning', enS: 'ML', year: '1990s', say: '不写规则，|让电脑从大量例子里自己找规律', deep: '用数据“训练”出模型，|再拿去判断没见过的新例子' }, dl: { name: '深度学习', en: 'Deep Learnin …（中间省略 6048 字）… C.inkSoft });", " mt('还能动手', 84, 102, 20, { color: C.inkSoft });") m.write_text(t) print('ok') PYEOF node -e "new Function(require('fs').readFileSync('~/ai-tree/scene/film.js','utf8'))" && echo ok2 && rm -f out/frames/cards-* && python3 tools/cards.py 2.5 && python3 tools/cards.py --portrait 2.5ok ok2 errors: none errors: none
读取 cards-l-02.5-0.png
{ "file_path": "~/ai-tree/out/frames/cards-l-02.5-0.png" }[图片]
读取 cards-l-02.5-1.png
{ "file_path": "~/ai-tree/out/frames/cards-l-02.5-1.png" }[图片]
读取 cards-p-02.5-2.png
{ "file_path": "~/ai-tree/out/frames/cards-p-02.5-2.png" }[图片]
思考：Fixing clipped label text in diagram layouts. Adjusting coor…
Fixing clipped label text in diagram layouts.
Adjusting coordinate shifts to fix MECH overflow.运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path m = Path('scene/mech.js'); t = m.read_text() def mrep(old, new): global t n = t.count(old); assert n == 1, (n, old[:80]); t = t.replace(old, new) # rlhf: everything up 10px so the caption fits mrep(""" [[-50, '#e3f1d4'], [10, '#fde2dc']].forEach(([y, fill], i) => {""", """ [[-62, '#e3f1d4'], [-2, '#fde2dc']].forEach(([y, fill], i) => {""") mrep(""" mCheck(58, -56, backOut(clamp((k - 0.4) / 0.35)) * 1.4, s + 20); mCross(58, 4, backOut(clamp((k - 0.9) / 0.35)) * 1.3, s + 22);""", """ mCheck(58, -68, backOut(clamp((k - 0.4) / 0.35)) * 1.4, s + 20); mCross(58, -8, backOut(clamp((k - 0.9) / 0.35)) * 1.3, s + 22);""") mrep(""" rc.rectangle(-60, 54, 120, 44, o(s + 30, { fill: mk > 0.9 ? '#fff0a8' : '#fffdf6', fillStyle: 'solid', stroke: mk > 0.9 ? C.red : C.inkSoft })); mt('模型', 0, 84, 22, { weight: 700 }); if (mk > 0) { mArrow(80, -26, 66, 58, s + 40, C.red, 2.2); mt('学着往 A 靠', 0, 116, 20, { color: C.red, weight: 700, alpha: mk }); }""", """ rc.rectangle(-60, 40, 120, 42, o(s + 30, { fill: mk > 0.9 ? '#fff0a8' : '#fffdf6', fillS …（中间省略 1313 字）… eight: 700, color: C.red });\n mt('提示：山、太阳', 0, 118, 18, { color: C.inkSoft });", " mt(`去噪 第 ${step} 步 / 50`, 0, 80, 22, { weight: 700, color: C.red });\n mt('提示词：山和太阳', 0, 106, 20, { color: C.inkSoft });") m.write_text(t); print('ok') PYEOF grep -n "rect(-80, -86" scene/mech.js | headok
思考：Updating the timeline build script with new anchors. Syncing…
Updating the timeline build script with new anchors.
Syncing year markers with the rewritten narration text.
Verifying anchor timing across remaining beats and cards.
Scripting card timing and preparing narration for re-synthesis.运行 cd ~/Movies/ai-tree-video/voice && mkdir -p v2.0 && cp s02.mp3 s0…
cd ~/Movies/ai-tree-video/voice && mkdir -p v2.0 && cp s02.mp3 s03.mp3 s05.mp3 s06.mp3 s08.mp3 s09.mp3 timing.json tts.py v2.0/ && ls v2.0 && ls ../.venv/bin | grep -i python | head -3
Process started with PID 24278 (shell: /bin/zsh) Initial output: s02.mp3 s03.mp3 s05.mp3 s06.mp3 s08.mp3 s09.mp3 timing.json tts.py python python3 python3.14 ✅ Process 24278 has finished execution
写入 tts.py
import asyncio, json, edge_tts VOICE, RATE = "zh-CN-YunxiNeural", "+5%" SEGS = [ ("s00", "这是一棵树的故事。它长了七十多年，最近这三年，突然爆发了。"), ("s01", "1950年，图灵问：机器能思考吗？六年后，一群科学家在达特茅斯开会，给这件事起了个名字——人工智能。"), ("s02", "1958年，有人造出了模仿大脑的“感知机”，这是神经网络的第一颗芽。每个“神经元”把输入乘上权重、加起来；所谓学习，就是不断调这些权重。可十来年后，它被一本书泼了冷水，冻住了。"), ("s03", "那时的做法，是把规则一条条写给电脑。说得太多、做到太少，钱被砍了，第一个冬天来了。八十年代，“专家系统”把专家的经验写成“如果……就……”，风光一时。1986年，冻住的那颗芽也开始化冻：“反向传播”出现了——答错了，就把误差从后往前传，算出每个权重该往哪边拧一点。而专家系统的规则越写越难维护，第二个冬天又来了。"), ("s04", "后来换了条路：不写规则了，让电脑从大量例子里自己找规律，这叫“机器学习”。1997年，每秒能算两亿步的深蓝，赢了国际象棋世界冠军。"), ("s05", "2012年，那颗冻了几十年的芽突然开花：很深的神经网络，喂进海量图片，用两块打游戏的显卡来算——这就是“深度学习”。前几层学会认边缘，中间几层认形状，最后几层认出整只猫。2016年，AlphaGo先学人类棋谱，再自己跟自己下，赢了李世石。2017年，Transformer诞生，后来GPT里的T，就是它。它的核心叫“注意力”：读每个词时，都回头看一遍句子里所有的词。"), ("s06", "人们发现，模型越大、数据越多，它就越聪明。说穿了，大模型只做一件事：根据前文，预测下一个词。可光会接话还不够，再用人类的反馈去调教，它才从“续写机器”变成了聊天助手。2022年底，ChatGPT上线，据估计两个月就有一亿人在用。各条树枝，开始长进同一个树冠。"), ("s07", "如果把这七十多年压缩成一天，最近这三年，只是最后的五十多分钟。可接下来，我要用一半的时间来讲它——因为几乎每个星期，都有大事。"), ("s08", "2024年，打一行字就能生成电影般的视频，背后是“扩散模型”：从一片噪点开始，一步步“去噪”，把画面擦出来。卖AI芯片的英伟达，一度成了全世界最值钱的公司。夏天，AI还会把9.11和9.9比错；九月，它学会了先打一长段草稿，也就是“思维链”，想清楚了再回答。十月，两个诺贝尔奖颁给了AI研究，其中一位得主，就是几十年来 …（中间省略 1492 字）… ", attempt + 1, type(e).__name__) await asyncio.sleep(2 + attempt * 3) else: print(sid, "FAILED"); continue print(sid, len(out[sid]["words"]), "boundaries") json.dump(out, open("timing.json", "w"), ensure_ascii=False, indent=1) asyncio.run(main())Error: Write rejected to prevent accidental data loss: ~/Movies/ai-tree-video/voice/tts.py already exists with content (4659 bytes), and no 'mode' was specified — the default mode 'rewrite' would REPLACE the entire file. Retry with an explicit mode: 'append' to add your content to the end of the existing file, or 'rewrite' to replace all existing content.
运行 cd ~/Movies/ai-tree-video/voice && ../.venv/bin/python tts.py 2>&…
cd ~/Movies/ai-tree-video/voice && ../.venv/bin/python tts.py 2>&1 | tail -20
Process started with PID 24317 (shell: /bin/zsh) Initial output: ✅ Process 24317 has finished execution
思考：Fixing the write mode to force the audio resynthesis.
Fixing the write mode to force the audio resynthesis.
写入 tts.py
import asyncio, json, edge_tts VOICE, RATE = "zh-CN-YunxiNeural", "+5%" SEGS = [ ("s00", "这是一棵树的故事。它长了七十多年，最近这三年，突然爆发了。"), ("s01", "1950年，图灵问：机器能思考吗？六年后，一群科学家在达特茅斯开会，给这件事起了个名字——人工智能。"), ("s02", "1958年，有人造出了模仿大脑的“感知机”，这是神经网络的第一颗芽。每个“神经元”把输入乘上权重、加起来；所谓学习，就是不断调这些权重。可十来年后，它被一本书泼了冷水，冻住了。"), ("s03", "那时的做法，是把规则一条条写给电脑。说得太多、做到太少，钱被砍了，第一个冬天来了。八十年代，“专家系统”把专家的经验写成“如果……就……”，风光一时。1986年，冻住的那颗芽也开始化冻：“反向传播”出现了——答错了，就把误差从后往前传，算出每个权重该往哪边拧一点。而专家系统的规则越写越难维护，第二个冬天又来了。"), ("s04", "后来换了条路：不写规则了，让电脑从大量例子里自己找规律，这叫“机器学习”。1997年，每秒能算两亿步的深蓝，赢了国际象棋世界冠军。"), ("s05", "2012年，那颗冻了几十年的芽突然开花：很深的神经网络，喂进海量图片，用两块打游戏的显卡来算——这就是“深度学习”。前几层学会认边缘，中间几层认形状，最后几层认出整只猫。2016年，AlphaGo先学人类棋谱，再自己跟自己下，赢了李世石。2017年，Transformer诞生，后来GPT里的T，就是它。它的核心叫“注意力”：读每个词时，都回头看一遍句子里所有的词。"), ("s06", "人们发现，模型越大、数据越多，它就越聪明。说穿了，大模型只做一件事：根据前文，预测下一个词。可光会接话还不够，再用人类的反馈去调教，它才从“续写机器”变成了聊天助手。2022年底，ChatGPT上线，据估计两个月就有一亿人在用。各条树枝，开始长进同一个树冠。"), ("s07", "如果把这七十多年压缩成一天，最近这三年，只是最后的五十多分钟。可接下来，我要用一半的时间来讲它——因为几乎每个星期，都有大事。"), ("s08", "2024年，打一行字就能生成电影般的视频，背后是“扩散模型”：从一片噪点开始，一步步“去噪”，把画面擦出来。卖AI芯片的英伟达，一度成了全世界最值钱的公司。夏天，AI还会把9.11和9.9比错；九月，它学会了先打一长段草稿，也就是“思维链”，想清楚了再回答。十月，两个诺贝尔奖颁给了AI研究，其中一位得主，就是几十年来 …（中间省略 1492 字）… ", attempt + 1, type(e).__name__) await asyncio.sleep(2 + attempt * 3) else: print(sid, "FAILED"); continue print(sid, len(out[sid]["words"]), "boundaries") json.dump(out, open("timing.json", "w"), ensure_ascii=False, indent=1) asyncio.run(main())Successfully wrote to ~/Movies/ai-tree-video/voice/tts.py (44 lines)
运行 cd ~/Movies/ai-tree-video/voice && ../.venv/bin/python tts.py 2>&…
cd ~/Movies/ai-tree-video/voice && ../.venv/bin/python tts.py 2>&1 | tail -20
Process started with PID 24345 (shell: /bin/zsh) Initial output: s02 46 boundaries s03 83 boundaries s05 99 boundaries s06 72 boundaries s08 92 boundaries s09 101 boundaries ✅ Process 24345 has finished execution
准备从你电脑传回文件
{ "paths": [ "~/Movies/ai-tree-video/voice/s02.mp3", "~/Movies/ai-tree-video/voice/s03.mp3", "~/Movies/ai-tree-video/voice/s05.mp3", "~/Movies/ai-tree-video/voice/s06.mp3", "~/Movies/ai-tree-video/voice/s08.mp3", "~/Movies/ai-tree-video/voice/s09.mp3", "~/Movies/ai-tree-video/voice/timing.json" ] }{"staged":[{"devicePath":"~/Movies/ai-tree-video/voice/s02.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s02.mp3","mtimeMs":1792036663490,"bytes":98784,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/s03.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s03.mp3","mtimeMs":1792036666167,"bytes":180864,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/s05.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s05.mp3","mtimeMs":1792036668609,"bytes":199872,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/s06.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s06.mp3","mtimeMs":1792036671170,"bytes":150480,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/s08.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s08.mp3","mtimeMs":1792036683561,"bytes":194544,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/s09.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s09.mp3","mtimeMs":1792036687519,"bytes":233424,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/timing.json","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/timing.json","mtimeMs":1792036687526,"bytes":50466,"ok":true,"dispatched":true}]}运行 sleep 2; cd ~/ai-tree && mkdir -p audio/voice/v2.0 && cp audio/voice…
sleep 2; cd ~/ai-tree && mkdir -p audio/voice/v2.0 && cp audio/voice/s02.mp3 audio/voice/s03.mp3 audio/voice/s05.mp3 audio/voice/s06.mp3 audio/voice/s08.mp3 audio/voice/s09.mp3 audio/voice/timing.json audio/voice/v2.0/ && for f in s02 s03 s05 s06 s08 s09; do cp /mnt/user-data/uploads/ai-tree-video/voice/$f.mp3 audio/voice/; done && cp /mnt/user-data/uploads/ai-tree-video/voice/timing.json audio/voice/timing.json && for f in s02 s03 s05 s06 s08 s09; do printf "%s %s\n" $f $(ffprobe -v error -show_entries format=duration -of csv=p=0 audio/voice/$f.mp3); done && python3 -c " import json; t=json.load(open('audio/voice/timing.json')) for k in ['s02','s03','s05','s06','s08','s09']: print(k, ' '.join(w['w'] for w in t[k]['words'])[:400])"s02 16.464000 s03 30.144000 s05 33.312000 s06 25.080000 s08 32.424000 s09 38.904000 s02 1958 年 有人 造出 了 模仿 大脑 的 感知机 这 是 神经 网络 的 第一 颗 芽 每个 神经元 把 输入 乘上 权重 加 起来 所谓 学习 就 是 不断 调 这些 权重 可 十来年 后 它 被 一本 书 泼 了 冷水 冻 住 了 s03 那时 的 做法 是 把 规则 一条条 写给 电脑 说 得 太 多 做到 太 少 钱 被 砍 了 第一个 冬天来了 八十 年代 专家 系统 把 专家 的 经验 写 成 如果 就 风光 一时 1986 年 冻住 的 那 颗 芽 也 开始 化冻 反向 传播 出现 了 答错 了 就 把 误差 从 后往 前传 算 出 每 个 权重 该 往 哪边 拧 一点 而 专家 系统 的 规则 越 写 越 难 维护 第二个 冬天 又 来 了 s05 2012 年 那 颗 冻 了 几十 年 的 芽 突然 开花 很 深 的 神经 网络 喂 进 海量 图片 用 两 块 打 游戏 的 显卡 来 算 这 就 是 深度 学习 前 几 层 学会 认 边缘 中间 几层 认 形状 最后 几层 认出 整 只 猫 2016 年 AlphaGo 先 学 人类 棋谱 再 自己 跟 自己 下 赢 了 李世石 2017 年 Transformer 诞生 后来 GPT 里 的 T 就 是 它 它 的 核心 叫 注意力 读 每 个 词 时 都 回头 看 一 遍 句 子 里 所有 的 词 s06 人们 发现 模型 越 大 数据 越 多 它 就 越 聪明 说穿 了 大 模型 只 做 一 件 事 根据 前文 预测 下 一个 词 可光 会 接话 还 不够 再 用 人类 的 反馈 去 调教 它 才 从 续 写 机器 变成 了 聊天 助手 2022 年底 ChatGPT 上线 据 估计 两 个 月 就 有 一亿 人 在 用 各 条 树枝 开始 长进 同 一个 树冠 s08 2024 年 打 一 行 字 就 能 生成 电影 般 的 视频 背后 是 扩散 模型 从 一片 噪点 开始 一 步 步 去 噪 把 画面 擦 出来 卖 AI 芯片 的 英伟达 一度 成 了 全世界 最 值钱 的 公司 夏天 AI 还 会 把 9.11 和 9.9 比错 九月 它 学会 了 先 打 一 长 段 草稿 也就是 思维 链 想 清楚 了 再 回答 十月 两 个 诺贝尔 奖 颁给 了 AI 研究 其中一位 得主 就 是 几十 年 来 一直 坚持 神经 网络 的 辛顿 s09 2025 年 一开年 中国 的 DeepSeek 免费 又 开源 一 周 就 冲上 美国 下载 榜 第一 英伟达 一天 蒸发 近 六千亿 美元 除夕夜 机器人 在 春晚 扭 起 了 秧歌 AI 不只 会 聊天 了 开始 自己 动手 写 程序 办 事情 这 叫 智能体 给 大 模型 配上 工具 让 它 自己 循环 想 一 步 做 一 步 看看 结果 全 网 把 照片 变成 动画风 服务器 忙 到 冒烟 AI 视频 开口 说话 奥数 考出 了 金牌 水平 十月 每周 已 有 八亿 人 在 用 ChatGPT 英伟 达成 了 史上 第一 家 五万亿 美元 的 公司
思考：Locating key terms and dates within the source text.
Locating key terms and dates within the source text.
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('tools/build_timeline.py'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:80]); s = s.replace(old, new) rep(""" dict(id="b3", seg="s03", pre=0.2, post=2.2, splits=[("第一个冬天来了", 1.4)], y0=1970.0, y1=1994.0, anchors=[("第一个", 1974.6), ("八十", 1980.0), ("风光", 1984.6), ("第二个", 1988.2)]),""", """ dict(id="b3", seg="s03", pre=0.2, post=2.2, splits=[("第一个冬天来了", 1.4)], y0=1970.0, y1=1994.0, anchors=[("第一个", 1974.6), ("八十", 1980.0), ("风光", 1984.6), ("1986", 1986.0), ("而", 1987.4), ("第二个", 1988.2)]),""") rep("""# term cards: (card id, beat, word that triggers it) CARDS = [ ("ai", "b1", "人工智能"), ("nn", "b2", "神经网络"), ("expert", "b3", "专家系统"), ("backprop", "b4", "后来"), ("ml", "b4", "机器学习"), ("dl", "b5", "深度"), ("rl", "b5", "自己"), ("transformer", "b5", "Transformer"), ("llm", "b6", "越聪明"), ("reason", "b8", "先"), ("agent", "b9", "智能体"), ("embodied", "b10", "多模态"), ]""", """# term cards: (card id, beat, word that brings the card in, word where the narrator explains the mechanism or None) CARDS = [ …（中间省略 2488 字）… TL["total"]) for c in TL["cards"]: print(c) print({k: round(v, 2) for k, v in TL["marks"].items()}) b = {x["id"]: x for x in TL["beats"]} p1 = b["b7"]["t0"] - b["b1"]["t0"]; p2 = b["end"]["t0"] - b["b7"]["t0"] print("part1", round(p1, 1), "part2", round(p2, 1), "share", round(p2 / (p1 + p2), 3)) EOFok total 314.2s | beats 12 | captions 96 | cards 14 | polaroids 16 | notes 40 intro 0.00 → 11.37 b1 11.37 → 22.93 b2 22.93 → 40.60 b3 40.60 → 74.54 b4 74.54 → 91.26 b5 91.26 → 126.57 b6 126.57 → 153.25 b7 153.25 → 166.66 b8 166.66 → 200.28 b9 200.28 → 240.38 b10 240.38 → 286.95 end 286.95 → 314.20 total 314.2 {'id': 'ai', 't': 20.228, 'td': 21.428, 'hold': 5.0} {'id': 'nn', 't': 26.734, 'td': 29.068, 'hold': 8.688} {'id': 'expert', 't': 51.784, 'td': 52.984, 'hold': 5.0} {'id': 'backprop', 't': 60.201, 'td': 61.727, 'hold': 7.25} {'id': 'ml', 't': 80.438, 'td': 81.638, 'hold': 5.0} {'id': 'dl', 't': 101.708, 'td': 103.041, 'hold': 7.259} {'id': 'rl', 't': 111.979, 'td': 113.179, 'hold': 3.119} {'id': 'transformer', 't': 115.348, 'td': 120.267, 'hold': 9.988} {'id': 'llm', 't': 130.053, 'td': 131.124, 'hold': 6.474} {'id': 'rlhf', 't': 138.277, 'td': 138.777, 'hold': 4.952} {'id': 'diffusion', 't': 171.125, 'td': 172.806, 'hold': 5.429} {'id': 'reason', 't': 185.813, 'td': 188.316, 'hold': 5.012} {'id': 'agent', 't': 217.913, 'td': 219.773, 'hold': 6.536} {'id': 'embodied', 't': 266.505, 'td': 267.705, 'hold': 5.0} {'ff0': 2.18, 'ff1': 5.02, 'rw0': 7.12, 'rw1': 8.32, 'intro_end': 11.37, 'seed_T': 11.42, 'crazy_T0': 269.11, 'crazy_T1': 277.07, 'day_bar': 154.77, 'sliver': 157.57, 'half': 160.61, 'weekly': 162.79, 'day_out': 165.96, 'bloom': 91.52, 'chatgpt': 144.87, 'winter1': 47.55, 'spring1': 50.91, 'winter2': 68.82, 'spring2': 74.25, 'wither': 68.1, 'method1': 74.74, 'method2': 96.75, 'method3': 126.77, 'year2024': 166.71, 'year2025': 200.33, 'year2026': 240.43, 'roots': 297.09, 'bud': 302.29} part1 141.9 part2 133.7 share 0.485思考：Checking timing calculations across each section. Adjusting …
Checking timing calculations across each section.
Adjusting card timings to avoid overlapping visuals.运行 sed -i 's/("rl", "b5", "自己", None)/("rl", "b5", "AlphaGo", None)/' tools/build_…
sed -i 's/("rl", "b5", "自己", None)/("rl", "b5", "AlphaGo", None)/' tools/build_timeline.py && python3 tools/build_timeline.py 2>&1 | head -1 && python3 -c " import json; s=open('scene/timeline.js').read(); TL=json.loads(s[len('window.TL = '):].rstrip().rstrip(';')) print([ (c['id'], c['t'], c['hold']) for c in TL['cards'] if c['id'] in ('dl','rl','transformer')])" && (timeout 1200 python3 tools/qa_layout.py > out/qa-land.txt 2>&1 &); timeout 1200 python3 tools/qa_layout.py --portrait > out/qa-port.txt 2>&1; sleep 8; tail -n 2 out/qa-land.txt; wc -l out/qa-land.txt out/qa-port.txttotal 314.2s | beats 12 | captions 96 | cards 14 | polaroids 16 | notes 40 [('dl', 101.708, 7.259), ('rl', 109.86, 5.0), ('transformer', 115.348, 9.988)] panels (229, 520, 682, 686) | (85, 565, 439, 962) @ 224.75-224.75 done landscape 43 out/qa-land.txt 50 out/qa-port.txt 93 total运行 cat out/qa-land.txt; echo; echo =====PORTRAIT; cat out/qa-port.txt
cat out/qa-land.txt; echo; echo =====PORTRAIT; cat out/qa-port.txt
hidden 画画 · 创作 @ 3.75-7.50, 109.25-109.25, 143.50-143.50, 154.75-288.25, 308.25-314.00 hidden 看图 @ 7.25-7.50, 87.75-87.75, 154.75-166.25, 167.00-167.75, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden ChatGPT @ 7.25-7.50, 154.75-166.25, 167.00-167.75, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden 推理模型 @ 7.25-7.50, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden 智能体 @ 7.25-7.25, 240.50-241.50, 308.25-314.00 hidden 多模态 @ 7.25-7.25, 308.25-314.00 hidden DeepSeek @ 7.50-7.50, 308.25-314.00 panels (432, 272, 796, 420) | (30, 40, 500, 500) @ 35.75-35.75 overlap 1997 深蓝 · 胜棋王卡斯帕罗夫 | 下棋 @ 87.00-87.00 hidden 1997 深蓝 · 胜棋王卡斯帕罗夫 @ 87.25-87.25 hidden 听懂说话 @ 87.75-89.25, 127.00-129.75, 154.75-166.25, 168.25-171.00, 176.75-180.00, 191.00-191.00, 192.25-196.50, 202.25-205.25, 210.50-213.25, 224.50-227.75, 245.25-248.50, 257.50-260.75, 271.75-271.75, 275.75-279.75, 308.25-314.00 overlap 1998 LeNet · 认手写数字 | 看图 @ 88.00-88.00 panels (95, 486, 429, 962) | (11, 566, 445, 962) @ 88.50-88.50 panels (90, 486, 424, 962) | (45, 566, 479, 962) @ 88.75-88.75 overlap 2006 谷歌翻译 · 按统计规律翻译 | 2011 Watson · 问答节目夺冠 @ 91.75-91.75 hidden 2006 谷歌翻译 · 按统计规律翻译 @ 92.00-92.50 hidden 2011 Siri · 语音助手 @ 97.00-100.50 panels (220, 361, 440, 453) | (30, 40, 500, 500) @ 115.25-115.25 panels (361, 487, 891, 553) | (30, 40, 500, 500) @ 127.00-127.00 panels (361, 485, 891, 551) | (30, 40, 500, 500) @ 127.25-127.25 panels (362, 483, 891 …（中间省略 4812 字）… 1346, 1024, 1714) @ 191.00-191.00 panels (22, 1343, 1058, 1671) | (56, 1346, 1024, 1714) @ 218.25-218.25 panels (24, 1353, 1058, 1680) | (56, 1346, 1024, 1714) @ 224.50-224.50 hidden DeepSeek @ 224.75-224.75, 308.25-314.00 panels (22, 1350, 1058, 1678) | (56, 1346, 1024, 1714) @ 266.75-266.75 panels (22, 1354, 1058, 1682) | (56, 1346, 1024, 1714) @ 267.00-267.00 done portrait
思考：Checking for a hidden paint name issue in the landscape. Dia…
Checking for a hidden paint name issue in the landscape.
Diagnosing overlapping speech labels hidden behind polaroids.
Checking whether a label still clashes with a timed callout.
Adjusting callout placement to avoid new timing overlaps.
Tracking other hidden overlaps across multiple time ranges.
Repositioning a card to avoid covering crown labels.
Scanning portrait layout for remaining hidden label conflicts.
Repositioning polaroids in portrait to clear hidden robot labels.
Testing a top-right polaroid placement for portrait mode.
Resolving a flying card overlapping a hidden chess label.
Recalculating polaroid size and position to clear card and labels.
Tracing a shadowed timed callout behind a portrait polaroid.运行 cat > /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpa…
cat > /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/qa1.py <<'EOF' import sys, json from pathlib import Path from playwright.sync_api import sync_playwright ROOT = Path('~/ai-tree') portrait = sys.argv[1] == 'p'; ts = [float(x) for x in sys.argv[2:]] W, H = (1080, 1920) if portrait else (1920, 1080) with sync_playwright() as p: b = p.chromium.launch(); page = b.new_page(viewport={"width": W, "height": H}) page.goto((ROOT / "scene" / "film.html").as_uri() + ("?v=portrait" if portrait else "")) page.wait_for_function("window.__ready === true", timeout=60000) for t in ts: r = page.evaluate(f"qa({t})") print("t =", t) for l in r["labels"]: print(" L", l) for q in r["panels"]: print(" P", q) b.close() EOF python3 /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/qa1.py p 88.0 && python3 /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/qa1.py l 147.0t = 88.0 L {'s': '翻译 · 聊天', 'box': [809, 1449, 1037, 1492], 'a': 1, 'final': 1, 'w8': 1} L {'s': '机器人 · 开车', 'box': [800, 1174, 1071, 1217], 'a': 1, 'final': 1, 'w8': 1} L {'s': '当专家', 'box': [98, 1444, 237, 1487], 'a': 1, 'final': 1, 'w8': 1} L {'s': '1997 深蓝 · 胜棋王卡斯帕罗夫', 'box': [513, 1173, 975, 1206], 'a': 1, 'final': 0, 'w8': 0.73} L {'s': '下棋', 'box': [892, 1043, 988, 1086], 'a': 1, 'final': 0, 'w8': 1} L {'s': '1997 Dragon · 听写软件', 'box': [157, 1188, 537, 1221], 'a': 1, 'final': 1, 'w8': 1.21} L {'s': '听懂说话', 'box': [103, 1102, 285, 1145], 'a': 1, 'final': 1, 'w8': 1} L {'s': '1998 LeNet · 认手写数字', 'box': [468, 1114, 856, 1147], 'a': 1, 'final': 1, 'w8': 2.52} L {'s': '看图', 'box': [697, 987, 793, 1030], 'a': 1, 'final': 0, 'w8': 1} P [746.66, 719.24, 1013.34, 1098, 1] P [20, 40, 1060, 356, 1] t = 147.0 L {'s': '翻译 · 聊天', 'box': [1477, 741, 1699, 783], 'a': 1, 'final': 1, 'w8': 1} L {'s': '机器人 · 开车', 'box': [1507, 611, 1771, 653], 'a': 1, 'final': 1, 'w8': 1} L {'s': '当专家', 'box': [517, 803, 653, 845], 'a': 1, 'final': 1, 'w8': 1} L {'s': '下棋', 'box': [1497, 461, 1591, 503], 'a': 1, 'final': 1, 'w8': 1} L {'s': '听懂说话', 'box': [365, 521, 543, 563], 'a': 1, 'final': 1, 'w8': 1} L {'s': '看图', 'box': [1327, 321, 1421, 363], 'a': 1, 'final': 1, 'w8': 1} L {'s': '2022 扩散模型 · 一句话出图', 'box': [436, 320, 856, 352], 'a': 1, 'final': 0.34, 'w8': 2.31} L {'s': '画画 · 创作', 'box': [549, 274, 771, 316], 'a': 1, 'final': 1, 'w8': 1} L {'s': '搞科研', 'box': [672, 134, 808, 176], 'a': 1, 'final': 1, 'w8': 1} L {'s': 'ChatGPT', 'box': [840, 363, 1000, 399], 'a': 1, 'final': 1, 'w8': 2.4} P [85, 565, 439, 962, 1] P [994.8, 119.32, 1292.4, 287.32, 1] P [597, 969, 1323, 1058, 1] P [30, 40, 500, 500, 1]思考：Tracking weight adjustments tied to age thresholds in a port…
Tracking weight adjustments tied to age thresholds in a portrait tag.
Recomputing the timeline interpolation behind the age-based weight value.运行 python3 - <<'EOF'
python3 - <<'EOF' import json s = open('scene/timeline.js').read() TL = json.loads(s[len("window.TL = "):].rstrip().rstrip(";")) yk = TL["ykeys"] for t, y in yk: if 70 < t < 95: print(t, y) b = {x["id"]: x for x in TL["beats"]} print(b["b4"]) for c in TL["captions"]: if 74 < c["t0"] < 92: print(c) EOF70.256 1988.2 74.54 1994.0 74.54 1994.0 74.838 1994.4 81.847 1997.36 86.752 1998.0 91.26 2011.6 91.26 2011.6 91.558 2012.0 {'id': 'b4', 't0': 74.54, 'ts': 74.74, 'te': 88.06, 't1': 91.26} {'text': '后来换了条路：不写规则了', 't0': 74.838, 't1': 77.195} {'text': '让电脑从大量例子里自己找规律', 't0': 77.338, 't1': 80.195} {'text': '这叫“机器学习”', 't0': 80.302, 't1': 81.552} {'text': '1997年，每秒能算两亿步的深蓝', 't0': 81.847, 't1': 85.335} {'text': '赢了国际象棋世界冠军', 't0': 85.347, 't1': 87.454} {'text': '2012年，那颗冻了几十年的芽突然开花', 't0': 91.558, 't1': 95.391}思考：Pinpointing when the deep blue tag's age value occurred.
Pinpointing when the deep blue tag's age value occurred.
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:80]); s = s.replace(old, new) rep("era: { x: 68, y: 240, size: 46 }, panel: [30, 40, 500, 500],", "era: { x: 68, y: 240, size: 46 }, panels: [[30, 40, 530, 345], [30, 345, 340, 500]],") rep("era: { x: 60, y: 268, size: 52 }, panel: [20, 40, 1060, 356],", "era: { x: 60, y: 268, size: 52 }, panels: [[20, 40, 1060, 380]],") rep(" add(L.panel, da);", " L.panels.forEach(r => add(r, da));") rep(" nameDir: { chat: 'right', robot: 'right', expert: 'left', chess: 'right', speech: 'left', vision: 'right', paint: 'up', science: 'up' },", " nameDir: { chat: 'right', robot: 'right', expert: 'left', chess: 'right', speech: 'up', vision: 'right', paint: 'up', science: 'up' },") rep(" stamp: [960, 540, 230],\n callout: { dx: -380, dy: 40 },", " stamp: [960, 540, 230],\n callout: { dx: -380, dy: 120 },") rep(" card: { p1: [1100, 12], p2: [430, 620] },", " card: { p1: [1100, 12], p2: [560, 10] },") rep(" pola: { cx: 228, bottom: 1712 }, pola2: { cx: 862, bottom: 1262 },", " pola: { cx: 866, bottom: 812 }, pola2: { cx: 950, bottom: 790, scale: 0.7 },") rep(" polaAlt: { cx: 880, bottom: 1084, scale: 0.78 },", " polaAlt: { cx: 190, bottom: 1050, scale: 0.68 },") p.write_text(s); print('ok') PYEOF grep -n "pola: {" scene/film.jsTraceback (most recent call last): File "<stdin>", line 13, in <module> File "<stdin>", line 5, in rep AssertionError: (0, ' pola: { cx: 228, bottom: 1712 }, pola2: { cx: 862, bottom: 1262 },') 55: pola: { cx: 262, bottom: 948 }, 84: pola: { cx: 866, bottom: 812 }, pola2: { cx: 862, bottom: 1262 },运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:80]); s = s.replace(old, new) rep("era: { x: 68, y: 240, size: 46 }, panel: [30, 40, 500, 500],", "era: { x: 68, y: 240, size: 46 }, panels: [[30, 40, 530, 345], [30, 345, 340, 500]],") rep("era: { x: 60, y: 268, size: 52 }, panel: [20, 40, 1060, 356],", "era: { x: 60, y: 268, size: 52 }, panels: [[20, 40, 1060, 380]],") rep(" add(L.panel, da);", " L.panels.forEach(r => add(r, da));") rep(" nameDir: { chat: 'right', robot: 'right', expert: 'left', chess: 'right', speech: 'left', vision: 'right', paint: 'up', science: 'up' },", " nameDir: { chat: 'right', robot: 'right', expert: 'left', chess: 'right', speech: 'up', vision: 'right', paint: 'up', science: 'up' },") rep(" stamp: [960, 540, 230],\n callout: { dx: -380, dy: 40 },", " stamp: [960, 540, 230],\n callout: { dx: -380, dy: 120 },") rep(" card: { p1: [1100, 12], p2: [430, 620] },", " card: { p1: [1100, 12], p2: [560, 10] },") rep(" pola: { cx: 866, bottom: 812 }, pola2: { c …（中间省略 243 字）… 'ok') PYEOF (timeout 1200 python3 tools/qa_layout.py > out/qa-land.txt 2>&1 &); timeout 1200 python3 tools/qa_layout.py --portrait > out/qa-port.txt 2>&1; sleep 8; grep -v "^panels" out/qa-land.txt; echo ====; grep -v "^panels" out/qa-port.txt; echo; grep -c "^panels" out/qa-land.txt out/qa-port.txtok hidden 看图 @ 7.25-7.50, 87.75-87.75, 154.75-166.25, 167.00-167.75, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden ChatGPT @ 7.25-7.50, 154.75-166.25, 167.00-167.75, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden 推理模型 @ 7.25-7.50, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden 智能体 @ 7.25-7.25, 240.50-241.50, 308.25-314.00 hidden 多模态 @ 7.25-7.25, 308.25-314.00 hidden DeepSeek @ 7.50-7.50, 224.75-224.75, 271.75-271.75, 308.25-314.00 overlap 1997 深蓝 · 胜棋王卡斯帕罗夫 | 下棋 @ 87.00-87.00 overlap 1997 Dragon · 听写软件 | 听懂说话 @ 87.00-87.00 hidden 1997 深蓝 · 胜棋王卡斯帕罗夫 @ 87.25-87.25 overlap 1998 LeNet · 认手写数字 | 看图 @ 88.00-88.00 overlap 2006 谷歌翻译 · 按统计规律翻译 | 2011 Watson · 问答节目夺冠 @ 91.75-91.75 hidden 2006 谷歌翻译 · 按统计规律翻译 @ 92.00-92.50 overlap 听懂说话 | 2014 GAN · 两个网络对练画画 @ 105.50-114.50 hidden 画画 · 创作 @ 109.25-109.25, 143.50-143.50, 154.75-166.25, 308.25-314.00 hidden 2014 GAN · 两个网络对练画画 @ 114.50-114.50 hidden 2020 AlphaFold · 猜出蛋白质结构 @ 133.50-133.50, 136.75-136.75 hidden 2022 扩散模型 · 一句话出图 @ 143.50-152.25 hidden 搞科研 @ 143.50-143.50, 154.75-166.25, 176.75-176.75, 224.75-224.75, 271.75-271.75, 308.25-314.00 hidden 翻译 · 聊天 @ 154.75-166.25, 308.25-314.00 hidden 机器人 · 开车 @ 154.75-166.25, 308.25-314.00 hidden 当专家 @ 154.75-155.00 hidden 下棋 @ 154.75-166.25, 167.00-167.75, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden 听懂说话 @ 154.75-166.25, 308.25-314.00 done landscape ==== hidden 智能体 @ 7.25-7.25, 224.75 …（中间省略 1066 字）… 两个网络对练画画 @ 110.75-114.50 overlap 2022 扩散模型 · 一句话出图 | ChatGPT @ 150.75-151.50 hidden 2022 扩散模型 · 一句话出图 @ 151.75-152.25 hidden 翻译 · 聊天 @ 154.75-166.25, 176.75-176.75, 271.75-271.75, 308.25-314.00 hidden 当专家 @ 154.75-155.00 hidden 下棋 @ 154.75-166.25, 224.75-224.75, 308.25-314.00 hidden DeepSeek @ 224.75-224.75, 308.25-314.00 done portrait out/qa-land.txt:14 out/qa-port.txt:39
思考：整理时间轴条目中的重叠问题。 调整压制公式以修复部分重叠。 调整日期面板布局以避免遮挡。 检查竖屏布局中的卡片位置重叠。…
整理时间轴条目中的重叠问题。
调整压制公式以修复部分重叠。
调整日期面板布局以避免遮挡。
检查竖屏布局中的卡片位置重叠。
重新定位竖屏卡片与宝丽来照片位置。运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:80]); s = s.replace(old, new) rep("s = Math.max(s, clamp((f - 0.03) * 6) * sstep((o.w8 - it.w8) / 0.3 + 0.5) * o.a);", "s = Math.max(s, clamp((f - 0.03) * 12) * sstep((o.w8 - it.w8) / 0.3 + 0.5) * o.a);") rep("if (f > 0.02) s = Math.max(s, clamp((f - 0.02) * 5) * pnl[4]); }", "if (f > 0.02) s = Math.max(s, clamp((f - 0.02) * 8) * pnl[4]); }") rep("panels: [[30, 40, 530, 345], [30, 345, 340, 500]],", "panels: [[30, 40, 530, 262], [30, 262, 448, 345], [30, 345, 340, 490]],") rep(" ruler: { x0: 70, x1: 480, y: 296, lab: 22 },", " ruler: { x0: 70, x1: 420, y: 296, lab: 22 },") rep(" card: { p1: [30, 372], p2: [30, 1372] },", " card: { p1: [30, 392], p2: [30, 1372] },") rep(" pola: { cx: 866, bottom: 812 }, pola2:", " pola: { cx: 866, bottom: 850 }, pola2:") p.write_text(s); print('ok') PYEOF (timeout 1200 python3 tools/qa_layout.py > out/qa-land.txt 2>&1 &); timeout 1200 python3 tools/qa_layout.py --portrait > out/qa-port.txt 2>&1; sleep 8; grep -v "hidden" out/qa-land.txt; echo ====; grep -v "hidden" out/qa-port.txt | grep -v "(56, 1346, 1024, 1714)"ok overlap 1997 深蓝 · 胜棋王卡斯帕罗夫 | 下棋 @ 87.00-87.00 panels (95, 486, 429, 962) | (11, 566, 445, 962) @ 88.50-88.50 panels (90, 486, 424, 962) | (45, 566, 479, 962) @ 88.75-88.75 overlap 听懂说话 | 2014 GAN · 两个网络对练画画 @ 105.50-105.50, 110.50-110.50 panels (220, 361, 440, 453) | (30, 345, 340, 490) @ 115.25-115.25 panels (481, 56, 1196, 307) | (30, 40, 530, 262) @ 176.75-176.75 panels (501, 38, 1246, 298) | (30, 40, 530, 262) @ 191.00-191.00 panels (293, 218, 746, 384) | (30, 40, 530, 262) @ 224.75-224.75 panels (293, 218, 746, 384) | (30, 262, 448, 345) @ 224.75-224.75 panels (413, 115, 1032, 335) | (30, 40, 530, 262) @ 271.75-271.75 panels (413, 115, 1032, 335) | (30, 262, 448, 345) @ 271.75-271.75 done landscape ==== panels (296, 257, 990, 484) | (20, 40, 1060, 380) @ 25.50-25.50 panels (483, 176, 943, 333) | (20, 40, 1060, 380) @ 35.75-35.75 overlap 1966 Shakey · 会自己找路 | 机器人 · 开车 @ 41.75-42.25 overlap 1970s MYCIN · 帮医生选药 | 1980 XCON · 帮公司配电脑 @ 51.25-51.25 panels (159, 316, 1024, 594) | (20, 40, 1060, 380) @ 57.00-57.00 panels (386, 218, 967, 411) | (20, 40, 1060, 380) @ 67.75-67.75 overlap 机器人 · 开车 | 1997 深蓝 · 胜棋王卡斯帕罗夫 @ 82.00-82.00, 87.00-87.00 panels (432, 198, 956, 376) | (20, 40, 1060, 380) @ 85.75-85.75 overlap 听懂说话 | 2014 GAN · 两个网络对练画画 @ 105.50-105.50, 110.50-110.50 panels (330, 242, 981, 456) | (20, 40, 1060, 380) @ 109.25-109.25 panels (22, 348, 1058, 676) | (20, 40, 1060, 380) @ 110.00-110. …（中间省略 109 字）… , 40, 1060, 380) @ 115.25-115.25 panels (22, 353, 1058, 685) | (20, 40, 1060, 380) @ 115.50-115.50 panels (82, 350, 1043, 659) | (20, 40, 1060, 380) @ 125.50-125.50 panels (173, 310, 1020, 583) | (20, 40, 1060, 380) @ 136.75-136.75 panels (293, 258, 991, 486) | (20, 40, 1060, 380) @ 143.50-143.50 panels (393, 700, 966, 891) | (822, 516, 1078, 804) @ 224.75-224.75 done portrait
思考：Tracking card positions and overlaps across layout regions.
Tracking card positions and overlaps across layout regions.
运行 grep "hidden" out/qa-land.txt; echo ===; grep "hidden" out/qa-port.txt
grep "hidden" out/qa-land.txt; echo ===; grep "hidden" out/qa-port.txt
hidden 看图 @ 7.25-7.50, 87.75-88.00, 154.75-166.25, 167.00-167.75, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden ChatGPT @ 7.25-7.50, 154.75-166.25, 167.00-167.75, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden 推理模型 @ 7.25-7.50, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden 智能体 @ 7.25-7.25, 240.50-241.50, 308.25-314.00 hidden 多模态 @ 7.25-7.25, 308.25-314.00 hidden DeepSeek @ 7.50-7.50, 224.75-224.75, 271.75-271.75, 308.25-314.00 hidden 2020 AlphaFold · 猜出蛋白质结构 @ 7.75-7.75, 133.50-142.25 hidden 听懂说话 @ 87.00-87.00, 105.75-110.25, 154.75-166.25, 308.25-314.00 hidden 1997 深蓝 · 胜棋王卡斯帕罗夫 @ 87.25-87.25 hidden 2006 谷歌翻译 · 按统计规律翻译 @ 91.75-92.50 hidden 画画 · 创作 @ 109.25-109.25, 143.50-143.50, 154.75-166.25, 308.25-314.00 hidden 2014 GAN · 两个网络对练画画 @ 110.75-114.50 hidden 2022 扩散模型 · 一句话出图 @ 143.50-143.50 hidden 搞科研 @ 143.50-143.50, 154.75-166.25, 176.75-176.75, 224.75-224.75, 271.75-271.75, 308.25-314.00 hidden 翻译 · 聊天 @ 154.75-166.25, 308.25-314.00 hidden 机器人 · 开车 @ 154.75-166.25, 308.25-314.00 hidden 当专家 @ 154.75-155.00 hidden 下棋 @ 154.75-166.25, 167.00-167.75, 200.50-201.50, 240.50-241.50, 308.25-314.00 === hidden 智能体 @ 7.25-7.25, 224.75-224.75, 308.25-314.00 hidden 多模态 @ 7.25-7.25, 308.25-314.00 hidden ChatGPT @ 7.50-7.50, 154.75-166.25, 167.00-167.75, 224.75-224.75, 308.25-314.00 hidden 推理模型 @ 7.50-7.50, 224.75-224.75, 308.25-314.00 hidden 看图 @ 7.75-7.75, 145.25-149.00, 154.75-166 …（中间省略 433 字）… 5.75-110.50, 154.75-166.25, 167.00-167.75, 200.50-201.50, 240.50-241.50, 308.25-314.00 hidden 2014 GAN · 两个网络对练画画 @ 110.75-114.50 hidden 2022 扩散模型 · 一句话出图 @ 150.75-152.25 hidden 翻译 · 聊天 @ 154.75-166.25, 176.75-176.75, 271.75-271.75, 308.25-314.00 hidden 当专家 @ 154.75-155.00 hidden 下棋 @ 154.75-166.25, 224.75-224.75, 308.25-314.00 hidden DeepSeek @ 224.75-224.75, 308.25-314.00
思考：确定2020年AlphaFold解决蛋白质结构这一时间点。
确定2020年AlphaFold解决蛋白质结构这一时间点。
运行 python3 /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratch…
python3 /tmp/claude-0/-home-claude/e3df710b-de86-557e-ba13-5a6aba8353c3/scratchpad/qa1.py l 137.0 2>&1 | grep -v "'final': 1,"
t = 137.0 L {'s': '2020 AlphaFold · 猜出蛋白质结构', 'box': [476, 176, 981, 208], 'a': 1, 'final': 0.3, 'w8': 2.31} P [79.91, 420.42, 203.22, 480.89, 0.15] P [447, 969, 1473, 1058, 1] P [30, 40, 530, 262, 1] P [30, 262, 448, 345, 1] P [30, 345, 340, 490, 1]思考：Calculating overlap between layout elements on a page. Adjus…
Calculating overlap between layout elements on a page.
Adjusting panel widths to resolve remaining overlaps.运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() old = " L.panels.forEach(r => add(r, da));" assert s.count(old) == 1 s = s.replace(old, " L.panels.forEach((r, k) => add(k === 0 && L.panelP1x1 && Y < 2024 ? [r[0], r[1], L.panelP1x1, r[3]] : r, da)); // the year is short before 2024") old2 = "panels: [[30, 40, 530, 262], [30, 262, 448, 345], [30, 345, 340, 490]]," assert s.count(old2) == 1 s = s.replace(old2, old2 + " panelP1x1: 400,") p.write_text(s); print('ok') PYEOF rm -f out/frames/*.png; T="6.0 9.6 14.0 21.5 30.0 40.0 49.0 54.0 63.0 70.0 76.0 82.0 85.0 90.0 93.0 97.5 104.0 111.0 118.0 122.0 127.5 132.0 140.0 146.0 150.0 156.0 161.0 168.0 173.5 180.0 189.0 196.0 202.0 210.0 220.0 230.0 242.0 255.0 268.5 280.0 290.0 298.5 304.5 312.0"; (timeout 1200 python3 tools/frames.py $T > out/frames-land.log 2>&1 &); timeout 1200 python3 tools/frames.py --portrait $T > out/frames-port.log 2>&1; sleep 6; tail -n1 out/frames-land.log; tail -n1 out/frames-port.log; python3 tools/sheets.pyok errors: none errors: none ['L00.png', 'L01.png', 'L02.png', 'L03.png', 'L04.png', 'L05.png', 'L06.png', 'L07.png', 'L08.png', 'L09.png', 'L10.png', 'P00.png', 'P01.png', 'P02.png', 'P03.png', 'P04.png', 'P05.png', 'P06.png', 'P07.png', 'P08.png', 'P09.png', 'P10.png', 'P11.png', 'P12.png', 'P13.png', 'P14.png']
读取 L00.png
{ "file_path": "~/ai-tree/out/sheets/L00.png" }[图片]
读取 L01.png
{ "file_path": "~/ai-tree/out/sheets/L01.png" }[图片]
读取 L02.png
{ "file_path": "~/ai-tree/out/sheets/L02.png" }[图片]
读取 L03.png
{ "file_path": "~/ai-tree/out/sheets/L03.png" }[图片]
读取 L04.png
{ "file_path": "~/ai-tree/out/sheets/L04.png" }[图片]
读取 L05.png
{ "file_path": "~/ai-tree/out/sheets/L05.png" }[图片]
思考：Analyzing timing details in the card display logic. Checking…
Analyzing timing details in the card display logic.
Checking label overlaps and adjusting timeline element positions.
Reviewing timeline pacing and tag visibility across animation phases.读取 L06.png
{ "file_path": "~/ai-tree/out/sheets/L06.png" }[图片]
读取 L07.png
{ "file_path": "~/ai-tree/out/sheets/L07.png" }[图片]
读取 L08.png
{ "file_path": "~/ai-tree/out/sheets/L08.png" }[图片]
读取 L09.png
{ "file_path": "~/ai-tree/out/sheets/L09.png" }[图片]
读取 L10.png
{ "file_path": "~/ai-tree/out/sheets/L10.png" }[图片]
思考：Reviewing the landscape video for timing and overlap issues.
Reviewing the landscape video for timing and overlap issues.
读取 P00.png
{ "file_path": "~/ai-tree/out/sheets/P00.png" }[图片]
读取 P01.png
{ "file_path": "~/ai-tree/out/sheets/P01.png" }[图片]
读取 P02.png
{ "file_path": "~/ai-tree/out/sheets/P02.png" }[图片]
读取 P03.png
{ "file_path": "~/ai-tree/out/sheets/P03.png" }[图片]
读取 P04.png
{ "file_path": "~/ai-tree/out/sheets/P04.png" }[图片]
读取 P05.png
{ "file_path": "~/ai-tree/out/sheets/P05.png" }[图片]
思考：Reviewing portrait frames for layout and labeling issues. Ch…
Reviewing portrait frames for layout and labeling issues.
Checking label collisions and fading overlaps across timeline frames.
Checking label placement near branch tips and trunk overlaps.
Spotting fading labels during timeline tag transitions.
Scanning timeline tags for lingering labels and layout timing.
Adjusting name fade-in timing for young branch labels.
Investigating why newer tags lose priority over older labels.
Tuning collision fade thresholds for new versus older tags.
Checking funnel label spacing against timeline tick marks.
Adjusting label position to clear tick overlaps.读取 P06.png
{ "file_path": "~/ai-tree/out/sheets/P06.png" }[图片]
读取 P07.png
{ "file_path": "~/ai-tree/out/sheets/P07.png" }[图片]
读取 P08.png
{ "file_path": "~/ai-tree/out/sheets/P08.png" }[图片]
读取 P09.png
{ "file_path": "~/ai-tree/out/sheets/P09.png" }[图片]
思考：Reviewing portrait layout tags and callout placements for is…
Reviewing portrait layout tags and callout placements for issues.
Checking stamp timing and funnel animation overlaps across frames.读取 P10.png
{ "file_path": "~/ai-tree/out/sheets/P10.png" }[图片]
读取 P11.png
{ "file_path": "~/ai-tree/out/sheets/P11.png" }[图片]
读取 P12.png
{ "file_path": "~/ai-tree/out/sheets/P12.png" }[图片]
读取 P13.png
{ "file_path": "~/ai-tree/out/sheets/P13.png" }[图片]
读取 P14.png
{ "file_path": "~/ai-tree/out/sheets/P14.png" }[图片]
思考：Reviewing screenshot layout and feed content details. Spotti…
Reviewing screenshot layout and feed content details.
Spotting overlapping cards and compiling final layout fixes.
Verifying hidden-label collisions before applying layout fixes.运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:80]); s = s.replace(old, new) # 1. branch names fade in shortly after the branch is born (not with its slow growth) rep("na = clamp((g - 0.36) / 0.18) * (wk > 0.5", "na = clamp((t - b.tBorn - 0.9) / 0.6) * (wk > 0.5") # 2. day card: the "last 52 minutes" label sits lower, clear of the hour labels rep(""" if (Dd.sliver === 'below-left') { text('最后 52 分钟', a - 30, Yb + 84, { ...hl, size: lab + 8, weight: 700, align: 'right' }); text('= 最近三年', a - 30, Yb + 84 + lab + 10, { ...hl, size: lab, align: 'right' }); }""", """ if (Dd.sliver === 'below-left') { text('最后 52 分钟', a - 30, Yb + 112, { ...hl, size: lab + 8, weight: 700, align: 'right' }); text('= 最近三年', a - 30, Yb + 112 + lab + 10, { ...hl, size: lab, align: 'right' }); }""") # 3. portrait: the news feed steps back while a term card sits on top of it rep("""function drawFeed(t) { const F = L.feed, notes = TL.notes, blow = clamp((t - ENDT - 0.2) / 1.6); if (blow >= 1) retu …（中间省略 715 字）… 7 },") # 5. a collision resolves decisively rep("s = Math.max(s, clamp((f - 0.03) * 12) * sstep((o.w8 - it.w8) / 0.3 + 0.5) * o.a);", "s = Math.max(s, clamp((f - 0.02) * 25) * sstep((o.w8 - it.w8) / 0.3 + 0.5) * o.a);") p.write_text(s); print('ok') PYEOF grep -n "drawFeed(t)" scene/film.js | head -3ok 925:function drawFeed(t) { 1210: drawFeed(t);思考：Building a music and sound effects script for the project. M…
Building a music and sound effects script for the project.
Mapping sound effects and music cues to timeline events.
Designing synthesized instruments for each musical texture.
Assigning sound effects to specific timeline moments and visuals.运行 ffprobe -v error -show_entries format=duration -of csv=p=0 audio/narration.wav …
ffprobe -v error -show_entries format=duration -of csv=p=0 audio/narration.wav && python3 -c " import json; s=open('scene/timeline.js').read(); TL=json.loads(s[len('window.TL = '):].rstrip().rstrip(';')) for x in TL['tempo']: print(x) print([ (k, [ (round(v['y'],3), v['v']) for v in vals]) for k, vals in TL['counters'].items()]) "314.200000 {'t0': 0.0, 't1': 11.368, 'bpm': 92, 'style': 'intro'} {'t0': 11.368, 't1': 47.551, 'bpm': 84, 'style': 'calm'} {'t0': 47.551, 't1': 50.909, 'bpm': 70, 'style': 'winter'} {'t0': 50.909, 't1': 68.821, 'bpm': 84, 'style': 'calm'} {'t0': 68.821, 't1': 74.245, 'bpm': 70, 'style': 'winter'} {'t0': 74.245, 't1': 91.521, 'bpm': 84, 'style': 'calm'} {'t0': 91.521, 't1': 153.252, 'bpm': 90, 'style': 'spring'} {'t0': 153.252, 't1': 166.656, 'bpm': 60, 'style': 'clock'} {'t0': 166.656, 't1': 200.28, 'bpm': 100, 'style': 'drive'} {'t0': 200.28, 't1': 240.384, 'bpm': 106, 'style': 'drive'} {'t0': 240.384, 't1': 286.952, 'bpm': 112, 'style': 'drive2'} {'t0': 286.952, 't1': 314.2, 'bpm': 76, 'style': 'end'} [('chatgpt', [(2023.847, 1.0), (2024.658, 2.0), (2024.924, 3.0), (2025.137, 4.0), (2025.244, 5.0), (2025.589, 7.0), (2025.762, 8.0), (2026.123, 9.0), (2026.534, 10.0), (2026.742, 12.0)]), ('nvidia', [(2024.0, 1.2), (2024.145, 2.0), (2024.426, 3.0), (2024.462, 3.3), (2025.071, 2.9), (2025.518, 4.0), (2025.825, 5.0)])]思考：Analyzing the short winter durations in the tempo map. Desig…
Analyzing the short winter durations in the tempo map.
Designing chord progressions and instrumentation for each section.
Sketching sound effects for clocks, swells, and transitions.
Mixing narration, music, and sound effects with ffmpeg filters.写入 audio.py
"""v2 sound: a procedural score that follows the film's tempo map, a sound-effects track keyed to on-screen events, and the final mix (music ducks under the narration; effects duck lightly; two-pass loudness normalisation to -16 LUFS). Inputs : scene/timeline.js, audio/narration.wav Outputs: audio/music.wav, audio/sfx.wav, audio/mix.wav """ import json, re, subprocess from pathlib import Path import numpy as np from scipy.io import wavfile from scipy.signal import fftconvolve, butter, sosfilt ROOT = Path(__file__).resolve().parent.parent SR = 48000 TL = json.loads((ROOT / "scene" / "timeline.js").read_text()[len("window.TL = "):].rstrip().rstrip(";")) TOTAL, M = TL["total"], TL["marks"] B = {b["id"]: b for b in TL["beats"]} YK = TL["ykeys"] N = int((TOTAL + 4) * SR) rng = np.random.default_rng(11) def t_of_y(y): for (ta, ya), (tb, yb) in zip(YK, YK[1:]): if ya <= y <= yb and yb > ya: return ta + (y - ya) / (yb - ya) * (tb - ta) return None class Bus: def __init__(self): self.L = np.zeros(N); self.R = np.zeros(N) def add(self, sig, t, pan=0.0, gain=1.0): …（中间省略 16655 字）… true") subprocess.run(["ffmpeg", "-y", "-loglevel", "error", *ins, "-filter_complex", FILTER + "," + ln + "[out]", "-map", "[out]", "-ar", "48000", str(ROOT / "audio" / "mix.wav")], check=True) print(f"music + sfx + mix written ({TOTAL:.1f}s); input loudness {meas['input_i']} LUFS")File created successfully at: ~/ai-tree/tools/audio.py (file state is current in your context — no need to Read it back)
运行 time python3 tools/audio.py 2>&1 | tail -5 && ffmpeg -hide_banner -nostats -i a…
time python3 tools/audio.py 2>&1 | tail -5 && ffmpeg -hide_banner -nostats -i audio/mix.wav -af ebur128=peak=true -f null - 2>&1 | grep -A 12 "Summary" | head -14 && ffprobe -v error -show_entries format=duration -of csv=p=0 audio/mix.wav
music + sfx + mix written (314.2s); input loudness -18.43 LUFS real 0m48.420s user 0m39.650s sys 0m7.871s [Parsed_ebur128_0 @ 0x558fd0e7dac0] Summary: Integrated loudness: I: -16.2 LUFS Threshold: -26.8 LUFS Loudness range: LRA: 5.8 LU Threshold: -36.8 LUFS LRA low: -20.3 LUFS LRA high: -14.5 LUFS True peak: 314.200000思考：Checking mix loudness and balancing sound effect levels.
Checking mix loudness and balancing sound effect levels.
运行 ffmpeg -hide_banner -nostats -i audio/mix.wav -af ebur128=peak=true -f null - 2…
ffmpeg -hide_banner -nostats -i audio/mix.wav -af ebur128=peak=true -f null - 2>&1 | grep -A 3 "True peak" | head -4; python3 - <<'EOF' import numpy as np, json, subprocess from scipy.io import wavfile def load(p, g): sr, x = wavfile.read(p); x = x.astype(np.float64) / 32768 if x.ndim > 1: x = x.mean(axis=1) if sr != 48000: import scipy.signal as ss; x = ss.resample_poly(x, 48000, sr) return x * g v = load('audio/narration.wav', 2.0); m = load('audio/music.wav', 0.34); s = load('audio/sfx.wav', 0.62) n = min(len(v), len(m), len(s)); v, m, s = v[:n], m[:n], s[:n] def rms_db(x): return 20 * np.log10(np.sqrt(np.mean(x ** 2)) + 1e-9) W = 4800 def frames(x): k = len(x) // W; return np.sqrt((x[:k * W].reshape(k, W) ** 2).mean(axis=1)) fv, fm, fs = frames(v), frames(m), frames(s) speaking = fv > 0.02 print("voice RMS while speaking dB:", round(20 * np.log10(np.median(fv[speaking])), 1)) print("music RMS (pre-duck) median dB:", round(20 * np.log10(np.median(fm) + 1e-9), 1)) loud = fs > 0.002 print("sfx 100ms-window peaks: p50 %.1f p90 %.1f max %.1f dB" % tuple(20 * np.log10(np.percentile(fs[loud], q)) for q in (50, 90, 100))) idx = np.argsort(fs)[-12:][::-1] print("loudest sfx windows (s, dB):", [(round(i * W / 48000, 1), round(20 * np.log10(fs[i]), 1)) for i in idx]) EOFTrue peak: Peak: -1.5 dBFS voice RMS while speaking dB: -22.9 music RMS (pre-duck) median dB: -35.5 sfx 100ms-window peaks: p50 -42.8 p90 -33.6 max -17.8 dB loudest sfx windows (s, dB): [(np.float64(166.7), np.float64(-17.8)), (np.float64(200.3), np.float64(-18.8)), (np.float64(240.4), np.float64(-19.3)), (np.float64(166.8), np.float64(-19.9)), (np.float64(200.4), np.float64(-20.0)), (np.float64(240.5), np.float64(-20.0)), (np.float64(240.6), np.float64(-21.5)), (np.float64(145.2), np.float64(-22.0)), (np.float64(200.5), np.float64(-22.3)), (np.float64(298.0), np.float64(-24.3)), (np.float64(145.3), np.float64(-24.9)), (np.float64(166.9), np.float64(-25.3))]思考：Checking the music-to-voice volume balance.
Checking the music-to-voice volume balance.
运行 python3 - <<'EOF'
python3 - <<'EOF' import numpy as np, json from scipy.io import wavfile TL = json.loads(open('scene/timeline.js').read()[len("window.TL = "):].rstrip().rstrip(";")) sr, m = wavfile.read('audio/music.wav'); m = m.astype(np.float64).mean(axis=1) / 32768 * 0.34 for sec in TL['tempo']: a, b = int(sec['t0'] * sr), int(sec['t1'] * sr) x = m[a:b] print(f"{sec['style']:7s} {sec['t0']:6.1f}-{sec['t1']:6.1f} rms {20*np.log10(np.sqrt(np.mean(x**2))+1e-9):6.1f} dB peak {20*np.log10(np.max(np.abs(x))+1e-9):6.1f} dB") EOFintro 0.0- 11.4 rms -36.5 dB peak -18.8 dB calm 11.4- 47.6 rms -35.9 dB peak -23.2 dB winter 47.6- 50.9 rms -35.8 dB peak -22.8 dB calm 50.9- 68.8 rms -35.6 dB peak -23.2 dB winter 68.8- 74.2 rms -37.0 dB peak -21.7 dB calm 74.2- 91.5 rms -36.5 dB peak -23.7 dB spring 91.5- 153.3 rms -34.9 dB peak -20.3 dB clock 153.3- 166.7 rms -43.7 dB peak -26.2 dB drive 166.7- 200.3 rms -33.3 dB peak -18.5 dB drive 200.3- 240.4 rms -32.3 dB peak -19.4 dB drive2 240.4- 287.0 rms -32.6 dB peak -19.3 dB end 287.0- 314.2 rms -38.5 dB peak -22.8 dB
思考：Checking section loudness levels across the mix.
Checking section loudness levels across the mix.
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('tools/audio.py'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:70]); s = s.replace(old, new) rep('"clock": 0.12, "drive": 0.12', '"clock": 0.17, "drive": 0.12') rep("music.add(epiano(notes[idx] + 12, 2.0, 0.7), t + qd * beat, 0.2 * (idx - 1), 0.11 * gain)", "music.add(epiano(notes[idx] + 12, 2.0, 0.7), t + qd * beat, 0.2 * (idx - 1), 0.16 * gain)") rep(" sfx.add(stamp(1.3), M[k] + 0.02, 0, 0.6)", " sfx.add(stamp(1.3), M[k] + 0.02, 0, 0.34)") rep('"[1:a]volume=0.34[m]', '"[1:a]volume=0.45[m]') p.write_text(s); print('ok') PYEOF python3 tools/audio.py 2>&1 | tail -2 && ffmpeg -hide_banner -nostats -i audio/mix.wav -af ebur128=peak=true -f null - 2>&1 | grep -E "^\s+(I|Peak):" | head -3ok music + sfx + mix written (314.2s); input loudness -18.72 LUFS I: -16.2 LUFS Peak: -1.5 dBFS运行 rm -f out/frames/*.png; T="40.0 161.5 173.5 189.0 196.0"; (timeout 600 python3 …
rm -f out/frames/*.png; T="40.0 161.5 173.5 189.0 196.0"; (timeout 600 python3 tools/frames.py $T > out/frames-land.log 2>&1 &); timeout 600 python3 tools/frames.py --portrait $T > out/frames-port.log 2>&1; sleep 3; tail -n1 out/frames-land.log; tail -n1 out/frames-port.log; python3 tools/sheets.py
errors: none errors: none ['L00.png', 'L01.png', 'P00.png', 'P01.png']
读取 L00.png
{ "file_path": "~/ai-tree/out/sheets/L00.png" }[图片]
读取 P00.png
{ "file_path": "~/ai-tree/out/sheets/P00.png" }[图片]
读取 P01.png
{ "file_path": "~/ai-tree/out/sheets/P01.png" }[图片]
思考：Designing a timeline poster with overlapping label placement…
Designing a timeline poster with overlapping label placement.
Estimating full render times for both orientations.运行 git add -A scene tools audio/voice script .gitignore package.json 2>/dev/null; …
git add -A scene tools audio/voice script .gitignore package.json 2>/dev/null; git status --short | head -30; git -c user.name="Claude" -c user.email="[邮箱]" commit -q -m "v2: principle layer (narration, two-layer term cards with mechanism animations), real names on tags, landscape + portrait layouts, collision-aware labels, new score and sound effects Co-Authored-By: Claude Opus 5.5 <[邮箱]> Claude-Session: https://claude.ai/code/session_01HZH8EiUQAY3dtZbkE3ysZz" && git log --oneline | head -3
M audio/mix.wav M audio/music.wav M audio/narration.wav M audio/voice/s00.mp3 M audio/voice/s02.mp3 M audio/voice/s03.mp3 M audio/voice/s05.mp3 M audio/voice/s06.mp3 M audio/voice/s07.mp3 M audio/voice/s08.mp3 M audio/voice/s09.mp3 M audio/voice/timing.json A audio/voice/v2.0/s02.mp3 A audio/voice/v2.0/s03.mp3 A audio/voice/v2.0/s05.mp3 A audio/voice/v2.0/s06.mp3 A audio/voice/v2.0/s08.mp3 A audio/voice/v2.0/s09.mp3 A audio/voice/v2.0/timing.json M scene/art.js M scene/film.html M scene/film.js M scene/lib.js A scene/mech.js M scene/timeline.js A tools/audio.py M tools/build_timeline.py A tools/cards.py M tools/frames.py A tools/qa_layout.py 0152c83 v2: principle layer (narration, two-layer term cards with mechanism animations), real names on tags, landscape + portrait layouts, collision-aware labels, new score and sound effects 7e5a868 v1.1: cap young branch length; fix two note dates 52f7100 v1: first full preview (hand-drawn tree, 4:10, narration + procedural music)
思考：Investigating why audio files appear modified but unstaged.
Investigating why audio files appear modified but unstaged.
更新待办
{ "status": "completed", "taskId": "14" }Updated task #14 status
更新待办
{ "status": "completed", "taskId": "15" }Updated task #15 status
更新待办
{ "status": "completed", "taskId": "16" }Updated task #16 status
更新待办
{ "status": "completed", "taskId": "17" }Updated task #17 status
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{ "status": "completed", "taskId": "18" }Updated task #18 status
更新待办
{ "status": "completed", "taskId": "12" }Updated task #12 status
更新待办
{ "status": "in_progress", "taskId": "13" }Updated task #13 status
运行 mkdir -p out/v2 && nohup sh -c 'python3 tools/render.py --fps 24 --workers 2 --…
mkdir -p out/v2 && nohup sh -c 'python3 tools/render.py --fps 24 --workers 2 --audio audio/mix.wav --out out/v2/ai-tree-v2-landscape-1080p.mp4 > out/render-land.log 2>&1; python3 tools/render.py --portrait --fps 24 --workers 2 --audio audio/mix.wav --out out/v2/ai-tree-v2-portrait-1080p.mp4 > out/render-port.log 2>&1' > /dev/null 2>&1 & sleep 60; cat out/render-land.log
worker 1: 0/3771 frames (0s) worker 0: 0/3770 frames (0s) worker 1: 240/3771 frames (23s) worker 0: 240/3770 frames (30s) Process Process-1: Traceback (most recent call last): File "/usr/lib/python3.13/multiprocessing/process.py", line 313, in _bootstrap self.run() ~~~~~~~~^^ File "/usr/lib/python3.13/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "~/ai-tree/tools/render.py", line 30, in worker d = page.evaluate(f"(() => {{ seek({i / fps}); return document.getElementById('c').toDataURL('image/jpeg', 0.93); }})()") File "/usr/local/lib/python3.13/dist-packages/playwright/sync_api/_generated.py", line 8531, in evaluate self._sync( ~~~~~~~~~~^ self._impl_obj.evaluate(expression=expression, arg=mapping.to_impl(arg)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "/usr/local/lib/python3.13/dist-packages/playwright/_impl/_sync_base.py", line 115, in _sync return task.result() ~~~~~~~~~~~^^ File "/usr/local/lib/python3.13/dist-packages/playwright/_impl/_page.py", line 467, in evaluate return await self._main_frame.evaluate(expression, arg) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.13/dist-packages/playwright/_impl/_frame.py", line 320, in evaluate await self._channel.send( ...<6 lines>... ) File "/usr/local/lib/p …（中间省略 426 字）… e.evaluate: SecurityError: Failed to execute 'toDataURL' on 'HTMLCanvasElement': Tainted canvases may not be exported. at eval (eval at evaluate (:290:30), <anonymous>:1:72) at eval (eval at evaluate (:290:30), <anonymous>:1:105) at eval (<anonymous>) at UtilityScript.evaluate (<anonymous>:290:30) at UtilityScript.<anonymous> (<anonymous>:1:44) worker 1: 480/3771 frames (51s)思考：Fixing a canvas cross-origin tainting issue with photo rende…
Fixing a canvas cross-origin tainting issue with photo rendering.
运行 pkill -f "tools/render.py" ; sleep 2; pkill -f chrome-linux/chrome; sleep 1; ps…
pkill -f "tools/render.py" ; sleep 2; pkill -f chrome-linux/chrome; sleep 1; ps aux | grep -c "[r]ender.py"; cd ~/ai-tree && python3 - <<'PYEOF' from pathlib import Path p = Path('tools/render.py'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:70]); s = s.replace(old, new) rep("import argparse, base64, json, subprocess, sys, time", "import argparse, base64, functools, json, subprocess, sys, threading, time\nfrom http.server import ThreadingHTTPServer, SimpleHTTPRequestHandler") rep("def worker(k, frames, fps, part, scale, portrait):", "def worker(k, frames, fps, part, scale, portrait, base):") rep(""" page.goto((ROOT / "scene" / "film.html").as_uri() + ("?v=portrait" if portrait else ""))""", """ page.goto(f"{base}/scene/film.html" + ("?v=portrait" if portrait else "")) # served over http so photos don't taint the canvas""") rep(""" t0 = time.time() procs = [Process(target=worker, args=(k, chunks[k], a.fps, parts[k], a.scale, a.portrait)) for k in range(a.workers)]""", """ srv = ThreadingHTTPServer(("127.0.0.1", 0), functool …（中间省略 326 字）… a.scale, a.portrait, base)) for k in range(a.workers)]""") p.write_text(s); print('ok') PYEOF timeout 120 python3 tools/render.py --start 12.0 --end 14.5 --workers 1 --audio none --out out/test-photo.mp4 2>&1 | tail -2 && ffprobe -v error -show_entries format=duration -of csv=p=0 out/test-photo.mp4Exit code 144
运行 grep -n "ThreadingHTTPServer\|base}" tools/render.py | head; ps aux | grep -c "…
grep -n "ThreadingHTTPServer\|base}" tools/render.py | head; ps aux | grep -c "[r]ender.py"
1
运行 ps aux | grep -E "[r]ender\.py|[c]hrome-linux" | awk '{print $2, $11, $12, $13,…
ps aux | grep -E "[r]ender\.py|[c]hrome-linux" | awk '{print $2, $11, $12, $13, $14, $15}' | head(Bash completed with no output)
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('tools/render.py'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:70]); s = s.replace(old, new) rep("import argparse, base64, json, subprocess, sys, time", "import argparse, base64, functools, json, subprocess, sys, threading, time\nfrom http.server import ThreadingHTTPServer, SimpleHTTPRequestHandler") rep("def worker(k, frames, fps, part, scale, portrait):", "def worker(k, frames, fps, part, scale, portrait, base):") rep(""" page.goto((ROOT / "scene" / "film.html").as_uri() + ("?v=portrait" if portrait else ""))""", """ page.goto(f"{base}/scene/film.html" + ("?v=portrait" if portrait else "")) # served over http so photos don't taint the canvas""") rep(""" t0 = time.time() procs = [Process(target=worker, args=(k, chunks[k], a.fps, parts[k], a.scale, a.portrait)) for k in range(a.workers)]""", """ srv = ThreadingHTTPServer(("127.0.0.1", 0), functools.partial(type("Quiet", (SimpleHTTPRequestHandler,), {"log_message": lambda *x: None}), directory=str(ROOT))) threading.T …（中间省略 199 字）… ], a.scale, a.portrait, base)) for k in range(a.workers)]""") p.write_text(s); print('ok') PYEOF timeout 150 python3 tools/render.py --start 12.0 --end 14.5 --workers 1 --audio none --out out/test-photo.mp4 2>&1 | tail -2; ffprobe -v error -show_entries format=duration -of csv=p=0 out/test-photo.mp4ok worker 0: 0/60 frames (0s) done: out/test-photo.mp4 (60 frames in 6s) 2.500000
运行 ffmpeg -y -loglevel error -ss 2.0 -i out/test-photo.mp4 -frames:v 1 -vf scale=9…
ffmpeg -y -loglevel error -ss 2.0 -i out/test-photo.mp4 -frames:v 1 -vf scale=960:-1 out/frames/test-photo.png && rm -f out/v2/*.mp4 && nohup sh -c 'python3 tools/render.py --fps 24 --workers 2 --audio audio/mix.wav --out out/v2/ai-tree-v2-landscape-1080p.mp4 > out/render-land.log 2>&1; python3 tools/render.py --portrait --fps 24 --workers 2 --audio audio/mix.wav --out out/v2/ai-tree-v2-portrait-1080p.mp4 > out/render-port.log 2>&1' > /dev/null 2>&1 & sleep 5; echo started
started
读取 test-photo.png
{ "file_path": "~/ai-tree/out/frames/test-photo.png" }[图片]
思考：Producing a video render while updating project documentatio…
Producing a video render while updating project documentation.
运行 ls -la ~/ai-tree/out/v1/ && cat ~/ai-tree/out/render-land…
ls -la ~/ai-tree/out/v1/ && cat ~/ai-tree/out/render-land.log | tail -3
total 183620 drwxr-xr-x 2 root root 4096 Oct 15 02:07 . drwxr-xr-x 7 root root 4096 Oct 15 04:20 .. -rw-r--r-- 1 root root 159788475 Oct 14 09:35 ai-tree-v1-1080p.mp4 -rw-r--r-- 1 root root 28223498 Oct 14 09:45 ai-tree-v1-720p.mp4 worker 0: 0/3770 frames (0s) worker 1: 0/3771 frames (0s)
思考：Estimating file sizes for chunked video delivery. Weighing c…
Estimating file sizes for chunked video delivery.
Weighing compression tradeoffs for phone-friendly sharing versions.
Verifying historical AI milestone dates for the timeline script.
Reassessing wording precision for the backprop narration detail.
Deciding whether to delay rendering for a narration tweak.运行 sleep 240; tail -n 3 ~/ai-tree/out/render-land.log
sleep 240; tail -n 3 ~/ai-tree/out/render-land.log
worker 1: 1920/3771 frames (258s) worker 0: 2400/3770 frames (290s) worker 1: 2160/3771 frames (293s)
写入 script-v2.md
# AI 这棵树 · 旁白脚本 v2（2026-10-15） 总长约 5 分 14 秒。开场约 11 秒；前半段 1950–2023 约 2 分 22 秒；后半段 2024–2026.10 约 2 分 14 秒，占正片约一半；结尾约 27 秒。 v2 相对 v1 的变化：每个关键名词都加了一句讲原理的旁白，画面上的词卡分两层（大白话 + 原理）并配会动的示意图；树上的里程碑改回真名加白话；横屏、竖屏两版。 ## 开场（倒叙钩子） 画面：快进到 2026 年枝繁叶茂的树，停一下，倒带回到一颗种子；标题「AI 这棵树」。 旁白：这是一棵树的故事。它长了七十多年，最近这三年，突然爆发了。 ## 第一部分：慢慢长大 ### s01 · 1950–1956 种子 旁白：1950年，图灵问：机器能思考吗？六年后，一群科学家在达特茅斯开会，给这件事起了个名字——人工智能。 词卡：人工智能 / 来历：1956 年达特茅斯会议上，第一次起了这个名字。 ### s02 · 1958–1969 冻住的芽 旁白：1958年，有人造出了模仿大脑的"感知机"，这是神经网络的第一颗芽。**每个"神经元"把输入乘上权重、加起来；所谓学习，就是不断调这些权重。**可十来年后，它被一本书泼了冷水，冻住了。 词卡：神经网络 / 原理：输入 × 权重，加起来；学习 = 不断调这些权重（示意图：三个输入经过"权重旋钮"汇入 Σ，够大就点亮）。 ### s03 · 写规则，两次寒冬，反向传播 旁白：那时的做法，是把规则一条条写给电脑。说得太多、做到太少，钱被砍了，第一个冬天来了。八十年代，"专家系统"把专家的经验写成"如果……就……"，风光一时。**1986年，冻住的那颗芽也开始化冻："反向传播"出现了——答错了，就把误差从后往前传，算出每个权重该往哪边拧一点。**而专家系统的规则越写越难维护，第二个冬天又来了。 词卡：专家系统 / 原理：知识库 + 推理机；反向传播 / 原理：误差从后往前传，梯度下降（示意图：答错 → 红色误差倒流 → 答对）。 ### s04 · 找规律 旁白：后来换了条路：不写规则了，让电脑从大量例子里自己找规律，这叫"机器学习"。1997年，每秒能算两亿步的深蓝，赢了国际象棋世界冠军。 词卡：机器学习 / 原理：用数据训练出模型，再判断没见过的新例子（示意图：一条线学会把两类点分开）。 ### s05 · 2012–2017 春天 旁白：2012年，那颗冻了几十年的芽突然开花：很深的神经网络，喂进海量图片，用两块打游戏的显卡来算——这就是"深度学习"。**前几层学会认边缘，中间几层认形状，最后几层认出整只猫。** …（中间省略 1630 字）… 电脑（1987 后退场） 下棋：1997 深蓝 · 胜棋王卡斯帕罗夫 / 2016 AlphaGo · 胜李世石 听懂说话：1997 Dragon · 听写软件 / 2011 Siri · 语音助手 看图：1998 LeNet · 认手写数字 / 2012 AlexNet · 认图大跃进 画画·创作：2014 GAN · 两个网络对练画画 / 2022 扩散模型 · 一句话出图 搞科研：2020 AlphaFold · 猜出蛋白质结构 树冠：ChatGPT · 推理模型 · DeepSeek · 智能体 · 多模态 树干：写规则 → 找规律 → 神经网络 → 大模型（每次换"材质"时有标注）
File created successfully at: ~/ai-tree/script/script-v2.md (file state is current in your context — no need to Read it back)
思考：Weighing a wording change against the rework it would requir…
Weighing a wording change against the rework it would require.
核对时间线中各历史细节的准确性。
核对近两年AI重大事件的细节准确性。运行 sleep 280; tail -n 2 ~/ai-tree/out/render-land.log; ls -la ~
sleep 280; tail -n 2 ~/ai-tree/out/render-land.log; ls -la ~/ai-tree/out/v2/ 2>/dev/null
worker 1: 3600/3771 frames (499s) done: out/v2/ai-tree-v2-landscape-1080p.mp4 (7541 frames in 525s) total 174524 drwxr-xr-x 2 root root 4096 Oct 15 04:28 . drwxr-xr-x 8 root root 4096 Oct 15 04:29 .. -rw-r--r-- 1 root root 178697414 Oct 15 04:29 ai-tree-v2-landscape-1080p.mp4
运行 mkdir -p out/sweep && rm -f out/sweep/*.png && ffmpeg -loglevel error -i out/v2…
mkdir -p out/sweep && rm -f out/sweep/*.png && ffmpeg -loglevel error -i out/v2/ai-tree-v2-landscape-1080p.mp4 -vf "fps=1/4,scale=640:360" out/sweep/s%03d.png && ls out/sweep | wc -l && python3 - <<'EOF' from PIL import Image, ImageDraw from pathlib import Path fr = sorted(Path('~/ai-tree/out/sweep').glob('s*.png')) cols, per = 3, 12 for k in range(0, len(fr), per): grp = fr[k:k+per]; rows = (len(grp) + cols - 1) // cols S = Image.new('RGB', (cols * 640, rows * 380), 'white'); d = ImageDraw.Draw(S) for i, f in enumerate(grp): x, y = (i % cols) * 640, (i // cols) * 380 S.paste(Image.open(f), (x, y + 20)); n = int(f.stem[1:]); d.text((x + 4, y + 4), f"{(n - 1) * 4 + 2:.0f}s", fill='red') S.save(f'~/ai-tree/out/sweep/sheet{k // per:02d}.png') print('ok') EOF ls out/sweep/sheet*79 ok out/sweep/sheet00.png out/sweep/sheet01.png out/sweep/sheet02.png out/sweep/sheet03.png out/sweep/sheet04.png out/sweep/sheet05.png out/sweep/sheet06.png
读取 sheet00.png
{ "file_path": "~/ai-tree/out/sweep/sheet00.png" }[图片]
读取 sheet01.png
{ "file_path": "~/ai-tree/out/sweep/sheet01.png" }[图片]
读取 sheet02.png
{ "file_path": "~/ai-tree/out/sweep/sheet02.png" }[图片]
读取 sheet03.png
{ "file_path": "~/ai-tree/out/sweep/sheet03.png" }[图片]
读取 sheet04.png
{ "file_path": "~/ai-tree/out/sweep/sheet04.png" }[图片]
读取 sheet05.png
{ "file_path": "~/ai-tree/out/sweep/sheet05.png" }[图片]
读取 sheet06.png
{ "file_path": "~/ai-tree/out/sweep/sheet06.png" }[图片]
思考：Reviewing the animation timeline for rendering issues. Worki…
Reviewing the animation timeline for rendering issues.
Working out file sizes for the video delivery.
Weighing chunked transfers against smaller re-encodes for delivery.
Avoiding deletion while planning temporary chunk file cleanup.
Considering whether the Mac's ffmpeg could simplify delivery.运行 tail -n 2 ~/ai-tree/out/render-port.log; which ffmpeg; ffmpeg -hide_…
tail -n 2 ~/ai-tree/out/render-port.log; which ffmpeg; ffmpeg -hide_banner -encoders 2>/dev/null | grep -E "libx264|libx265|aac" | head
worker 1: 1440/3771 frames (216s) worker 0: 1680/3770 frames (223s) /usr/bin/ffmpeg V....D libx264 libx264 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10 (codec h264) V....D libx264rgb libx264 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10 RGB (codec h264) V....D libx265 libx265 H.265 / HEVC (codec hevc) A....D aac AAC (Advanced Audio Coding)
运行 which ffmpeg; ls -la ~/Movies/ai-tree-video/; df -h ~
which ffmpeg; ls -la ~/Movies/ai-tree-video/; df -h ~/Movies | tail -1
Process started with PID 25266 (shell: /bin/zsh) Initial output: /opt/homebrew/bin/ffmpeg total 55128 drwxr-xr-x@ 6 niracler staff 192 Oct 15 10:07 . drwx------ 6 niracler staff 192 Oct 14 16:00 .. drwxr-xr-x@ 7 niracler staff 224 Oct 14 16:00 .venv -rw-------@ 1 niracler staff 28223498 Oct 14 17:46 ai-tree-v1-720p.mp4 drwxr-xr-x@ 9 niracler staff 288 Oct 15 10:08 photos drwxr-xr-x@ 20 niracler staff 640 Oct 15 11:56 voice /dev/disk3s5 460Gi 404Gi 27Gi 94% 5.8M 282M 2% /System/Volumes/Data ✅ Process 25266 has finished execution
思考：Planning video export sizes and formats for delivery. Weighi…
Planning video export sizes and formats for delivery.
Weighing how to transfer high-quality files without leaving clutter.
Choosing resolutions and filenames for the finished exports.
Verifying historical AI milestones in the new script content.
Cross-checking later AI milestones and core concept summaries.
Fixing attention weight percentages and planning a precise video splice.
Rebalancing attention weights and planning seamless video patching.运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path m = Path('scene/mech.js'); t = m.read_text() old = "words = [['小猫', -74, 0.95], ['喝完', -26, 0.2], ['牛奶', 22, 0.35], ['因为', 66, 0.15]];" assert t.count(old) == 1 t = t.replace(old, "words = [['小猫', -74, 0.70], ['喝完', -26, 0.08], ['牛奶', 22, 0.17], ['因为', 66, 0.05]]; // attention weights sum to 100%") m.write_text(t); print('ok') PYEOF tail -n 1 out/render-port.logok worker 1: 1920/3771 frames (285s)
思考：Preparing a delivery script while the portrait render finish…
Preparing a delivery script while the portrait render finishes.
写入 deliver.py
"""Final encodes for delivery, optionally splicing in re-rendered patches (frame-exact). usage: python3 tools/deliver.py SRC.mp4 NAME [--patch PATCH.mp4:START:END ...] [--portrait] writes out/v2/NAME-1080p.mp4 (x264 slow, CRF 20, for presenting) and out/v2/NAME-720p.mp4 (2-pass, under 28 MiB, for chat apps) """ import argparse, subprocess, json from pathlib import Path ROOT = Path(__file__).resolve().parent.parent FPS = 24 ap = argparse.ArgumentParser() ap.add_argument("src"); ap.add_argument("name") ap.add_argument("--patch", action="append", default=[]) ap.add_argument("--portrait", action="store_true") ap.add_argument("--max-mib", type=float, default=27.5) a = ap.parse_args() out = ROOT / "out" / "v2"; out.mkdir(parents=True, exist_ok=True) dur = float(subprocess.check_output(["ffprobe", "-v", "error", "-show_entries", "format=duration", "-of", "csv=p=0", a.src]).decode()) patches = [] for p in a.patch: f, s, e = p.rsplit(":", 2) patches.append((f, round(float(s) * FPS), round(float(e) * FPS))) patches.sort(key=lambda x: x[1]) ins = ["-i", a.src] + sum((["-i", f] for f, _, _ in patches), []) i …（中间省略 1879 字）… "-of", "json", str(f)]).decode()) print(f.name, f"{int(info['format']['size']) / 1024 / 1024:.1f} MiB", info["streams"][0]["width"], "x", info["streams"][0]["height"], "frames", info["streams"][0].get("nb_frames"), "dur", info["format"]["duration"])File created successfully at: ~/ai-tree/tools/deliver.py (file state is current in your context — no need to Read it back)
运行 sleep 200; tail -n 2 ~/ai-tree/out/render-port.log
sleep 200; tail -n 2 ~/ai-tree/out/render-port.log
worker 1: 3600/3771 frames (514s) done: out/v2/ai-tree-v2-portrait-1080p.mp4 (7541 frames in 539s)
运行 mkdir -p out/master && mv out/v2/ai-tree-v2-landscape-1080p.mp4 out/master/land…
mkdir -p out/master && mv out/v2/ai-tree-v2-landscape-1080p.mp4 out/master/landscape.mp4 && mv out/v2/ai-tree-v2-portrait-1080p.mp4 out/master/portrait.mp4 && (python3 tools/render.py --start 114.5 --end 127.0 --workers 1 --audio none --out out/master/patch-land.mp4 > out/patch-land.log 2>&1 &) && python3 tools/render.py --portrait --start 114.5 --end 127.0 --workers 1 --audio none --out out/master/patch-port.mp4 > out/patch-port.log 2>&1; sleep 3; tail -n1 out/patch-land.log out/patch-port.log 2>/dev/null; ls -la out/master
==> out/patch-land.log <== done: out/master/patch-land.mp4 (300 frames in 41s) ==> out/patch-port.log <== done: out/master/patch-port.mp4 (300 frames in 40s) total 339728 drwxr-xr-x 2 root root 4096 Oct 15 04:38 . drwxr-xr-x 10 root root 4096 Oct 15 04:38 .. -rw-r--r-- 1 root root 178697414 Oct 15 04:29 landscape.mp4 -rw-r--r-- 1 root root 7601563 Oct 15 04:38 patch-land.mp4 -rw-r--r-- 1 root root 5808813 Oct 15 04:38 patch-port.mp4 -rw-r--r-- 1 root root 155751174 Oct 15 04:38 portrait.mp4
运行 rm -f out/v2/* && time python3 tools/deliver.py out/master/landscape.mp4 ai-tre…
rm -f out/v2/* && time python3 tools/deliver.py out/master/landscape.mp4 ai-tree-v2-landscape --patch out/master/patch-land.mp4:114.5:127.0 2>&1 | tail -4
Exit code 143 Command timed out after 10m 0s
运行 pkill -f deliver.py; pkill -f "ffmpeg -y -loglevel error -i out/master"; sleep …
pkill -f deliver.py; pkill -f "ffmpeg -y -loglevel error -i out/master"; sleep 1; rm -f out/v2/*; nohup sh -c 'python3 tools/deliver.py out/master/landscape.mp4 ai-tree-v2-landscape --patch out/master/patch-land.mp4:114.5:127.0 > out/deliver-land.log 2>&1; python3 tools/deliver.py out/master/portrait.mp4 ai-tree-v2-portrait --portrait --patch out/master/patch-port.mp4:114.5:127.0 > out/deliver-port.log 2>&1' > /dev/null 2>&1 & sleep 2; echo started
Exit code 144
运行 ps aux | grep -E "[d]eliver.py|[f]fmpeg" | awk '{print $2, $11, $12, $13, $14}'…
ps aux | grep -E "[d]eliver.py|[f]fmpeg" | awk '{print $2, $11, $12, $13, $14}' | head; ls -la ~/ai-tree/out/v2/total 146304 drwxr-xr-x 2 root root 4096 Oct 15 04:48 . drwxr-xr-x 10 root root 4096 Oct 15 04:38 .. -rw-r--r-- 1 root root 145521034 Oct 15 04:47 ai-tree-v2-landscape-1080p.mp4 -rw-r--r-- 1 root root 4283871 Oct 15 04:49 ai-tree-v2-landscape-720p.mp4
思考：Debugging a failed restart after a process kill.
Debugging a failed restart after a process kill.
运行 ffprobe -v error -show_entries format=duration:stream=nb_frames,codec_type -of …
ffprobe -v error -show_entries format=duration:stream=nb_frames,codec_type -of compact out/v2/ai-tree-v2-landscape-1080p.mp4
stream|codec_type=video|nb_frames=7539 stream|codec_type=audio|nb_frames=14730 format|duration=314.218000
思考：Tracking down a frame-count mismatch in the video splice.
Tracking down a frame-count mismatch in the video splice.
运行 for f in out/master/landscape.mp4 out/master/portrait.mp4 out/master/patch-land…
for f in out/master/landscape.mp4 out/master/portrait.mp4 out/master/patch-land.mp4 out/master/patch-port.mp4; do echo "$f $(ffprobe -v error -select_streams v -show_entries stream=nb_frames,r_frame_rate,duration -of csv=p=0 $f)"; done
out/master/landscape.mp4 24/1,314.125000,7539 out/master/portrait.mp4 24/1,314.125000,7539 out/master/patch-land.mp4 24/1,12.500000,300 out/master/patch-port.mp4 24/1,12.500000,300
思考：Investigating a frame-count mismatch after concatenating vid…
Investigating a frame-count mismatch after concatenating video parts.
运行 for f in out/parts/part0.mp4 out/parts/part1.mp4 out/parts/silent.mp4 out/parts…
for f in out/parts/part0.mp4 out/parts/part1.mp4 out/parts/silent.mp4 out/parts-p/part0.mp4 out/parts-p/part1.mp4; do echo "$f $(ffprobe -v error -select_streams v -count_frames -show_entries stream=nb_frames,nb_read_frames -of csv=p=0 $f)"; done
out/parts/part0.mp4 300,300 out/parts/part1.mp4 3771,3771 out/parts/silent.mp4: No such file or directory out/parts/silent.mp4 out/parts-p/part0.mp4 300,300 out/parts-p/part1.mp4 3771,3771
思考：Untangling conflicting render paths that overwrote a video p…
Untangling conflicting render paths that overwrote a video part.
Checking whether frame drops land at the boundary or the end.运行 python3 tools/frames.py 200.0 > /dev/null 2>&1; for n in 4799 4800 4801 4802; d…
python3 tools/frames.py 200.0 > /dev/null 2>&1; for n in 4799 4800 4801 4802; do ffmpeg -loglevel error -y -i out/master/landscape.mp4 -vf "select=eq(n\,$n)" -frames:v 1 out/frames/m$n.png; printf "%s " $n; ffmpeg -hide_banner -nostats -i out/frames/t0200.00.png -i out/frames/m$n.png -lavfi psnr -f null - 2>&1 | grep -o "average:[0-9.inf]*"; done
4799 average:21.488170 4800 average:34.226661 4801 average:22.294776 4802 average:21.891261
思考：Matching render frames to master footage at a key timestamp.
Matching render frames to master footage at a key timestamp.
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('tools/deliver.py'); s = p.read_text() s = s.replace('ap.add_argument("--max-mib", type=float, default=27.5)', 'ap.add_argument("--max-mib", type=float, default=27.5)\nap.add_argument("--skip-hq", action="store_true")') s = s.replace('hq = out / f"{a.name}-1080p.mp4"\nrun(', 'hq = out / f"{a.name}-1080p.mp4"\nif not a.skip_hq:\n run(') p.write_text(s); print('ok') PYEOF python3 -c "import ast; ast.parse(open('~/ai-tree/tools/deliver.py').read())" && cat > ~/ai-tree/out/deliver.sh <<'EOF' #!/bin/sh cd ~/ai-tree python3 tools/deliver.py out/master/landscape.mp4 ai-tree-v2-landscape --skip-hq --patch out/master/patch-land.mp4:114.5:127.0 > out/deliver-land.log 2>&1 python3 tools/deliver.py out/master/portrait.mp4 ai-tree-v2-portrait --portrait --patch out/master/patch-port.mp4:114.5:127.0 > out/deliver-port.log 2>&1 echo ALLDONE >> out/deliver-port.log EOF chmod +x ~/ai-tree/out/deliver.sh && rm -f ~/ai-tree/out/v2/ai-tree-v2-landscape-720p.mp4 && (nohup ~/ai-tree/out/deliver.sh > /dev/null 2>&1 &) ; sleep 2; ps aux | grep -c "[f]fmpeg"ok 1
运行 ffmpeg -loglevel error -y -ss 122.5 -i out/v2/ai-tree-v2-landscape-1080p.mp4 -f…
ffmpeg -loglevel error -y -ss 122.5 -i out/v2/ai-tree-v2-landscape-1080p.mp4 -frames:v 1 -vf "crop=820:340:1090:0" out/frames/check-attn.png && echo ok
ok
读取 check-attn.png
{ "file_path": "~/ai-tree/out/frames/check-attn.png" }[图片]
思考：Planning a video file transfer and encoding workflow. Chunki…
Planning a video file transfer and encoding workflow.
Chunking large video files for transfer and verification.运行 git add -A scene tools script && git -c user.name="Claude" -c user.email="norep…
git add -A scene tools script && git -c user.name="Claude" -c user.email="[邮箱]" commit -q -m "v2: attention weights sum to 100%, serve scene over http for rendering, delivery encodes, script v2 Co-Authored-By: Claude Opus 5.5 <[邮箱]> Claude-Session: https://claude.ai/code/session_01HZH8EiUQAY3dtZbkE3ysZz" && git tag -f v2 && git log --oneline | head -3; ls -la /mnt/user-data/outputs 2>&1 | head -3; tail -n 3 out/deliver-land.log
9948a17 v2: attention weights sum to 100%, serve scene over http for rendering, delivery encodes, script v2 0152c83 v2: principle layer (narration, two-layer term cards with mechanism animations), real names on tags, landscape + portrait layouts, collision-aware labels, new score and sound effects 7e5a868 v1.1: cap young branch length; fix two note dates lrwxrwxrwx 1 root root 19 Oct 14 06:42 /mnt/user-data/outputs -> /mnt/attach/outputs
运行 sleep 150; cat ~/ai-tree/out/deliver-land.log; ls -la ~/a…
sleep 150; cat ~/ai-tree/out/deliver-land.log; ls -la ~/ai-tree/out/v2/
total 157740 drwxr-xr-x 2 root root 4096 Oct 15 04:53 . drwxr-xr-x 10 root root 4096 Oct 15 04:51 .. -rw-r--r-- 1 root root 145521034 Oct 15 04:47 ai-tree-v2-landscape-1080p.mp4 -rw-r--r-- 1 root root 15990832 Oct 15 04:55 ai-tree-v2-landscape-720p.mp4
运行 sleep 120; cat ~/ai-tree/out/deliver-land.log; ls -la ~/a…
sleep 120; cat ~/ai-tree/out/deliver-land.log; ls -la ~/ai-tree/out/v2/
ai-tree-v2-landscape-1080p.mp4 138.8 MiB 1920 x 1080 frames 7539 dur 314.218000 ai-tree-v2-landscape-720p.mp4 27.3 MiB 1280 x 720 frames 7539 dur 314.218000 total 176788 drwxr-xr-x 2 root root 4096 Oct 15 04:56 . drwxr-xr-x 10 root root 4096 Oct 15 04:56 .. -rw-r--r-- 1 root root 145521034 Oct 15 04:47 ai-tree-v2-landscape-1080p.mp4 -rw-r--r-- 1 root root 28678075 Oct 15 04:56 ai-tree-v2-landscape-720p.mp4 -rw-r--r-- 1 root root 6815792 Oct 15 04:57 ai-tree-v2-portrait-1080p.mp4
思考：Checking video file sizes and quality at different resolutio…
Checking video file sizes and quality at different resolutions.
运行 ffmpeg -loglevel error -y -ss 104.5 -i out/v2/ai-tree-v2-landscape-720p.mp4 -fr…
ffmpeg -loglevel error -y -ss 104.5 -i out/v2/ai-tree-v2-landscape-720p.mp4 -frames:v 1 out/frames/check-720.png && echo ok
ok
读取 check-720.png
{ "file_path": "~/ai-tree/out/frames/check-720.png" }[图片]
把文件发给你
{ "caption": "v2 横屏版（720p，27 MB，可以直接在手机上看）。竖屏版还在编码，好了再发。", "display": "render", "files": [ "~/ai-tree/out/v2/ai-tree-v2-landscape-720p.mp4" ], "status": "proactive" }1 file delivered to user. ~/ai-tree/out/v2/ai-tree-v2-landscape-720p.mp4 → file_uuid: 511d80f4-8afb-4786-8431-7f471432eb9c Each file shows as a card whose position relative to your text depends on the app the user is reading in, so refer to a file by its name, never as above or below.
从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/ai-tree-v2-landscape-720p.mp4", "fileUuid": "511d80f4-8afb-4786-8431-7f471432eb9c" } ] }{"written":["~/Movies/ai-tree-video/ai-tree-v2-landscape-720p.mp4"],"rejected":[]}运行 mkdir -p /mnt/user-data/outputs/transfer && cd /mnt/user-data/outputs/transfer …
mkdir -p /mnt/user-data/outputs/transfer && cd /mnt/user-data/outputs/transfer && rm -f land-* && split -b 27m -d -a 2 ~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4 land-1080p.part && ls -la && sha256sum ~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4
total 142112 drwxr-xr-x 1 claude ubuntu 0 Oct 15 04:57 . drwxr-xr-x 1 claude ubuntu 0 Oct 15 04:57 .. -rw-r--r-- 1 claude ubuntu 28311552 Oct 15 04:57 land-1080p.part00 -rw-r--r-- 1 claude ubuntu 28311552 Oct 15 04:57 land-1080p.part01 -rw-r--r-- 1 claude ubuntu 28311552 Oct 15 04:57 land-1080p.part02 -rw-r--r-- 1 claude ubuntu 28311552 Oct 15 04:57 land-1080p.part03 -rw-r--r-- 1 claude ubuntu 28311552 Oct 15 04:57 land-1080p.part04 -rw-r--r-- 1 claude ubuntu 3963274 Oct 15 04:57 land-1080p.part05 eb7dedeee1d39148a8b02484aeff057acf73ea0eb494330b45a8fc219e1e40b1 ~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4 Shell cwd was reset to ~
从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.part00", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.part00" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.part01", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.part01" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.part02", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.part02" } ] }{"written":[],"rejected":[{"devicePath":"~/Movies/ai-tree-video/.transfer/land-1080p.part00","reason":"HTTP 400 fetching session file. Call SendUserFile and pass its fileUuid to device_commit_files instead."},{"devicePath":"~/Movies/ai-tree-video/.transfer/land-1080p.part01","reason":"HTTP 400 fetching session file. Call SendUserFile and pass its fileUuid to device_commit_files instead."},{"devicePath":"~/Movies/ai-tree-video/.transfer/land-1080p.part02","reason":"HTTP 400 fetching session file. Call SendUserFile and pass its fileUuid to device_commit_files instead."}]}从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.part03", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.part03" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.part04", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.part04" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.part05", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.part05" } ] }{"written":["~/Movies/ai-tree-video/.transfer/land-1080p.part05"],"rejected":[{"devicePath":"~/Movies/ai-tree-video/.transfer/land-1080p.part03","reason":"HTTP 400 fetching session file. Call SendUserFile and pass its fileUuid to device_commit_files instead."},{"devicePath":"~/Movies/ai-tree-video/.transfer/land-1080p.part04","reason":"HTTP 400 fetching session file. Call SendUserFile and pass its fileUuid to device_commit_files instead."}]}思考：Debugging failed uploads for large file chunks.
Debugging failed uploads for large file chunks.
从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.part00", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.part00" } ] }{"written":[],"rejected":[{"devicePath":"~/Movies/ai-tree-video/.transfer/land-1080p.part00","reason":"HTTP 400 fetching session file. Call SendUserFile and pass its fileUuid to device_commit_files instead."}]}运行 cd /mnt/user-data/outputs/transfer && rm -f land-* && split -b 9m -d -a 2 /home…
cd /mnt/user-data/outputs/transfer && rm -f land-* && split -b 9m -d -a 2 ~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4 land-1080p.p && ls | head -20 && ls | wc -l
land-1080p.p00 land-1080p.p01 land-1080p.p02 land-1080p.p03 land-1080p.p04 land-1080p.p05 land-1080p.p06 land-1080p.p07 land-1080p.p08 land-1080p.p09 land-1080p.p10 land-1080p.p11 land-1080p.p12 land-1080p.p13 land-1080p.p14 land-1080p.p15 16 Shell cwd was reset to ~
从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p00", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p00" } ] }{"written":["~/Movies/ai-tree-video/.transfer/land-1080p.p00"],"rejected":[]}从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p01", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p01" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p02", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p02" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p03", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p03" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p04", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p04" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p05", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p05" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p06", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p06" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p07", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p07" } ] }{"written":["~/Movies/ai-tree-video/.transfer/land-1080p.p01","~/Movies/ai-tree-video/.transfer/land-1080p.p02","~/Movies/ai-tree-video/.transfer/land-1080p.p03","~/Movies/ai-tree-video/.transfer/land-1080p.p04","~/Movies/ai-tree-video/.transfer/land-1080p.p05","~/Movies/ai-tree-video/.transfer/land-1080p.p06","~/Movies/ai-tree-video/.transfer/land-1080p.p07"],"rejected":[]}从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p08", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p08" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p09", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p09" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p10", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p10" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p11", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p11" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p12", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p12" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p13", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p13" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p14", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p14" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.p15", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.p15" } ] }{"written":["~/Movies/ai-tree-video/.transfer/land-1080p.p08","~/Movies/ai-tree-video/.transfer/land-1080p.p09","~/Movies/ai-tree-video/.transfer/land-1080p.p10","~/Movies/ai-tree-video/.transfer/land-1080p.p11","~/Movies/ai-tree-video/.transfer/land-1080p.p12","~/Movies/ai-tree-video/.transfer/land-1080p.p13","~/Movies/ai-tree-video/.transfer/land-1080p.p14","~/Movies/ai-tree-video/.transfer/land-1080p.p15"],"rejected":[]}运行 cd ~/Movies/ai-tree-video && ls .transfer && cat .transfer/land-1…
cd ~/Movies/ai-tree-video && ls .transfer && cat .transfer/land-1080p.p?? > ai-tree-v2-landscape-1080p.mp4 && shasum -a 256 ai-tree-v2-landscape-1080p.mp4 && ffprobe -v error -show_entries format=duration -of csv=p=0 ai-tree-v2-landscape-1080p.mp4
Process started with PID 25854 (shell: /bin/zsh) Initial output: land-1080p.p00 land-1080p.p01 land-1080p.p02 land-1080p.p03 land-1080p.p04 land-1080p.p05 land-1080p.p06 land-1080p.p07 land-1080p.p08 land-1080p.p09 land-1080p.p10 land-1080p.p11 land-1080p.p12 land-1080p.p13 land-1080p.p14 land-1080p.p15 land-1080p.part05 bad0fdf4871e2d5cfb960d7f9378a94f8181c66b3f7732995e60e8cc0d1151eb ai-tree-v2-landscape-1080p.mp4 314.218000 ✅ Process 25854 has finished execution
思考：Investigating a mismatched file hash despite a valid duratio…
Investigating a mismatched file hash despite a valid duration.
运行 cd /mnt/user-data/outputs/transfer && sha256sum land-1080p.p?? | awk '{print su…
cd /mnt/user-data/outputs/transfer && sha256sum land-1080p.p?? | awk '{print substr($1,1,16), $2}'; ls -l land-1080p.p?? | awk '{print $5, $9}'47bd3ecbfc245d03 land-1080p.p00 0e37c77d78e08720 land-1080p.p01 7ca713906cd1873e land-1080p.p02 b7b989856a35038d land-1080p.p03 6c5646ba790c72b5 land-1080p.p04 90dfa8816faaa47d land-1080p.p05 b23dd6fd28c62691 land-1080p.p06 a7299b497b68ee84 land-1080p.p07 fc53b53780f6e931 land-1080p.p08 9be3bd30d9e9e716 land-1080p.p09 9faa815ded6e3f93 land-1080p.p10 0ac1421aa4eb0916 land-1080p.p11 1037930dfb1ffa21 land-1080p.p12 be521aab3c6e8280 land-1080p.p13 38ee47a940cee495 land-1080p.p14 a97aaa22f95dd757 land-1080p.p15 9443059 land-1080p.p00 9437184 land-1080p.p01 9437184 land-1080p.p02 9437184 land-1080p.p03 9437184 land-1080p.p04 9437184 land-1080p.p05 9437184 land-1080p.p06 9437184 land-1080p.p07 9437184 land-1080p.p08 9437184 land-1080p.p09 9437184 land-1080p.p10 9437184 land-1080p.p11 9437184 land-1080p.p12 9437184 land-1080p.p13 9437184 land-1080p.p14 3963274 land-1080p.p15 Shell cwd was reset to ~
运行 cd ~/Movies/ai-tree-video/.transfer && shasum -a 256 land-1080p.p…
cd ~/Movies/ai-tree-video/.transfer && shasum -a 256 land-1080p.p?? | awk '{print substr($1,1,16), $2}'; ls -l land-1080p.p?? | awk '{print $5, $9}'Process started with PID 25870 (shell: /bin/zsh) Initial output: 47bd3ecbfc245d03 land-1080p.p00 0e37c77d78e08720 land-1080p.p01 7ca713906cd1873e land-1080p.p02 b7b989856a35038d land-1080p.p03 6c5646ba790c72b5 land-1080p.p04 90dfa8816faaa47d land-1080p.p05 b23dd6fd28c62691 land-1080p.p06 a7299b497b68ee84 land-1080p.p07 fc53b53780f6e931 land-1080p.p08 9be3bd30d9e9e716 land-1080p.p09 9faa815ded6e3f93 land-1080p.p10 0ac1421aa4eb0916 land-1080p.p11 1037930dfb1ffa21 land-1080p.p12 be521aab3c6e8280 land-1080p.p13 38ee47a940cee495 land-1080p.p14 a97aaa22f95dd757 land-1080p.p15 9443059 land-1080p.p00 9437184 land-1080p.p01 9437184 land-1080p.p02 9437184 land-1080p.p03 9437184 land-1080p.p04 9437184 land-1080p.p05 9437184 land-1080p.p06 9437184 land-1080p.p07 9437184 land-1080p.p08 9437184 land-1080p.p09 9437184 land-1080p.p10 9437184 land-1080p.p11 9437184 land-1080p.p12 9437184 land-1080p.p13 9437184 land-1080p.p14 3963274 land-1080p.p15 ✅ Process 25870 has finished execution
思考：Investigating a file-size mismatch in split chunk sizes.
Investigating a file-size mismatch in split chunk sizes.
运行 cd /mnt/user-data/outputs/transfer && F=~/ai-tree/out/v2/ai-tree-v2-…
cd /mnt/user-data/outputs/transfer && F=~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4; ls -l $F; cmp <(head -c 9437184 $F) <(head -c 9437184 land-1080p.p00) && echo "first 9MiB identical"; tail -c 5875 land-1080p.p00 | xxd | head -5; cmp <(tail -c +9437185 $F | head -c 9437184) land-1080p.p01 && echo "p01 matches"; stat -c '%s %y' land-1080p.p00; mount | grep attach | head -3
-rw-r--r-- 1 root root 145521034 Oct 15 04:47 ~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4 /dev/fd/63 /dev/fd/62 differ: char 34, line 1 /bin/bash: line 1: xxd: command not found p01 matches 9443059 2026-10-15 04:58:09.394348000 +0000 rclone-filestore:cse_01HZH8EiUQAY3dtZbkE3ysZz:/mnt/attach/outputs on /mnt/attach/outputs type fuse.rclone (rw,nosuid,nodev,relatime,user_id=0,group_id=0,allow_other,max_read=1048576) Shell cwd was reset to ~
思考：Comparing file timestamps to trace the split operation's his…
Comparing file timestamps to trace the split operation's history.
Suspecting the source file changed mid-split unexpectedly.运行 F=~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4; T=/mnt/user-data/…
F=~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4; T=/mnt/user-data/outputs/transfer; head -c 9437184 $F > ~/q00.bin && sha256sum ~/q00.bin | cut -c1-16 && cp ~/q00.bin $T/land-1080p.q00 && sleep 2 && sha256sum $T/land-1080p.q00 | cut -c1-16 && stat -c %s $T/land-1080p.q00 && for i in $(seq -w 1 15); do off=$((10#$i * 9437184)); h1=$(tail -c +$((off+1)) $F | head -c 9437184 | sha256sum | cut -c1-16); h2=$(sha256sum $T/land-1080p.p$i | cut -c1-16); [ "$h1" = "$h2" ] && echo "p$i ok" || echo "p$i MISMATCH"; done
cfdf13061c9bfefc cfdf13061c9bfefc 9437184 p01 ok p02 ok p03 ok p04 ok p05 ok p06 ok p07 ok p08 ok p09 ok p10 ok p11 ok p12 ok p13 ok p14 ok p15 ok
从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.q00", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.q00" } ] }{"written":["~/Movies/ai-tree-video/.transfer/land-1080p.q00"],"rejected":[]}运行 cd ~/Movies/ai-tree-video && shasum -a 256 .transfer/land-1080p.q…
cd ~/Movies/ai-tree-video && shasum -a 256 .transfer/land-1080p.q00 | cut -c1-16 && cat .transfer/land-1080p.q00 .transfer/land-1080p.p0[1-9] .transfer/land-1080p.p1[0-5] > ai-tree-v2-landscape-1080p.mp4 && shasum -a 256 ai-tree-v2-landscape-1080p.mp4 && ls -l ai-tree-v2-landscape-1080p.mp4
Process started with PID 25905 (shell: /bin/zsh) Initial output: 24e0a7075da6aaa3 c04d0cc72264dd73f1bae5508e531287cfca057fd7a33b19bbd63bafab636a64 ai-tree-v2-landscape-1080p.mp4 -rw-r--r--@ 1 niracler staff 145526909 Oct 15 13:00 ai-tree-v2-landscape-1080p.mp4 ✅ Process 25905 has finished execution
思考：Verifying hash values and reassembled file sizes. Suspecting…
Verifying hash values and reassembled file sizes.
Suspecting server-side mp4 processing altered the first chunk.运行 T=/mnt/user-data/outputs/transfer; F=~/ai-tree/out/v2/ai-tree-v2-lan…
T=/mnt/user-data/outputs/transfer; F=~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4; { printf 'AITREE-CHUNK-000'; head -c 9437184 $F; } > ~/r00.bin && sha256sum ~/r00.bin | cut -c1-16 && cp ~/r00.bin $T/land-1080p.r00 && sha256sum ~/ai-tree/out/v2/ai-tree-v2-landscape-720p.mp4 | cut -c1-1614bddc7c2cdd908f 983e3afba80a0846
从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/land-1080p.r00", "stagedPath": "/mnt/user-data/outputs/transfer/land-1080p.r00" } ] }{"written":["~/Movies/ai-tree-video/.transfer/land-1080p.r00"],"rejected":[]}运行 cd ~/Movies/ai-tree-video && shasum -a 256 .transfer/land-1080p.r…
cd ~/Movies/ai-tree-video && shasum -a 256 .transfer/land-1080p.r00 | cut -c1-16 && { tail -c +17 .transfer/land-1080p.r00; cat .transfer/land-1080p.p0[1-9] .transfer/land-1080p.p1[0-5]; } > ai-tree-v2-landscape-1080p.mp4 && shasum -a 256 ai-tree-v2-landscape-1080p.mp4 && ls -l ai-tree-v2-landscape-1080p.mp4 && shasum -a 256 ai-tree-v2-landscape-720p.mp4 | cut -c1-16 && ffprobe -v error -show_entries format=duration,size -of csv=p=0 ai-tree-v2-landscape-720p.mp4Process started with PID 25933 (shell: /bin/zsh) Initial output: 14bddc7c2cdd908f eb7dedeee1d39148a8b02484aeff057acf73ea0eb494330b45a8fc219e1e40b1 ai-tree-v2-landscape-1080p.mp4 -rw-r--r--@ 1 niracler staff 145521034 Oct 15 13:00 ai-tree-v2-landscape-1080p.mp4 983e3afba80a0846 314.218000,28678075 ✅ Process 25933 has finished execution
思考：Verifying matching resolutions across file versions.
Verifying matching resolutions across file versions.
运行 cat ~/ai-tree/out/deliver-port.log; ls -la ~/ai-tree/out/…
cat ~/ai-tree/out/deliver-port.log; ls -la ~/ai-tree/out/v2/
total 234900 drwxr-xr-x 2 root root 4096 Oct 15 04:56 . drwxr-xr-x 10 root root 4096 Oct 15 04:56 .. -rw-r--r-- 1 root root 145521034 Oct 15 04:47 ai-tree-v2-landscape-1080p.mp4 -rw-r--r-- 1 root root 28678075 Oct 15 04:56 ai-tree-v2-landscape-720p.mp4 -rw-r--r-- 1 root root 66322480 Oct 15 05:00 ai-tree-v2-portrait-1080p.mp4
思考：Encoding multiple video resolutions at different quality set…
Encoding multiple video resolutions at different quality settings.
派出子任务：Fact-check video script v2
You are fact-checking the narration script of a short Chinese-language explainer video about ~70 years of AI history, aimed at non-programmer colleagues ("simple enough that my dad understands"), but it must not be wrong or misleading to an expert either. Read ~/ai-tree/script/script-v2.md. Focus on: 1. The bolded "principle" sentences and the 词卡 (term card) principle lines: are the plain-language explanations of neural networks/weights, backpropagation/gradient descent, expert systems, machine learning, deep learning (edges→shapes→cat), reinforcement learning, Transformer attention, LLMs (next-word prediction, scaling laws), RLHF (pretraining → fine-tuning → RL from human ratings), diffusion models (denoising from noise guided by the prompt), reasoning models (chain of thought, RL rewarding correct reasoning), agents (LLM + tools + loop), multimodal/embodied (all modalities as tokens) correct at this level of simplification? Flag anything an ML expert would call wrong (not merely simplified). 2. The "树上的里程碑" list (tree milestones): check each name + year + gloss (ELIZA 1966, Google Translate 2006 statistic …（中间省略 926 字）… specific historical fact. Report back in under 250 words: a list of concrete problems only (quote the exact Chinese text, say what is wrong, and propose a minimal corrected wording in Chinese of similar length). If something is fine, do not mention it. If there are no problems, say so in one line.This agent's report was delivered to you as a message from "ab8d24518129cd9f0" (its SubagentHandback call). Read it there; it is not repeated here. agentId: ab8d24518129cd9f0 (use SendMessage with to: 'ab8d24518129cd9f0', summary: '<5-10 word recap>' to continue this agent) <usage>subagent_tokens: 118600 tool_uses: 10 duration_ms: 389921</usage>
思考：Verifying claimed inaccuracies in the Deep Blue comparison. …
Verifying claimed inaccuracies in the Deep Blue comparison.
Checking the backpropagation and XCON timeline claims.
Reviewing the Dartmouth naming claim and frozen-sprout metaphor.
Weighing whether rechecked wording fixes justify redoing the render.
Rewriting narration lines to fix the identified inaccuracies.
Adjusting the Deep Blue sentence to clarify its contrast.
Finalizing wording and planning the full re-render pipeline.运行 pkill -f "[d]eliver" ; pkill -f "[f]fmpeg -y -loglevel error -i out/master"; sl…
pkill -f "[d]eliver" ; pkill -f "[f]fmpeg -y -loglevel error -i out/master"; sleep 1; ps aux | grep -E "[d]eliver|[f]fmpeg" | wc -l; cd ~/ai-tree && python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:80]); s = s.replace(old, new) rep("deep: '1956 年达特茅斯会议上，|第一次起了这个名字' }", "deep: '麦卡锡为 1956 年|达特茅斯会议起的名字' }") rep("tag: '2014 GAN · 两个网络对练画画'", "tag: '2014 GAN · 一个网络画、一个挑错'") rep("tag: '2012 AlexNet · 认图大跃进'", "tag: '2012 AlexNet · 认图大突破'") p.write_text(s) d = Path('script/script-v2.md'); t = d.read_text() for a, b in [("1980 XCON · 帮公司配电脑（1987 后退场）", "1980 XCON · 帮公司配电脑（专家系统热潮 1987 年后退去，整条枝变灰）"), ("2014 GAN · 两个网络对练画画", "2014 GAN · 一个网络画、一个挑错"), ("2012 AlexNet · 认图大跃进", "2012 AlexNet · 认图大突破"), ("词卡：人工智能 / 来历：1956 年达特茅斯会议上，第一次起了这个名字。", "词卡：人工智能 / 来历：麦卡锡为 1956 年达特茅斯会议起的名字。"), ("**1986年，冻住的那颗芽也开始化冻：\"反向传播\"出现了——", "**1986年，冻住的那颗芽也开始化冻：\"反向传播\"流行开来——"), ("1997年，每秒能算两亿步的深蓝，赢了国际象棋世界冠军。", "1997年，靠每秒两亿步\"硬算\"的深蓝，赢了国际象棋世界冠军。"), ("2012年，那颗冻了几十年的芽突然开花", "2012年，那颗等了几十年的芽突然开花")]: assert t.count(a) == 1, a t = t.replace(a, b) d.write_text(t); print('ok') PYEOF2 ok
运行 ps aux | grep -E "[d]eliver|[f]fmpeg" | awk '{print $2, $11, $12, $13, $14, $15…
ps aux | grep -E "[d]eliver|[f]fmpeg" | awk '{print $2, $11, $12, $13, $14, $15, $16}'(Bash completed with no output)
运行 cd ~/Movies/ai-tree-video/voice && cp s03.mp3 v2.0/s03-b.mp3 && c…
cd ~/Movies/ai-tree-video/voice && cp s03.mp3 v2.0/s03-b.mp3 && cp s04.mp3 v2.0/s04.mp3 && cp s05.mp3 v2.0/s05-b.mp3 && cp timing.json v2.0/timing-b.json && python3 - <<'EOF' from pathlib import Path p = Path('tts.py'); s = p.read_text() pairs = [("1986年，冻住的那颗芽也开始化冻：“反向传播”出现了——", "1986年，冻住的那颗芽也开始化冻：“反向传播”流行开来——"), ("1997年，每秒能算两亿步的深蓝，赢了国际象棋世界冠军。", "1997年，靠每秒两亿步“硬算”的深蓝，赢了国际象棋世界冠军。"), ("2012年，那颗冻了几十年的芽突然开花", "2012年，那颗等了几十年的芽突然开花")] for a, b in pairs: assert s.count(a) == 1, a s = s.replace(a, b) p.write_text(s); print("tts.py updated") EOF ../.venv/bin/python tts.py 2>&1 | tail -5Process started with PID 26102 (shell: /bin/zsh) Initial output: tts.py updated s03 83 boundaries s04 37 boundaries s05 99 boundaries ✅ Process 26102 has finished execution
准备从你电脑传回文件
{ "paths": [ "~/Movies/ai-tree-video/voice/s03.mp3", "~/Movies/ai-tree-video/voice/s04.mp3", "~/Movies/ai-tree-video/voice/s05.mp3", "~/Movies/ai-tree-video/voice/timing.json" ] }{"staged":[{"devicePath":"~/Movies/ai-tree-video/voice/s03.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s03.mp3","mtimeMs":1792040952955,"bytes":180432,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/s04.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s04.mp3","mtimeMs":1792040954731,"bytes":79488,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/s05.mp3","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/s05.mp3","mtimeMs":1792040959812,"bytes":197856,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/voice/timing.json","stagedPath":"/mnt/user-data/uploads/ai-tree-video/voice/timing.json","mtimeMs":1792040959816,"bytes":50494,"ok":true,"dispatched":true}]}运行 sleep 2; cd ~/ai-tree && U=/mnt/user-data/uploads/ai-tree-video/voic…
sleep 2; cd ~/ai-tree && U=/mnt/user-data/uploads/ai-tree-video/voice && python3 -c " import json; t=json.load(open('$U/timing.json')); print(t['s03']['text'][60:110]); print(t['s04']['text']); print(t['s05']['text'][:30])" && cp $U/s03.mp3 $U/s04.mp3 $U/s05.mp3 audio/voice/ && cp $U/timing.json audio/voice/timing.json && python3 tools/build_timeline.py 2>&1 | head -1 && python3 - <<'EOF' import json TL = json.loads(open('~/ai-tree/scene/timeline.js').read()[len("window.TL = "):].rstrip().rstrip(";")) b = {x["id"]: x for x in TL["beats"]} p1 = b["b7"]["t0"] - b["b1"]["t0"]; p2 = b["end"]["t0"] - b["b7"]["t0"] print("total", TL["total"], "part2 share", round(p2 / (p1 + p2), 3)) print([(c["id"], c["t"], c["hold"]) for c in TL["cards"][:6]]) for c in TL["captions"]: if 74 < c["t0"] < 98: print(round(c["t0"], 1), c["text"]) EOF“如果……就……”，风光一时。1986年，冻住的那颗芽也开始化冻：“反向传播”流行开来——答错了，就 后来换了条路：不写规则了，让电脑从大量例子里自己找规律，这叫“机器学习”。1997年，靠每秒两亿步“硬算”的深蓝，赢了国际象棋世界冠军。 2012年，那颗等了几十年的芽突然开花：很深的神经网络，喂进 total 313.7s | beats 12 | captions 96 | cards 14 | polaroids 16 | notes 40 total 313.72 part2 share 0.486 [('ai', 20.228, 5.0), ('nn', 26.734, 8.688), ('expert', 51.784, 5.0), ('backprop', 60.153, 7.239), ('ml', 80.366, 5.0), ('dl', 101.23, 7.272)] 74.8 后来换了条路：不写规则了 77.3 让电脑从大量例子里自己找规律 80.2 这叫“机器学习” 81.8 1997年，靠每秒两亿步“硬算”的深蓝 85.2 赢了国际象棋世界冠军 91.4 2012年，那颗等了几十年的芽突然开花 95.8 很深的神经网络，喂进海量图片运行 python3 tools/audio.py 2>&1 | tail -1 && ffmpeg -hide_banner -nostats -i audio/…
python3 tools/audio.py 2>&1 | tail -1 && ffmpeg -hide_banner -nostats -i audio/mix.wav -af ebur128=peak=true -f null - 2>&1 | grep -E "^\s+(I|Peak):" | head -2 && (timeout 900 python3 tools/qa_layout.py > out/qa-land.txt 2>&1 &) ; timeout 900 python3 tools/qa_layout.py --portrait > out/qa-port.txt 2>&1; sleep 6; grep -E "^overlap|^panels" out/qa-land.txt | grep -v "(56, 1346" ; echo ===; grep -E "^overlap|^panels" out/qa-port.txt | grep -v "(56, 1346, 1024, 1714)"
music + sfx + mix written (313.7s); input loudness -18.74 LUFS I: -16.1 LUFS Peak: -1.5 dBFS panels (316, 321, 601, 434) | (30, 262, 448, 345) @ 67.75-67.75 panels (316, 321, 601, 434) | (30, 345, 340, 490) @ 67.75-67.75 panels (236, 354, 467, 455) | (30, 345, 340, 490) @ 85.75-85.75 panels (95, 486, 429, 962) | (-71, 566, 363, 962) @ 88.25-88.25 panels (95, 486, 429, 962) | (44, 566, 478, 962) @ 88.50-88.50 overlap 听懂说话 | 2014 GAN · 一个网络画、一个挑错 @ 105.00-105.00 overlap 2014 GAN · 一个网络画、一个挑错 | 画画 · 创作 @ 105.00-105.00 panels (326, 317, 619, 432) | (30, 262, 448, 345) @ 114.75-114.75 panels (250, 348, 490, 453) | (30, 345, 340, 490) @ 125.25-125.25 panels (500, 39, 1244, 298) | (30, 40, 530, 262) @ 176.25-176.25 panels (110, 376, 305, 460) | (30, 345, 340, 490) @ 190.75-190.75 panels (342, 176, 862, 364) | (30, 40, 530, 262) @ 224.25-224.25 panels (342, 176, 862, 364) | (30, 262, 448, 345) @ 224.25-224.25 panels (444, 87, 1108, 322) | (30, 40, 530, 262) @ 271.25-271.25 === panels (296, 257, 990, 484) | (20, 40, 1060, 380) @ 25.50-25.50 overlap 1966 ELIZA · 第一个聊天程序 | 翻译 · 聊天 @ 35.00-35.00 panels (483, 176, 943, 333) | (20, 40, 1060, 380) @ 35.75-35.75 overlap 1970s MYCIN · 帮医生选药 | 1980 XCON · 帮公司配电脑 @ 51.25-51.25 panels (159, 316, 1024, 594) | (20, 40, 1060, 380) @ 57.00-57.00 panels (564, 141, 923, 268) | (20, 40, 1060, 380) @ 67.75-67.75 panels (22, 345, 1058, 677) | (20, 40, 106 …（中间省略 499 字）… , 232) | (20, 40, 1060, 380) @ 125.25-125.25 panels (22, 363, 1058, 691) | (20, 40, 1060, 380) @ 129.75-129.75 panels (136, 327, 1030, 612) | (20, 40, 1060, 380) @ 136.25-136.25 panels (237, 283, 1004, 531) | (20, 40, 1060, 380) @ 143.00-143.00 panels (656, 235, 900, 329) | (20, 40, 1060, 380) @ 190.75-190.75 panels (324, 822, 983, 1039) | (831, 592, 1087, 880) @ 224.25-224.25运行 git add -A scene tools script audio/voice && git -c user.name="Claude" -c user.…
git add -A scene tools script audio/voice && git -c user.name="Claude" -c user.email="[邮箱]" commit -q -m "v2: fact-check fixes (Deep Blue = brute force, backprop popularised in 1986, bud 'waited' not 'frozen', McCarthy named AI for Dartmouth, GAN gloss, AlexNet wording) Co-Authored-By: Claude Opus 5.5 <[邮箱]> Claude-Session: https://claude.ai/code/session_01HZH8EiUQAY3dtZbkE3ysZz" && git tag -f v2 && cat > out/final.sh <<'EOF' #!/bin/sh cd ~/ai-tree python3 tools/render.py --fps 24 --workers 2 --audio audio/mix.wav --out out/master/landscape.mp4 > out/render-land.log 2>&1 python3 tools/render.py --portrait --fps 24 --workers 2 --audio audio/mix.wav --out out/master/portrait.mp4 > out/render-port.log 2>&1 rm -f out/v2/* python3 tools/deliver.py out/master/landscape.mp4 ai-tree-v2-landscape > out/deliver-land.log 2>&1 python3 tools/deliver.py out/master/portrait.mp4 ai-tree-v2-portrait --portrait > out/deliver-port.log 2>&1 echo ALLDONE > out/final.done EOF chmod +x out/final.sh && rm -f out/final.done out/master/patch-*.mp4 && (nohup out/final.sh > /dev/null 2>&1 &) ; sleep 3; ps aux | grep -c "[r]ender.py"
Updated tag 'v2' (was 9948a17) 4
思考：Estimating total time for rendering and encoding steps.
Estimating total time for rendering and encoding steps.
刚才发你的横屏版先当草稿看。我让一个独立的 agent 从内行角度把关了一遍，它挑出几处会被懂行的人指出来的问题，我已经改好了，正在重新渲染定稿：
- 深蓝：原句紧跟在"机器学习"后面，听起来像深蓝是靠学习赢的。其实它靠的是暴力搜索，所以改成「靠每秒两亿步"硬算"的深蓝」。
- 反向传播：它 1970 年代就有了，1986 年是开始流行，所以「出现了」改成「流行开来」。
- 2012 年的那颗芽：前面已经讲了它 1986 年化冻，这里再说「冻了几十年」就前后矛盾，改成「等了几十年」。
- 画面上的小改动：「认图大跃进」改成「认图大突破」，避开"大跃进"的联想；GAN 改成「一个网络画、一个挑错」；"人工智能"词卡改成「麦卡锡为 1956 年达特茅斯会议起的名字」。
改动涉及 3 段旁白，已在你电脑上重新合成。定稿两个方向各出两版：1080p 用来投屏，720p 小文件方便发群。全部弄完大约 40 分钟。
干了 34 分钟，11 步
读 1 个文件，运行 8 条命令，其他操作 2 次
运行 sleep 540; tail -n 1 ~/ai-tree/out/render-land.log; tail -n 1 ~
sleep 540; tail -n 1 ~/ai-tree/out/render-land.log; tail -n 1 ~/ai-tree/out/render-port.log 2>/dev/null
done: out/master/landscape.mp4 (7529 frames in 537s) worker 0: 0/3764 frames (0s)
思考：Spot-checking the new landscape master at key timestamps.
Spot-checking the new landscape master at key timestamps.
运行 for t in 22.5 83.0 92.5 106.0; do ffmpeg -loglevel error -y -ss $t -i out/maste…
for t in 22.5 83.0 92.5 106.0; do ffmpeg -loglevel error -y -ss $t -i out/master/landscape.mp4 -frames:v 1 -vf scale=960:540 out/frames/fin-$t.png; done && python3 - <<'EOF' from PIL import Image fs = ['22.5', '83.0', '92.5', '106.0'] S = Image.new('RGB', (1920, 1080), 'white') for i, f in enumerate(fs): S.paste(Image.open(f'~/ai-tree/out/frames/fin-{f}.png'), ((i % 2) * 960, (i // 2) * 540)) S.save('~/ai-tree/out/frames/fin-sheet.png'); print('ok') EOFok
读取 fin-sheet.png
{ "file_path": "~/ai-tree/out/frames/fin-sheet.png" }[图片]
运行 sleep 560; tail -n 1 ~/ai-tree/out/render-port.log; ls -la ~
sleep 560; tail -n 1 ~/ai-tree/out/render-port.log; ls -la ~/ai-tree/out/v2/ 2>/dev/null
done: out/master/portrait.mp4 (7529 frames in 526s) total 18444 drwxr-xr-x 2 root root 4096 Oct 15 05:29 . drwxr-xr-x 10 root root 4096 Oct 15 05:11 .. -rw-r--r-- 1 root root 18874416 Oct 15 05:30 ai-tree-v2-landscape-1080p.mp4
运行 sleep 580; cat ~/ai-tree/out/deliver-land.log; ls -la ~/a…
sleep 580; cat ~/ai-tree/out/deliver-land.log; ls -la ~/ai-tree/out/v2/
total 153000 drwxr-xr-x 2 root root 4096 Oct 15 05:39 . drwxr-xr-x 10 root root 4096 Oct 15 05:11 .. -rw-r--r-- 1 root root 145384700 Oct 15 05:37 ai-tree-v2-landscape-1080p.mp4 -rw-r--r-- 1 root root 11272240 Oct 15 05:40 ai-tree-v2-landscape-720p.mp4
思考：Encoding and transferring video files across multiple resolu…
Encoding and transferring video files across multiple resolutions.
运行 T=/mnt/user-data/outputs/transfer; F=~/ai-tree/out/v2/ai-tree-v2-lan…
T=/mnt/user-data/outputs/transfer; F=~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4; rm -f $T/*; cd ~ && mkdir -p chunks && rm -f chunks/* && split -b 9m -d -a 2 $F chunks/L && { printf 'AITREE-CHUNK-000'; cat chunks/L00; } > chunks/L00x && rm chunks/L00 && for f in chunks/L*; do cp $f $T/; done && ls $T | tr '\n' ' ' && echo && sha256sum $F | cut -c1-64 && ls chunks | wc -lPermission for this command was denied by a built-in Claude Code safety check, not by the user. The check stops removals that can delete far more than intended: a system, home or workspace directory, or a target it cannot resolve, such as a shell variable that, if unset or empty, turns this into `rm -rf /` or `rm -rf /*`. Only a person may approve such a removal, and no person did (the permission prompt timed out, or this session cannot prompt). The command was NOT run; do not claim it succeeded. Do not work around the check by splitting, scripting, or re-issuing the removal through another tool or shell: the check exists because a removal like this can destroy the user's data, and getting past it would not make it safe. If the text below suggests a safe rewrite, run that instead; it goes through the same check. Otherwise finish the rest of the task without this removal, tell the user what you wanted to delete and why, and leave the removal to them. What was flagged: Dangerous rm operation detected in `rm -f $T/*`. The target '$T/*' is a shell variable expansion: when $T is unset or empty it becomes `/`, `/*` or a top-level path. This requires explicit approval and cannot be auto-allowed by permission rules. This check does not fire on a target that cannot expand to the filesystem root: rewrite it as `"${T:?}"/*`, which makes the shell stop with an error instead of running rm when $T is unset or empty, or use a literal absolute path.运行 rm -f /mnt/user-data/outputs/transfer/land-1080p.* && mkdir -p ~/chu…
rm -f /mnt/user-data/outputs/transfer/land-1080p.* && mkdir -p ~/chunks && rm -f ~/chunks/L* && cd ~ && split -b 9m -d -a 2 ~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4 chunks/L && { printf 'AITREE-CHUNK-000'; cat chunks/L00; } > chunks/L00x && rm -f chunks/L00 && cp chunks/L* /mnt/user-data/outputs/transfer/ && ls /mnt/user-data/outputs/transfer | tr '\n' ' ' && echo && sha256sum ~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4 && ls chunks | wc -lL00x L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15 8ce60adeb8871dc7399393aaf9555c199c24f6dbbe7f58ba5c6da2c4b979da04 ~/ai-tree/out/v2/ai-tree-v2-landscape-1080p.mp4 16
从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/L00x", "stagedPath": "/mnt/user-data/outputs/transfer/L00x" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L01", "stagedPath": "/mnt/user-data/outputs/transfer/L01" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L02", "stagedPath": "/mnt/user-data/outputs/transfer/L02" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L03", "stagedPath": "/mnt/user-data/outputs/transfer/L03" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L04", "stagedPath": "/mnt/user-data/outputs/transfer/L04" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L05", "stagedPath": "/mnt/user-data/outputs/transfer/L05" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L06", "stagedPath": "/mnt/user-data/outputs/transfer/L06" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L07", "stagedPath": "/mnt/user-data/outputs/transfer/L07" } ] }{"written":["~/Movies/ai-tree-video/.transfer/L00x","~/Movies/ai-tree-video/.transfer/L01","~/Movies/ai-tree-video/.transfer/L02","~/Movies/ai-tree-video/.transfer/L03","~/Movies/ai-tree-video/.transfer/L04","~/Movies/ai-tree-video/.transfer/L05","~/Movies/ai-tree-video/.transfer/L06","~/Movies/ai-tree-video/.transfer/L07"],"rejected":[]}从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/L08", "stagedPath": "/mnt/user-data/outputs/transfer/L08" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L09", "stagedPath": "/mnt/user-data/outputs/transfer/L09" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L10", "stagedPath": "/mnt/user-data/outputs/transfer/L10" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L11", "stagedPath": "/mnt/user-data/outputs/transfer/L11" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L12", "stagedPath": "/mnt/user-data/outputs/transfer/L12" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L13", "stagedPath": "/mnt/user-data/outputs/transfer/L13" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L14", "stagedPath": "/mnt/user-data/outputs/transfer/L14" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L15", "stagedPath": "/mnt/user-data/outputs/transfer/L15" } ] }{"written":["~/Movies/ai-tree-video/.transfer/L08","~/Movies/ai-tree-video/.transfer/L09","~/Movies/ai-tree-video/.transfer/L10","~/Movies/ai-tree-video/.transfer/L11","~/Movies/ai-tree-video/.transfer/L12","~/Movies/ai-tree-video/.transfer/L13","~/Movies/ai-tree-video/.transfer/L14","~/Movies/ai-tree-video/.transfer/L15"],"rejected":[]}运行 cd ~/Movies/ai-tree-video && { tail -c +17 .transfer/L00x; cat .t…
cd ~/Movies/ai-tree-video && { tail -c +17 .transfer/L00x; cat .transfer/L0[1-9] .transfer/L1[0-5]; } > ai-tree-v2-landscape-1080p.mp4 && shasum -a 256 ai-tree-v2-landscape-1080p.mp4 && ffprobe -v error -show_entries format=duration -of csv=p=0 ai-tree-v2-landscape-1080p.mp4Process started with PID 26918 (shell: /bin/zsh) Initial output: 8ce60adeb8871dc7399393aaf9555c199c24f6dbbe7f58ba5c6da2c4b979da04 ai-tree-v2-landscape-1080p.mp4 313.708333 ✅ Process 26918 has finished execution
运行 sleep 240; cat ~/ai-tree/out/deliver-land.log; ls -la ~/a…
sleep 240; cat ~/ai-tree/out/deliver-land.log; ls -la ~/ai-tree/out/v2/
ai-tree-v2-landscape-1080p.mp4 138.6 MiB 1920 x 1080 frames 7529 dur 313.708333 ai-tree-v2-landscape-720p.mp4 27.3 MiB 1280 x 720 frames 7529 dur 313.708333 total 216336 drwxr-xr-x 2 root root 4096 Oct 15 05:42 . drwxr-xr-x 10 root root 4096 Oct 15 05:11 .. -rw-r--r-- 1 root root 145384700 Oct 15 05:37 ai-tree-v2-landscape-1080p.mp4 -rw-r--r-- 1 root root 28677824 Oct 15 05:42 ai-tree-v2-landscape-720p.mp4 -rw-r--r-- 1 root root 47448112 Oct 15 05:45 ai-tree-v2-portrait-1080p.mp4
- 照片我觉得可以再放多一点？
- 左边的「学到了 xx 个词」这种可以优化？
干了 2 分钟，1 步
派出 1 个子任务
思考：处理用户对界面提出的修改意见。 寻找符合许可要求的候选照片。 列出可自由使用的历史人物照片候选。 为每个历史节点匹配合适…
处理用户对界面提出的修改意见。
寻找符合许可要求的候选照片。
列出可自由使用的历史人物照片候选。
为每个历史节点匹配合适的照片候选。
规划下载流程与许可确认步骤。
重新设计词汇计数器的呈现方式。
设计学习词汇的终章回顾呈现。
设计词汇本图标与终章回顾的联动方案。
核对历史人物照片的版权许可信息。
为不同历史节点分配具体的图片素材。
核实名人肖像照片的许可可用性。
规划新版本的交付节奏与用户确认方式。派出子任务：Find licensed AI-history photos
I'm making a short educational video about AI history (hand-drawn style; real photos appear as small "polaroids" with a caption, and a credits line at the end). I need candidate photos from Wikimedia Commons with licenses that allow reuse in a video with attribution (public domain, CC0, CC BY, CC BY-SA; NOT non-free / fair-use / "personality rights only" uploads, NOT NC/ND licenses). DO NOT download any image files — only read the Commons file description pages (use WebSearch to find them, then WebFetch on the page URLs the search returns). For each subject below, find the single best candidate: a clear, recognisable photo that still reads at ~300×340 px (portraits: face and shoulders visible; objects: the whole object). Subjects, in priority order: 1. John McCarthy (coined "artificial intelligence" for the 1956 Dartmouth workshop) — portrait. 2. Marvin Minsky — portrait (for 1969: his book "Perceptrons" chilled neural-network research). 3. A Lisp machine (e.g. Symbolics 3600/3640) or a DEC VAX-11/780 (the computer XCON configured) — to represent the 1980s expert-system boom. 4. An NVIDIA GeForce GTX 580 gr …（中间省略 671 字）… as shown on the page, year taken, and one line on whether it would work small (and where the subject sits in the frame: e.g. "face centered, upper third"). If a subject has no acceptably licensed photo, say so in one line rather than suggesting a non-free one. Keep the whole answer under 400 words.
The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user to tell you how to proceed.
你是不是哪里挂了？
- 照片我觉得可以再放多一点?
- 左边的「学到了 xx 个词」这种可以优化?因为我觉得是不是能有更好的表达方式?
没挂，刚才在后台渲染和编码，所以一直没出声，抱歉。
当前进度
- 按核查意见修好的横屏版已经放进你电脑的
ai-tree-video文件夹，1080p 和 720p 各一个。 - 竖屏版还在编码，大约还要 10 分钟。
- 你刚才拦下的那一步，是让子任务去维基共享资源（Wikimedia Commons）查有哪些可以合法使用的照片。只查授权，不下载任何东西。
你提的两点我都同意，有两件事想先听你的意见。
- 按核查意见修好的横屏版已经放进你电脑的
- Claude 问你
左上角那个「已学会 N 个词」，换成哪种表达？
- 词汇本 + 结尾回顾（推荐）
- 词直接挂在树上
- 去掉计数，只留结尾回顾
想在哪些时刻加真实照片？（可多选。选完我先去维基共享资源找授权合适的，再把文件名、来源、大小列给你确认后才下载）
- 1956 麦卡锡 · 1969 明斯基
- Lisp 机 · 游戏显卡
- 2024 辛顿 · 黄仁勋
- 宇树人形机器人
词汇本 + 结尾回顾（推荐）
宇树人形机器人,Lisp 机 · 游戏显卡,2024 辛顿 · 黄仁勋,1956 麦卡锡 · 1969 明斯基,我觉得各种你觉得可以的都可以加图片干了 5 分钟，24 步
搜索 8 次，打开 8 个网页，读 2 个文件，运行 4 条命令，写 1 个文件，其他操作 1 次
思考：规划词汇本与结尾回顾的呈现方式。 挑选并核对各环节适配的候选图片。
规划词汇本与结尾回顾的呈现方式。
挑选并核对各环节适配的候选图片。搜索「John McCarthy computer scientist photo Wikimedia Commons fi…」
{ "allowed_domains": [ "commons.wikimedia.org" ], "mode": "standard", "query": "John McCarthy computer scientist photo Wikimedia Commons file" }Web search results for query: "John McCarthy computer scientist photo Wikimedia Commons file" Links: [{"title":"File:John McCarthy Stanford.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:John_McCarthy_Stanford.jpg"},{"title":"File:John McCarthy (computer scientist) Stanford 2006 (272020300).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:John_McCarthy_(computer_scientist)_Stanford_2006_(272020300).jpg"},{"title":"Category:John McCarthy (computer scientist) - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:John_McCarthy_(computer_scientist)"},{"title":"File:John McCarthy 2006.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:John_McCarthy_2006.jpg"},{"title":"File:John Jay McCarthy.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:John_Jay_McCarthy.jpg"},{"title":"File:J.J. McCarthy B1GCCG.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:J.J._McCarthy_B1GCCG.jpg"},{"title":"File:John F. McCarthy Jr.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:John_F._McCarthy_Jr.jpg"},{"title":"File:John McCarthy MMA ref.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:John_McCarthy_MMA_ref.jpg"},{"title":"Category:John McCarthy, QC - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:John_McCarthy,_QC"},{"title":"Category:John P. McCarthy - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:John_P._McCarthy"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Marvin Minsky photo Wikimedia Commons file」
{ "allowed_domains": [ "commons.wikimedia.org" ], "mode": "standard", "query": "Marvin Minsky photo Wikimedia Commons file" }Web search results for query: "Marvin Minsky photo Wikimedia Commons file" Links: [{"title":"File:Marvin Minsky.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Marvin_Minsky.jpg"},{"title":"File:Marvin Minsky at OLPCc.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Marvin_Minsky_at_OLPCc.jpg"},{"title":"File:Marvin Minsky at OLPCb.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Marvin_Minsky_at_OLPCb.jpg"},{"title":"File:Marvin Minsky at OLPCb (3x4 cropped).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Marvin_Minsky_at_OLPCb_(3x4_cropped).jpg"},{"title":"File:Marvin Minsky at OLPC.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Marvin_Minsky_at_OLPC.jpg"},{"title":"Category:Marvin Minsky - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Marvin_Minsky"},{"title":"File:Minskytron-PDP-1-20070512.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Minskytron-PDP-1-20070512.jpg"},{"title":"File:Minski-confocal-patent-figure1.gif - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Minski-confocal-patent-figure1.gif"},{"title":"File:Primeira Página da Patente de Marvin Minsky Sobre o Microscópio Confocal.gif - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Primeira_P%C3%A1gina_da_Patente_de_Marvin_Minsky_Sobre_o_Microsc%C3%B3pio_Confocal.gif"},{"title":"Category:Minsky's Tentacle Arm - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Minsky's_Tentacle_Arm"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Symbolics Lisp machine 3640 photo Wikimedia Commons」
{ "allowed_domains": [ "commons.wikimedia.org" ], "mode": "standard", "query": "Symbolics Lisp machine 3640 photo Wikimedia Commons" }Web search results for query: "Symbolics Lisp machine 3640 photo Wikimedia Commons" Links: [{"title":"File:Symbolics3640.JPG - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Symbolics3640.JPG"},{"title":"File:Symbolics3640 Modified.JPG - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Symbolics3640_Modified.JPG"},{"title":"Category:Lisp machine - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Lisp_machine"},{"title":"File:Symbolics LISP Machine, Google NY office computer museum.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Symbolics_LISP_Machine,_Google_NY_office_computer_museum.jpg"},{"title":"File:Symbolics LISP Workstation (Computer History Museum).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Symbolics_LISP_Workstation_(Computer_History_Museum).jpg"},{"title":"Category:Symbolics - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Symbolics"},{"title":"File:Symbolics-3600-on.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Symbolics-3600-on.jpg"},{"title":"File:MF 3640.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:MF_3640.jpg"},{"title":"Category:NGC 3640 - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:NGC_3640"},{"title":"File:3640 N Halsted.JPG - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:3640_N_Halsted.JPG"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「NVIDIA GeForce GTX 580 graphics card photo Wikimedia Commons」
{ "allowed_domains": [ "commons.wikimedia.org" ], "mode": "standard", "query": "NVIDIA GeForce GTX 580 graphics card photo Wikimedia Commons" }Web search results for query: "NVIDIA GeForce GTX 580 graphics card photo Wikimedia Commons" Links: [{"title":"Category:Nvidia GeForce 500 series video cards - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Nvidia_GeForce_500_series_video_cards"},{"title":"File:NVIDIA@40nm@Fermi@GF110@GeForce GTX 580@UA10B338 1041A1 N2Y540.000 GF110-375-A1 DSC04152 (27576804994).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:NVIDIA@40nm@Fermi@GF110@GeForce_GTX_580@UA10B338_1041A1_N2Y540.000_GF110-375-A1_DSC04152_(27576804994).jpg"},{"title":"Category:Innovision video cards - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Innovision_video_cards"},{"title":"File:NVIDIA GPU.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:NVIDIA_GPU.jpg"},{"title":"Category:PCIe video cards - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:PCIe_video_cards"},{"title":"Category:Nvidia GeForce 10 series video cards - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Nvidia_GeForce_10_series_video_cards"},{"title":"File:Geforce8800gtx.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Geforce8800gtx.jpg"},{"title":"Category:Video cards - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Video_cards"},{"title":"Category:Nvidia GeForce 6 series video cards - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Nvidia_GeForce_6_series_video_cards"},{"title":"Category:Nvidia GeForce FX series video cards - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Nvidia_GeForce_FX_series_video_cards"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Geoffrey Hinton photo Wikimedia Commons file」
{ "allowed_domains": [ "commons.wikimedia.org" ], "mode": "standard", "query": "Geoffrey Hinton photo Wikimedia Commons file" }Web search results for query: "Geoffrey Hinton photo Wikimedia Commons file" Links: [{"title":"Category:Geoffrey Hinton - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Geoffrey_Hinton"},{"title":"File:Geoffrey E. Hinton, 2024 Nobel Prize Laureate in Physics.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Geoffrey_E._Hinton,_2024_Nobel_Prize_Laureate_in_Physics.jpg"},{"title":"File:Geoffrey Hinton at UBC.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Geoffrey_Hinton_at_UBC.jpg"},{"title":"File:Geoffrey Hinton in 2026.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Geoffrey_Hinton_in_2026.jpg"},{"title":"File:Geoffrey Hinton - Collision 2023 - Centre Stage RCZ 1307 (cropped).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Geoffrey_Hinton_-_Collision_2023_-_Centre_Stage_RCZ_1307_(cropped).jpg"},{"title":"File:Geoffrey E. Hinton, 2024 Nobel Prize Laureate in Physics (cropped1).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Geoffrey_E._Hinton,_2024_Nobel_Prize_Laureate_in_Physics_(cropped1).jpg"},{"title":"File:Geoffrey Hinton at Collision 2024 in Toronto (cropped).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Geoffrey_Hinton_at_Collision_2024_in_Toronto_(cropped).jpg"},{"title":"File:John J. Hopfield and Geoffrey E. Hinton, 2024 Nobel Prize Laureate in Physics.jpg - Wikimedia Commons","url":"https://commons.wi …（中间省略 213 字）… https://commons.wikimedia.org/wiki/File:Deep_Thinkers_on_Deep_Learning_(cropped_to_Geoffrey_Hinton).jpg"},{"title":"File:Hinton-1904-Views of the Tessaract-aka Tesseract.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Hinton-1904-Views_of_the_Tessaract-aka_Tesseract.jpg"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Jensen Huang photo Wikimedia Commons file」
{ "allowed_domains": [ "commons.wikimedia.org" ], "mode": "standard", "query": "Jensen Huang photo Wikimedia Commons file" }Web search results for query: "Jensen Huang photo Wikimedia Commons file" Links: [{"title":"File:Jensen Huang at Computex Taipei 20160531b.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Jensen_Huang_at_Computex_Taipei_20160531b.jpg"},{"title":"File:Jensen Huang at GTC Japan 20161005.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Jensen_Huang_at_GTC_Japan_20161005.jpg"},{"title":"File:Jensen Huang at Computex Taipei 20100531.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Jensen_Huang_at_Computex_Taipei_20100531.jpg"},{"title":"File:Jensen Huang at Computex Taipei 20160531c.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Jensen_Huang_at_Computex_Taipei_20160531c.jpg"},{"title":"File:Jensen Huang 20231109 (cropped2).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Jensen_Huang_20231109_(cropped2).jpg"},{"title":"File:Jensen huang stanford 2026-04-30 008.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Jensen_huang_stanford_2026-04-30_008.jpg"},{"title":"File:Jensen huang stanford 2026-04-30 010.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Jensen_huang_stanford_2026-04-30_010.jpg"},{"title":"File:NVidiaMobileMediaPress JensenHuang.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:NVidiaMobileMediaPress_JensenHuang.jpg"},{"title":"File:Jensen Huang - RTX Blackwell - Nvidia Keynote - CES 2025 Las Vegas (3).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Jensen_Huang_-_RTX_Blackwell_-_Nvidia_Keynote_-_CES_2025_Las_Vegas_(3).jpg"},{"title":"File:P20250430JB-0891.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:P20250430JB-0891.jpg"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Unitree H1 humanoid robot photo Wikimedia Commons」
{ "allowed_domains": [ "commons.wikimedia.org" ], "mode": "standard", "query": "Unitree H1 humanoid robot photo Wikimedia Commons" }Web search results for query: "Unitree H1 humanoid robot photo Wikimedia Commons" Links: [{"title":"File:Unitree G1.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Unitree_G1.jpg"},{"title":"File:Unitree.svg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Unitree.svg"},{"title":"Category:Humanoid robots - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Humanoid_robots"},{"title":"File:Humanoid Robot.webp - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Humanoid_Robot.webp"},{"title":"Category:Humanoid Robotics Project - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Humanoid_Robotics_Project"},{"title":"File:Cronos humanoid robot.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Cronos_humanoid_robot.jpg"},{"title":"File:Halodi Robotics' Perception Engineer With a Humanoid Collaborative Robot.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Halodi_Robotics'_Perception_Engineer_With_a_Humanoid_Collaborative_Robot.jpg"},{"title":"File:Nao humanoid robot.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Nao_humanoid_robot.jpg"},{"title":"File:Robot Platform 3D Robot Image.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Robot_Platform_3D_Robot_Image.jpg"},{"title":"File:REEM-A humanoid robot.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:REEM-A_humanoid_robot.jpg"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.搜索「Demis Hassabis photo Wikimedia Commons file」
{ "allowed_domains": [ "commons.wikimedia.org" ], "mode": "standard", "query": "Demis Hassabis photo Wikimedia Commons file" }Web search results for query: "Demis Hassabis photo Wikimedia Commons file" Links: [{"title":"Category:Demis Hassabis - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Demis_Hassabis"},{"title":"File:Demis Hassabis Royal Society.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Demis_Hassabis_Royal_Society.jpg"},{"title":"File:Demis Hassabis Royal Society (cropped).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Demis_Hassabis_Royal_Society_(cropped).jpg"},{"title":"File:Demis Hassabis, 2024 Nobel Prize Laureate in Chemistry (cropped).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Demis_Hassabis,_2024_Nobel_Prize_Laureate_in_Chemistry_(cropped).jpg"},{"title":"File:Demis Hassabis.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Demis_Hassabis.jpg"},{"title":"File:Demis Hassabis, 2024 Nobel Prize Laureate in Chemistry 6.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Demis_Hassabis,_2024_Nobel_Prize_Laureate_in_Chemistry_6.jpg"},{"title":"File:Demis Hassabis Royal Society (3x4 cropped).jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Demis_Hassabis_Royal_Society_(3x4_cropped).jpg"},{"title":"File:Demis Hassabis, 2024 Nobel Prize Laureate in Chemistry 7.jpg - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/File:Demis_Hassabis,_2024_Nobel_Prize_Laureate_in_Chemistry_7.jpg"},{"title":"File:PhotonQ-Demis Ha …（中间省略 154 字）… /File:PhotonQ-Demis_Hassabis_on_Artificial_Playful_Intelligence_(15366514658)_(2)_(cropped_to_Demis_Hassabis).jpg"},{"title":"Category:Computer scientists from the United Kingdom - Wikimedia Commons","url":"https://commons.wikimedia.org/wiki/Category:Computer_scientists_from_the_United_Kingdom"}] REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.思考：Verifying image licenses, authors, and file details.
Verifying image licenses, authors, and file details.
打开 commons.wikimedia.org/wiki/File:John_McCarthy_Stanford.jpg
{ "prompt": "Report: license (exact), author/attribution as required, original resolution in pixels, file size, date taken, and a one-line description of the image (is the face clear?).", "url": "https://commons.wikimedia.org/wiki/File:John_McCarthy_Stanford.jpg" }{"error_type":"URL_NOT_FOUND","source":"target","message":"There was an error while fetching."}打开 commons.wikimedia.org/wiki/File:Marvin_Minsky_at_OLPCb.jpg
{ "prompt": "Report: license (exact), author/attribution as required, original resolution in pixels, file size, date taken, and a one-line description of the image (is the face clear?).", "url": "https://commons.wikimedia.org/wiki/File:Marvin_Minsky_at_OLPCb.jpg" }{"error_type":"URL_NOT_FOUND","source":"target","message":"There was an error while fetching."}打开 commons.wikimedia.org/wiki/File:Symbolics3640_Modified.JPG
{ "prompt": "Report: license (exact), author/attribution as required, original resolution in pixels, file size, date taken, and a one-line description of the image.", "url": "https://commons.wikimedia.org/wiki/File:Symbolics3640_Modified.JPG" }{"error_type":"URL_NOT_FOUND","source":"target","message":"There was an error while fetching."}打开 commons.wikimedia.org/wiki/Category:Nvidia_GeForce_500_seri…
{ "prompt": "List the file names in this category that show a GeForce GTX 580 graphics card (the whole card, not just the chip). Give the exact File: names.", "url": "https://commons.wikimedia.org/wiki/Category:Nvidia_GeForce_500_series_video_cards" }{"error_type":"URL_NOT_FOUND","source":"target","message":"There was an error while fetching."}打开 commons.wikimedia.org/wiki/File:Geoffrey_E._Hinton,_2024_No…
{ "prompt": "Report: license (exact), author/attribution as required, original resolution in pixels, file size, date taken, and a one-line description of the image (is the face clear?).", "url": "https://commons.wikimedia.org/wiki/File:Geoffrey_E._Hinton,_2024_Nobel_Prize_Laureate_in_Physics.jpg" }{"error_type":"URL_NOT_FOUND","source":"target","message":"There was an error while fetching."}打开 commons.wikimedia.org/wiki/File:Jensen_Huang_20231109_(crop…
{ "prompt": "Report: license (exact), author/attribution as required, original resolution in pixels, file size, date taken, and a one-line description of the image (is the face clear?).", "url": "https://commons.wikimedia.org/wiki/File:Jensen_Huang_20231109_(cropped2).jpg" }{"error_type":"URL_NOT_FOUND","source":"target","message":"There was an error while fetching."}打开 commons.wikimedia.org/wiki/File:Unitree_G1.jpg
{ "prompt": "Report: license (exact), author/attribution as required, original resolution in pixels, file size, date taken, and a one-line description of the image (is the robot clearly visible, whole body?).", "url": "https://commons.wikimedia.org/wiki/File:Unitree_G1.jpg" }{"error_type":"URL_NOT_FOUND","source":"target","message":"There was an error while fetching."}打开 commons.wikimedia.org/wiki/File:Demis_Hassabis,_2024_Nobel_…
{ "prompt": "Report: license (exact), author/attribution as required, original resolution in pixels, file size, date taken, and a one-line description of the image (is the face clear?).", "url": "https://commons.wikimedia.org/wiki/File:Demis_Hassabis,_2024_Nobel_Prize_Laureate_in_Chemistry_(cropped).jpg" }{"error_type":"URL_NOT_FOUND","source":"target","message":"There was an error while fetching."}思考：Retrieving an image from Wikimedia Commons.
Retrieving an image from Wikimedia Commons.
读取 fetch.py
{ "path": "~/Movies/ai-tree-video/photos/fetch.py" }[Reading 37 lines from start (total: 37 lines, 0 remaining)] import json, re, time, urllib.parse, urllib.request, urllib.error UA = {"User-Agent": "ai-tree-video/0.1 (personal non-commercial explainer video)"} API = "https://commons.wikimedia.org/w/api.php" def get(url, tries=6): for k in range(tries): try: with urllib.request.urlopen(urllib.request.Request(url, headers=UA), timeout=60) as r: return r.read() except urllib.error.HTTPError as e: if e.code in (429, 503): wait = int(e.headers.get("Retry-After") or 0) or 15 * (k + 1) print(f"HTTP {e.code}, waiting {wait}s", flush=True); time.sleep(wait); continue raise raise SystemExit("gave up: " + url) def api(**p): p["format"] = "json"; time.sleep(3) return json.loads(get(API + "?" + urllib.parse.urlencode(p))) strip = lambda h: re.sub(r"\s+", " ", re.sub("<[^>]+>", "", h or "")).strip() s = api(action="query", list="search", srsearch="Shakey the Robot developed between 1966-1972 SRI International Computer History Museum", srnamespace=6, srlimit=10) shakey = next(h["title"] for h in s["query"]["search"] if h["title"].startswith("File:Shakey the Robot (developed between 1966-1972 at SRI International)")) want = {"turing": "File:Alan Turing (1951).jpg", "perceptron": "File:Mark I Perceptron, Figure 2 of operator's manual.png", "shakey": shakey, "deepblue": "File:Deep Blue.jpg", "stanley": "File:Stanley2.JPG …（中间省略 794 字）… rl", {}).get("value")), "artist": strip(m.get("Artist", {}).get("value")), "credit": strip(m.get("Credit", {}).get("value"))[:200], "bytes": len(data), "original": f'{ii["width"]}x{ii["height"]}'} print(key, len(data), "bytes", credits[key]["license"], "|", credits[key]["artist"], flush=True) json.dump(credits, open("photos/credits.json", "w"), ensure_ascii=False, indent=1)读取 credits.json
{ "path": "~/Movies/ai-tree-video/photos/credits.json" }[Reading 52 lines from start (total: 52 lines, 0 remaining)] { "turing": { "title": "File:Alan Turing (1951).jpg", "page": "https://commons.wikimedia.org/wiki/File:Alan_Turing_(1951).jpg", "license": "Public domain", "license_url": "", "artist": "Elliott & Fry", "credit": "https://www.computerhistory.org/timeline/1949/", "bytes": 92890, "original": "800x1067" }, "perceptron": { "title": "File:Mark I Perceptron, Figure 2 of operator's manual.png", "page": "https://commons.wikimedia.org/wiki/File:Mark_I_Perceptron,_Figure_2_of_operator%27s_manual.png", "license": "Public domain", "license_url": "", "artist": "John C. Hay, Albert E. Murray", "credit": "https://apps.dtic.mil/sti/tr/pdf/AD0236965.pdf", "bytes": 200782, "original": "1166x945" }, "shakey": { "title": "File:Shakey the Robot (developed between 1966-1972 at SRI International) - Computer History Museum (2007-11-10 23.16.01 by Carlo Nardone).jpg", "page": "https://commons.wikimedia.org/wiki/File:Shakey_the_Robot_(developed_between_1966-1972_at_SRI_International)_-_Computer_History_Museum_(2007-11-10_23.16.01_by_Carlo_Nardone).jpg", "license": "CC BY-SA 2.0", "license_url": "https://creativecommons.org/licenses/by-sa/2.0", "artist": "Carlo Nardone from Roma, Italy", "credit": "Computer Museum: Shakley Robot", "bytes": 312323, "original": "1704x2272" }, "deepblue": { "title": "File:Deep Blue.jpg", "page": "https://commons.wikimedia.org/wiki/File:Deep_Blue.jpg", "license": "CC BY 2.0", "license_url": "https://creativecommons.org/licenses/by/2.0", "artist": "James the photographer", "credit": "https://www.flickr.com/photos/22453761@N00/592436598/", "bytes": 97206, "original": "400x601" }, "stanley": { "title": "File:Stanley2.JPG", "page": "https://commons.wikimedia.org/wiki/File:Stanley2.JPG", "license": "Public domain", "license_url": "", "artist": "", "credit": "", "bytes": 150336, "original": "2560x1668" } }思考：Building a metadata-only lookup for Commons image details. C…
Building a metadata-only lookup for Commons image details.
Compiling candidate image titles for each historical moment.
Weighing whether to add photos for Go and Nobel moments.
Spotting gaps where new photos could fill empty moments.写入 lookup.py
"""Metadata only (no image downloads): license, author, size of candidate photos on Wikimedia Commons.""" import json, re, sys, time, urllib.parse, urllib.request, urllib.error UA = {"User-Agent": "ai-tree-video/0.1 (personal non-commercial explainer video)"} API = "https://commons.wikimedia.org/w/api.php" def get(url, tries=6, head=False): for k in range(tries): try: req = urllib.request.Request(url, headers=UA, method="HEAD" if head else "GET") with urllib.request.urlopen(req, timeout=60) as r: return r.headers if head else r.read() except urllib.error.HTTPError as e: if e.code in (429, 503): wait = int(e.headers.get("Retry-After") or 0) or 15 * (k + 1) print(f"HTTP {e.code}, waiting {wait}s", file=sys.stderr, flush=True); time.sleep(wait); continue raise raise SystemExit("gave up: " + url) def api(**p): p["format"] = "json"; time.sleep(2) return json.loads(get(API + "?" + urllib.parse.urlencode(p))) strip = lambda h: re.sub(r"\s+", " ", re.sub("<[^>]+>", "", h or "")).strip …（中间省略 1540 字）… t("DateTimeOriginal", {}).get("value"))[:20], "original": f'{ii["width"]}x{ii["height"]}', "download_bytes": n, "download_url": url, "desc": strip(m.get("ImageDescription", {}).get("value"))[:140]}) print(json.dumps(out, ensure_ascii=False, indent=1))Successfully wrote to ~/Movies/ai-tree-video/photos/lookup.py (54 lines) ✅ File written successfully! (54 lines) 💡 Performance tip: For optimal speed, consider chunking files into ≤30 line pieces in future operations.运行 cd ~/Movies/ai-tree-video && .venv/bin/python photos/lookup.py "F…
cd ~/Movies/ai-tree-video && .venv/bin/python photos/lookup.py "File:John McCarthy Stanford.jpg" "File:John McCarthy (computer scientist) Stanford 2006 (272020300).jpg" "File:Marvin Minsky at OLPCb (3x4 cropped).jpg" "File:Marvin Minsky at OLPCb.jpg" "File:Symbolics3640 Modified.JPG" "File:Symbolics LISP Workstation (Computer History Museum).jpg" "File:Geoffrey E. Hinton, 2024 Nobel Prize Laureate in Physics (cropped1).jpg" "File:Geoffrey E. Hinton, 2024 Nobel Prize Laureate in Physics.jpg" "File:Jensen Huang 20231109 (cropped2).jpg" "File:Jensen Huang - RTX Blackwell - Nvidia Keynote - CES 2025 Las Vegas (3).jpg" "File:Unitree G1.jpg" "File:Demis Hassabis, 2024 Nobel Prize Laureate in Chemistry (cropped).jpg" > photos/lookup.json 2> photos/lookup.err; tail -3 photos/lookup.err; head -c 200 photos/lookup.json
Process started with PID 28545 (shell: /bin/zsh) Initial output: # extra candidates: ["File:EVGAGeforceGTX580.jpg", "File:Inno3D HAWX 580GTX.jpg", "File:Nvidia@40nm@Fermi@GF110@GeForce GTX 580@UA10B338 1041A1 N2Y540.000 GF110-375-A1 DSCx2 [邮箱]", "File ✅ Process 28545 has finished execution
运行 cd ~/Movies/ai-tree-video && /usr/bin/python3 -c "
cd ~/Movies/ai-tree-video && /usr/bin/python3 -c " import json d = json.load(open('photos/lookup.json')) for x in d: if x.get('missing'): print('MISSING', x['title']); continue print('|'.join([x['title'][5:80], x['license'], x['artist'][:40], x['original'], str(round(x['download_bytes']/1024)) + 'KB', x['date'][:10], x['desc'][:90]])) "Process started with PID 28752 (shell: /bin/zsh) Initial output: Traceback (most recent call last): File "<string>", line 3, in <module> File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/__init__.py", line 293, in load return loads(fp.read(), File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/__init__.py", line 346, in loads return _default_decoder.decode(s) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/json/decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) ✅ Process 28752 has finished execution运行 cd ~/Movies/ai-tree-video && /usr/bin/python3 -c "
cd ~/Movies/ai-tree-video && /usr/bin/python3 -c " import json s = open('photos/lookup.json').read(); s = s[s.index('\n')+1:] d = json.loads(s) for x in d: if x.get('missing'): print('MISSING', x['title']); continue print('|'.join([x['title'][5:80], x['license'], x['artist'][:40], x['original'], str(round(x['download_bytes']/1024)) + 'KB', x['date'][:10], x['desc'][:90]])) "Process started with PID 28843 (shell: /bin/zsh) Initial output: Demis Hassabis, 2024 Nobel Prize Laureate in Chemistry (cropped).jpg|CC BY-SA 4.0|Arthur Petron|1141x1306|206KB|2024-12-08|Demis Hassabis of the United Kingdom, 2024 Nobel Prize Laureate in Chemistry, at the press EVGAGeforceGTX580.jpg|CC BY-SA 4.0|TheStriker|4031x1910|139KB|2023-02-09|EVGA-manufactured Nvidia Geforce GTX 580 video card from late 2010 Geoffrey E. Hinton, 2024 Nobel Prize Laureate in Physics.jpg|CC BY-SA 4.0|Arthur Petron|1610x3256|269KB|2024-12-08|Geoffrey E. Hinton of Canada, 2024 Nobel Prize Laureate in Physics, at the press conferenc Geoffrey E. Hinton, 2024 Nobel Prize Laureate in Physics (cropped1).jpg|CC BY-SA 4.0|Arthur Petron|1070x1074|123KB|2024-12-08|Geoffrey E. Hinton of Canada, 2024 Nobel Prize Laureate in Physics, at the press conferenc Inno3D HAWX 580GTX.jpg|CC BY-SA 3.0|Martin Soubre|1570x765|70KB|2011-08-17|photo d'une carte graphique inno3D Geforce 580GTX HAWX Jensen Huang - RTX Blackwell - Nvidia Keynote - CES 2025 Las Vegas (3).jpg|CC0|Pronoia|4032x3024|129KB|2025-01-06|Jensen Huang présentant la prochaine génératioin de RTX Blackwell GPUs lors de la Nvidia K Jensen Huang 20231109 (cropped2).jpg|CC BY 2.0|總統府|1080x1464|189KB|2023-11-09|黃仁勳出席第一屆李國鼎獎頒獎典禮 John McCarthy (computer scientist) Stanford 2006 (272020300).jpg|CC BY 2.0|null0 from Singapore, Singapore|3504x2332|123KB|2006-05-13|Professor "Uncle John" McCarthy discusses LISP. John McCarthy Stanford.jpg|CC BY-SA 2.0|"null …（中间省略 1419 字）… lisp machine running the Genera operating system in the Retro-Computing Soc Symbolics LISP Workstation (Computer History Museum).jpg|CC BY-SA 2.0|Thomas Conté|2592x1936|164KB|2011-07-30|Symbolics LISP Workstation at the Computer History Museum. ("3620 Lisp Workstation CPU, Sy Unitree G1.jpg|CC0|Sayanesy|1380x2326|209KB|2024-11-18|Unitree G1 at Uav expo 2024 ✅ Process 28843 has finished execution思考：Finding properly licensed photos for each notable figure. We…
Finding properly licensed photos for each notable figure.
Weighing image resolution and source credits for each portrait.
Debating whether a photo or illustration tells the story better.
Designing a composite portrait for two paired Nobel laureates.
Finalizing which photos and captions make the timeline cut.运行 cat ~/ai-tree/out/deliver-port.log; ls -la ~/ai-tree/out/…
cat ~/ai-tree/out/deliver-port.log; ls -la ~/ai-tree/out/v2/; cat ~/ai-tree/out/final.done 2>/dev/null
ai-tree-v2-portrait-1080p.mp4 121.5 MiB 1080 x 1920 frames 7529 dur 313.708333 ai-tree-v2-portrait-720p.mp4 27.4 MiB 720 x 1280 frames 7529 dur 313.708333 total 322436 drwxr-xr-x 2 root root 4096 Oct 15 05:51 . drwxr-xr-x 10 root root 4096 Oct 15 05:55 .. -rw-r--r-- 1 root root 145384700 Oct 15 05:37 ai-tree-v2-landscape-1080p.mp4 -rw-r--r-- 1 root root 28677824 Oct 15 05:42 ai-tree-v2-landscape-720p.mp4 -rw-r--r-- 1 root root 127395041 Oct 15 05:50 ai-tree-v2-portrait-1080p.mp4 -rw-r--r-- 1 root root 28699715 Oct 15 05:55 ai-tree-v2-portrait-720p.mp4 ALLDONE
把文件发给你
{ "caption": "竖屏版（已改好核查出的问题，27 MB），可以先在手机上看排版。加照片和「词汇本」的新版接下来做。", "display": "render", "files": [ "~/ai-tree/out/v2/ai-tree-v2-portrait-720p.mp4" ], "status": "normal" }1 file delivered to user. ~/ai-tree/out/v2/ai-tree-v2-portrait-720p.mp4 → file_uuid: 09764810-b2b7-48bf-aaa8-c3b3d00243b0 Each file shows as a card whose position relative to your text depends on the app the user is reading in, so refer to a file by its name, never as above or below.
维基共享资源上能合法使用的照片我都查过了，下面这几张都允许署名后放进视频。下载前需要你确认一下，都是从 commons.wikimedia.org 下的 900px 版本，一共约 1.5 MB：
放在哪 文件 授权 / 作者 大小 1956 起名「人工智能」 John McCarthy Stanford.jpg CC BY-SA 2.0 · null0 257 KB 1969「被一本书泼了冷水」 Marvin Minsky at OLPCb (3x4 cropped).jpg CC BY 3.0 · Sethwoodworth 202 KB 80 年代专家系统 Symbolics LISP Workstation (Computer History Museum).jpg CC BY-SA 2.0 · Thomas Conté 164 KB 2012「两块打游戏的显卡」 EVGAGeforceGTX580.jpg CC BY-SA 4.0 · TheStriker 139 KB 2016「赢了李世石」 Lee Se-Dol - 2016 (cropped).jpg CC BY 2.0 · LG Electronics 95 KB 2024 英伟达 Jensen Huang 20231109 (cropped2).jpg CC BY 2.0 · 總統府 189 KB 2024 两个诺贝尔奖 Geoffrey E. Hinton, 2024 Nobel Prize Laureate in Physics (cropped1).jpg CC BY-SA 4.0 · Arthur Petron 123 KB 同上 Demis Hassabis, 2024 Nobel Prize Laureate in Chemistry (cropped).jpg CC BY-SA 4.0 · Arthur Petron 206 KB 2026 宇树上市 Unitree G1.jpg CC0 · Sayanesy 209 KB 怎么放进片子：
- 辛顿和哈萨比斯拼成一张「两个诺奖」的照片，替换现在的奖牌插画。
- 黄仁勋替换「卖铲子」的插画。
- 李世石接在围棋棋盘后面出现，图注写「李世石 1:4 AlphaGo」。
- 其余几张放在原来没有配图的地方。
结尾的署名会补上所有新照片的作者和授权。
全部下载
干了 21 分钟，37 步
读 15 个文件，运行 13 条命令，写 1 个文件，其他操作 8 次
思考：Writing a script to download the approved photos.
Writing a script to download the approved photos.
新建待办
{ "activeForm": "Adding licensed photos", "description": "麦卡锡、明斯基、Lisp 机、GTX 580、李世石、黄仁勋、辛顿+哈萨比斯、宇树 G1；替换奖牌和卖铲子插画；结尾署名更新。", "subject": "加 9 张授权照片（下载、拼诺奖合照、排进时间轴、补署名）" }Task #19 created successfully: 加 9 张授权照片（下载、拼诺奖合照、排进时间轴、补署名）
新建待办
{ "activeForm": "Building the vocabulary notebook", "description": "过程中按树干四段（写规则/找规律/神经网络/大模型）显示收集进度，新词飞入时格子亮起并闪出词名；结尾旁白「从写规则到会学习」时，14 个词按段挂回树干旁。", "subject": "「已学会 N 个词」改成词汇本 + 结尾回顾" }Task #20 created successfully: 「已学会 N 个词」改成词汇本 + 结尾回顾
新建待办
{ "activeForm": "Rendering and delivering the new version", "description": "静帧自查 + 排版扫描，全片渲染，编码，720p 发到对话，四个文件放进你电脑的 ai-tree-video 文件夹并校验。", "subject": "渲染新版并交付（横竖屏 1080p + 720p）" }Task #21 created successfully: 渲染新版并交付（横竖屏 1080p + 720p）
写入 fetch2.py
"""Download the 9 approved photos (900px versions) from Wikimedia Commons and add their credits to credits.json.""" import json, re, time, urllib.parse, urllib.request, urllib.error UA = {"User-Agent": "ai-tree-video/0.1 (personal non-commercial explainer video)"} API = "https://commons.wikimedia.org/w/api.php" def get(url, tries=6): for k in range(tries): try: with urllib.request.urlopen(urllib.request.Request(url, headers=UA), timeout=60) as r: return r.read() except urllib.error.HTTPError as e: if e.code in (429, 503): wait = int(e.headers.get("Retry-After") or 0) or 15 * (k + 1) print(f"HTTP {e.code}, waiting {wait}s", flush=True); time.sleep(wait); continue raise raise SystemExit("gave up: " + url) def api(**p): p["format"] = "json"; time.sleep(3) return json.loads(get(API + "?" + urllib.parse.urlencode(p))) strip = lambda h: re.sub(r"\s+", " ", re.sub("<[^>]+>", "", h or "")).strip() want = {"mccarthy": "File:John McCarthy Stanford.jpg", "minsky": "File:Marvin Minsky at OLPCb (3x4 c …（中间省略 1416 字）… m.get("Credit", {}).get("value"))[:200], "bytes": len(data), "original": f'{ii["width"]}x{ii["height"]}'} print(key, ext, len(data), "bytes", credits[key]["license"], "|", credits[key]["artist"][:60], flush=True) json.dump(credits, open("photos/credits.json", "w"), ensure_ascii=False, indent=1)Successfully wrote to ~/Movies/ai-tree-video/photos/fetch2.py (45 lines)
运行 cd ~/Movies/ai-tree-video && cp photos/credits.json photos/credit…
cd ~/Movies/ai-tree-video && cp photos/credits.json photos/credits-v2.0.json && .venv/bin/python photos/fetch2.py 2>&1 | tail -12 && ls -la photos/
Process started with PID 29770 (shell: /bin/zsh) Initial output: mccarthy org&utm_campaign=imageinfo&utm_content=thumbnail 262670 bytes CC BY-SA 2.0 | "null0" minsky org&utm_campaign=imageinfo&utm_content=original 206469 bytes CC BY 3.0 | The original uploader was Sethwoodworth at English Wikipedia lisp org&utm_campaign=imageinfo&utm_content=thumbnail 168326 bytes CC BY-SA 2.0 | Thomas Conté gtx580 org&utm_campaign=imageinfo&utm_content=thumbnail 141935 bytes CC BY-SA 4.0 | TheStriker leesedol org&utm_campaign=imageinfo&utm_content=original 97567 bytes CC BY 2.0 | LG Electronics huang org&utm_campaign=imageinfo&utm_content=thumbnail 193138 bytes CC BY 2.0 | 總統府 hinton org&utm_campaign=imageinfo&utm_content=thumbnail 125861 bytes CC BY-SA 4.0 | Arthur Petron hassabis org&utm_campaign=imageinfo&utm_content=thumbnail 210559 bytes CC BY-SA 4.0 | Arthur Petron unitree org&utm_campaign=imageinfo&utm_content=thumbnail 213588 bytes CC0 | Sayanesy total 4968 drwxr-xr-x@ 23 niracler staff 736 Oct 15 14:17 . drwxr-xr-x@ 9 niracler staff 288 Oct 15 12:58 .. -rw-r--r--@ 1 niracler staff 1927 Oct 15 14:16 credits-v2.0.json -rw-r--r--@ 1 niracler staff 5478 Oct 15 14:17 credits.json -rw-r--r--@ 1 niracler staff 97206 Oct 15 10:07 deepblue.jpg -rw-r--r--@ 1 niracler staff 2633 Oct 15 10:07 fetch.py -rw-r--r--@ 1 niracler staff 2841 Oct 15 14:16 fetch2.py -rw-r--r--@ 1 niracler staff 141935 Oct 15 14:16 gtx580.org&utm_campaign=imageinfo&utm_content=thumbn …（中间省略 955 字）… --r--@ 1 niracler staff 200782 Oct 15 10:07 perceptron.png -rw-r--r--@ 1 niracler staff 312323 Oct 15 10:07 shakey.jpg -rw-r--r--@ 1 niracler staff 150336 Oct 15 10:07 stanley.jpg -rw-r--r--@ 1 niracler staff 92890 Oct 15 10:07 turing.jpg -rw-r--r--@ 1 niracler staff 213588 Oct 15 14:17 unitree.org&utm_campaign=imageinfo&utm_content=thumbnail ✅ Process 29770 has finished execution
思考：Fixing odd filenames caused by lingering URL query strings.
Fixing odd filenames caused by lingering URL query strings.
运行 cd ~/Movies/ai-tree-video/photos && for f in *.org\&utm_campaign=…
cd ~/Movies/ai-tree-video/photos && for f in *.org\&utm_campaign=*; do k="${f%%.org&*}"; t=$(file -b --mime-type "$f"); case "$t" in image/jpeg) e=jpg;; image/png) e=png;; *) e=bin;; esac; mv "$f" "$k.$e"; echo "$k.$e $t"; done; ls *.jpg *.pngProcess started with PID 29853 (shell: /bin/zsh) Initial output: gtx580.jpg image/jpeg hassabis.jpg image/jpeg hinton.jpg image/jpeg huang.jpg image/jpeg leesedol.jpg image/jpeg lisp.jpg image/jpeg mccarthy.jpg image/jpeg minsky.jpg image/jpeg unitree.jpg image/jpeg deepblue.jpg gtx580.jpg hassabis.jpg hinton.jpg huang.jpg leesedol.jpg lisp.jpg mccarthy.jpg minsky.jpg perceptron.png shakey.jpg stanley.jpg turing.jpg unitree.jpg ✅ Process 29853 has finished execution
准备从你电脑传回文件
{ "paths": [ "~/Movies/ai-tree-video/photos/gtx580.jpg", "~/Movies/ai-tree-video/photos/hassabis.jpg", "~/Movies/ai-tree-video/photos/hinton.jpg", "~/Movies/ai-tree-video/photos/huang.jpg", "~/Movies/ai-tree-video/photos/leesedol.jpg", "~/Movies/ai-tree-video/photos/lisp.jpg", "~/Movies/ai-tree-video/photos/mccarthy.jpg", "~/Movies/ai-tree-video/photos/minsky.jpg", "~/Movies/ai-tree-video/photos/unitree.jpg", "~/Movies/ai-tree-video/photos/credits.json" ] }{"staged":[{"devicePath":"~/Movies/ai-tree-video/photos/gtx580.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/gtx580.jpg","mtimeMs":1792045018135,"bytes":141935,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/hassabis.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/hassabis.jpg","mtimeMs":1792045033885,"bytes":210559,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/hinton.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/hinton.jpg","mtimeMs":1792045029962,"bytes":125861,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/huang.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/huang.jpg","mtimeMs":1792045026042,"bytes":193138,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/leesedol.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/leesedol.jpg","mtimeMs":1792045022153,"bytes":97567,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/lisp.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/lisp.jpg","mtimeMs":1792045013169,"bytes":168326,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/mccarthy.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/mccarthy.jpg","mtimeMs":1792045004838,"bytes":262670,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/minsky.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/minsky.jpg","mtimeMs":1792045008890,"bytes":206469,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/unitree.jpg","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/unitree.jpg","mtimeMs":1792045037797,"bytes":213588,"ok":true,"dispatched":true},{"devicePath":"~/Movies/ai-tree-video/photos/credits.json","stagedPath":"/mnt/user-data/uploads/ai-tree-video/photos/credits.json","mtimeMs":1792045037798,"bytes":5478,"ok":true,"dispatched":true}]}运行 sleep 2; cd ~/ai-tree && U=/mnt/user-data/uploads/ai-tree-video/phot…
sleep 2; cd ~/ai-tree && U=/mnt/user-data/uploads/ai-tree-video/photos && for k in gtx580 hassabis hinton huang leesedol lisp mccarthy minsky unitree; do cp $U/$k.jpg assets/photos/; done && cp $U/credits.json assets/photos/credits.json && python3 - <<'EOF' from PIL import Image import json for k in ['mccarthy','minsky','lisp','gtx580','leesedol','huang','hinton','hassabis','unitree']: im = Image.open(f'~/ai-tree/assets/photos/{k}.jpg'); print(k, im.size, im.mode) c = json.load(open('~/ai-tree/assets/photos/credits.json')) for k, v in c.items(): print(k, '|', v['license'], '|', v['artist'][:60]) EOFmccarthy (960, 1112) RGB minsky (770, 1025) RGB lisp (960, 717) RGB gtx580 (960, 455) RGB leesedol (367, 550) RGB huang (960, 1301) RGB hinton (960, 964) RGB hassabis (960, 1099) RGB unitree (960, 1618) RGB turing | Public domain | Elliott & Fry perceptron | Public domain | John C. Hay, Albert E. Murray shakey | CC BY-SA 2.0 | Carlo Nardone from Roma, Italy deepblue | CC BY 2.0 | James the photographer stanley | Public domain | mccarthy | CC BY-SA 2.0 | "null0" minsky | CC BY 3.0 | The original uploader was Sethwoodworth at English Wikipedia lisp | CC BY-SA 2.0 | Thomas Conté gtx580 | CC BY-SA 4.0 | TheStriker leesedol | CC BY 2.0 | LG Electronics huang | CC BY 2.0 | 總統府 hinton | CC BY-SA 4.0 | Arthur Petron hassabis | CC BY-SA 4.0 | Arthur Petron unitree | CC0 | Sayanesy
运行 python3 - <<'EOF'
python3 - <<'EOF' from PIL import Image, ImageDraw ks = ['mccarthy','minsky','lisp','gtx580','leesedol','huang','hinton','hassabis','unitree'] S = Image.new('RGB', (5 * 300, 2 * 330), 'white'); d = ImageDraw.Draw(S) for i, k in enumerate(ks): im = Image.open(f'assets/photos/{k}.jpg'); im.thumbnail((290, 300)) x, y = (i % 5) * 300, (i // 5) * 330 S.paste(im, (x + 5, y + 25)); d.text((x + 6, y + 6), f"{k} {Image.open(f'assets/photos/{k}.jpg').size}", fill='red') S.save('out/frames/newphotos.png'); print('ok') EOFok
读取 newphotos.png
{ "file_path": "~/ai-tree/out/frames/newphotos.png" }[图片]
思考：Setting crop focus points for each portrait photo. Composing…
Setting crop focus points for each portrait photo.
Composing a composite layout and captions for each photo.
为每张照片撰写简短说明文字。
为每张照片关联时间轴触发点和时长。
规划词汇本图标与分组标签的展示设计。
用树干色系为词汇分组配色并匹配进度文字。
计算横竖屏布局下词汇本元素的具体定位。
调整横竖屏下词汇本元素以避免与拍立得照片重叠。
设计结尾词汇回顾面板与引导线布局。
调整回顾面板行间距以避开视觉重叠。
为竖屏结尾回顾面板规划顶部留白布局。
为回顾面板的词条分组设计高亮联动效果。
为词汇徽章动画规划卡片位置与配色方案。更新待办
{ "status": "in_progress", "taskId": "19" }Updated task #19 status
运行 python3 - <<'EOF'
python3 - <<'EOF' from PIL import Image # the two 2024 Nobel laureates side by side in one photo (each cropped to head and shoulders) h = Image.open('assets/photos/hinton.jpg').crop((120, 40, 800, 780)) # 680x740 d = Image.open('assets/photos/hassabis.jpg').crop((140, 90, 820, 830)) # 680x740 W, H, gap = 960, 522, 10 cw = (W - gap) // 2 out = Image.new('RGB', (W, H), (250, 248, 240)) out.paste(h.resize((cw, H), Image.LANCZOS), (0, 0)); out.paste(d.resize((cw, H), Image.LANCZOS), (cw + gap, 0)) out.save('assets/photos/nobel.jpg', quality=92) print(out.size) EOF python3 -c " import json s = open('~/ai-tree/scene/timeline.js').read(); TL = json.loads(s[len('window.TL = '):].rstrip().rstrip(';')) for b in TL['beats']: print(b['id'], b['ts'], b['te']) for c in TL['captions']: if c['t0'] < 75 or 95 < c['t0'] < 125 or 166 < c['t0'] < 200 or 260 < c['t0'] < 287: print(round(c['t0'],2), round(c['t1'],2), c['text']) print([(p['id'], p['t'], p['dur']) for p in TL['polaroids']])"(960, 522) intro 0.8 6.968 b1 11.568 22.032 b2 23.132 39.596 b3 40.796 72.268 b4 74.668 87.916 b5 91.316 125.092 b6 126.292 151.372 b7 153.372 164.676 b8 166.376 198.8 b9 200.0 238.904 b10 240.104 282.272 end 288.072 306.72 0.9 2.34 这是一棵树的故事 2.63 6.38 它长了七十多年，最近这三年，突然爆发了 11.67 15.11 1950年，图灵问：机器能思考吗 15.39 18.66 六年后，一群科学家在达特茅斯开会 18.7 21.44 给这件事起了个名字——人工智能 23.23 24.36 1958年 24.4 26.49 有人造出了模仿大脑的“感知机” 26.63 28.59 这是神经网络的第一颗芽 28.87 32.35 每个“神经元”把输入乘上权重、加起来 32.45 35.07 所谓学习，就是不断调这些权重 35.37 39.01 可十来年后，它被一本书泼了冷水，冻住了 40.89 44.25 那时的做法，是把规则一条条写给电脑 44.55 47.81 说得太多、做到太少，钱被砍了 47.87 49.0 第一个冬天来了 50.7 51.76 八十年代 51.93 55.65 “专家系统”把专家的经验写成“如果……就……” 55.6 56.78 风光一时 57.08 60.22 1986年，冻住的那颗芽也开始化冻 60.3 62.86 “反向传播”流行开来——答错了 62.78 64.67 就把误差从后往前传 64.72 67.04 算出每个权重该往哪边拧一点 67.33 70.09 而专家系统的规则越写越难维护 70.2 71.7 第二个冬天又来了 74.77 77.12 后来换了条路：不写规则了 95.81 98.78 很深的神经网络，喂进海量图片 98.8 100.96 用两块打游戏的显卡来算 100.98 102.38 这就是“深度学习” 102.68 105.97 前几层学会认边缘，中间几层认形状 106.05 108.15 最后几层认出整只猫 108.44 111.58 2016年，AlphaGo先学人类棋谱 111.5 113.77 再自己跟自己下，赢了李世石 114.07 116.36 2017年，Transformer诞生 116.39 118.88 后来GPT里的T，就是它 119.16 121.97 它的核心叫“注意力”：读每个词时 121.94 124.52 都回头看一遍句子里所有的词 166.47 170.34 2024年，打一行字就能生成电影般的视频 170.35 173.41 背后是“扩散模型”：从一片噪点开始 173.41 175.72 一步步“去噪”，把画面擦出来 176.01 178.04 卖AI芯片的英伟达 178.08 180.46 一度成了全世界最值钱的公司 180.76 184.5 夏天，AI还会把9.11和9.9比错 184.57 187.64 九月，它学会了先打一长段草稿 187.59 190.0 也就是“思维链”，想清楚了再回答 190.28 193.91 十月，两个诺贝尔奖颁给了AI研究 193.98 195.26 其中一位得主 195.17 198.21 就是几十年来一直坚持神经网络的辛顿 261.53 265.63 AI会看、会听、会画，还开始有了 …（中间省略 269 字）… 40.794, 4.0), ('photo:deepblue', 84.294, 4.2), ('photo:stanley', 88.066, 3.0), ('ill:go', 109.445, 4.6), ('ill:chat', 143.68, 4.0), ('ill:projector', 167.576, 3.0), ('ill:shovel', 175.907, 3.6), ('ill:nobel', 191.431, 4.4), ('ill:boat', 201.557, 3.2), ('ill:gala1', 209.626, 3.2), ('ill:melt', 223.817, 3.4), ('ill:lobster', 244.468, 3.6), ('ill:gala2', 256.683, 3.6), ('ill:animator', 274.921, 4.4)]思考：Scheduling timed polaroid entries with fades and durations. …
Scheduling timed polaroid entries with fades and durations.
Resolving overlaps between consecutive caption-triggered entries.运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('tools/build_timeline.py'); s = p.read_text() old_start = s.index("POLAROIDS = [") old_end = s.index("]", s.index("(\"ill:animator\"")) + 1 s = s[:old_start] + '''POLAROIDS = [ ("photo:turing", "b1", "图", 3.6), ("photo:mccarthy", "b1", "达特茅斯", 3.6), ("photo:perceptron", "b2", "感知", 4.4), ("photo:minsky", "b2", "一本", 2.6), ("photo:shakey", "b3", "那时", 4.0), ("photo:lisp", "b3", "风光", 3.6), ("photo:deepblue", "b4", "深蓝", 4.2), ("photo:stanley", "b4", "@post", 3.0), ("photo:gtx580", "b5", "打游戏", 3.0), ("ill:go", "b5", "AlphaGo", 2.4), ("photo:leesedol", "b5", "李世石", 3.2), ("ill:chat", "b6", "ChatGPT", 4.0), ("ill:projector", "b8", "打", 3.0), ("photo:huang", "b8", "卖", 3.6), ("photo:nobel", "b8", "诺贝尔", 6.0), ("ill:boat", "b9", "中国", 3.2), ("ill:gala1", "b9", "除夕", 3.2), ("ill:melt", "b9", "全网", 3.4), ("ill:lobster", "b10", "养", 3.6), ("ill:gala2", "b10", "春晚", 3.6), ("photo:unitree", "b10", "宇树", 3.2), ("ill:animator", "b10", "写代码", 4.4), ]''' + s[old_end:] p.write_text(s); print('ok') PYEOF python3 tools/build_timeline.py 2>&1 | head -1 && python3 -c " import json s = open('~/ai-tree/scene/timeline.js').read(); TL = json.loads(s[len('window.TL = '):].rstrip().rstrip(';')) ps = TL['polaroids'] for a, b in zip(ps, ps[1:]): gap = b['t'] - (a['t'] + a['dur'] + 0.5) print(f\"{a['id']:18s} {a['t']:7.2f} → {a['t']+a['dur']:7.2f} next {b['id']:18s} at {b['t']:7.2f} gap {gap:+.2f}\") "ok total 313.7s | beats 12 | captions 96 | cards 14 | polaroids 22 | notes 40 photo:turing 12.76 → 16.36 next photo:mccarthy at 17.40 gap +0.54 photo:mccarthy 17.40 → 21.00 next photo:perceptron at 25.58 gap +4.09 photo:perceptron 25.58 → 29.98 next photo:minsky at 36.72 gap +6.24 photo:minsky 36.72 → 39.32 next photo:shakey at 40.79 gap +0.97 photo:shakey 40.79 → 44.79 next photo:lisp at 55.50 gap +10.21 photo:lisp 55.50 → 59.10 next photo:deepblue at 84.29 gap +24.69 photo:deepblue 84.29 → 88.49 next photo:stanley at 88.07 gap -0.93 photo:stanley 88.07 → 91.07 next photo:gtx580 at 99.30 gap +7.74 photo:gtx580 99.30 → 102.30 next ill:go at 109.44 gap +6.64 ill:go 109.44 → 111.84 next photo:leesedol at 112.79 gap +0.45 photo:leesedol 112.79 → 115.99 next ill:chat at 143.68 gap +27.19 ill:chat 143.68 → 147.68 next ill:projector at 167.58 gap +19.40 ill:projector 167.58 → 170.58 next photo:huang at 175.91 gap +4.83 photo:huang 175.91 → 179.51 next photo:nobel at 191.43 gap +11.42 photo:nobel 191.43 → 197.43 next ill:boat at 201.56 gap +3.63 ill:boat 201.56 → 204.76 next ill:gala1 at 209.63 gap +4.37 ill:gala1 209.63 → 212.83 next ill:melt at 223.82 gap +10.49 ill:melt 223.82 → 227.22 next ill:lobster at 244.47 gap +16.75 ill:lobster 244.47 → 248.07 next ill:gala2 at 256.68 gap +8.12 ill:gala2 256.68 → 260.28 next photo:unitree at 269.88 gap +9.10 photo:unitree 269.88 → 273.08 next ill:animator at 274.92 gap +1.34
思考：Investigating overlap timing between tracked entries across …
Investigating overlap timing between tracked entries across versions.
Compiling photo frames, captions, and credits for the film.
Weighing a politically sensitive attribution choice for one photo.
Drafting bilingual vocabulary notebook entries and color groupings.
Coding pip animations and layout for the recap panel.
Scheduling panel reveal timing and estimating recap layout dimensions.
Checking label positions against the recap panel in portrait view.
Designing word wrapping and trunk highlight overlays for recap rows.运行 sed -i 's/("photo:deepblue", "b4", "深蓝", 4.2)/("photo:deepblue", "b4", "深蓝", 3.…
sed -i 's/("photo:deepblue", "b4", "深蓝", 4.2)/("photo:deepblue", "b4", "深蓝", 3.3)/' tools/build_timeline.py && grep -n "photo:deepblue" tools/build_timeline.py && grep -n "words learned\|已学会\|const n = learnedCount" scene/film.js && grep -n "^function drawDatePanel" -A 52 scene/film.js | sed -n '40,53p'65: ("photo:deepblue", "b4", "深蓝", 3.3), ("photo:stanley", "b4", "@post", 3.0), 650:// ================================================================ date panel: year, era, ruler, time speed, words learned 705: // words learned: a small stack of cards + count 706: const n = learnedCount(t); 708: const Ch = L.chip, s = Ch.size, msg = `已学会 ${n} 个词`, last = TL.cards[n - 1], bump = backOut((t - (last.t + cardHold(n - 1) + 0.6)) / 0.4); 1225: '写规则找规律神经网络大模型已学会个词冻住了感知机曾经风光后来退场ChatGPT每周用户英伟达市值亿人万亿美元导航垃圾邮件过滤购物推荐规则引擎这支视频的每一帧都是AI写代码画出来的' + 707- if (n > 0) { 708- const Ch = L.chip, s = Ch.size, msg = `已学会 ${n} 个词`, last = TL.cards[n - 1], bump = backOut((t - (last.t + cardHold(n - 1) + 0.6)) / 0.4); 709- const tw = text(msg, 0, 0, { size: s, weight: 700, measureOnly: true, yearBold: false }).w, iconX = Ch.align === 'left' ? Ch.x + 18 : Ch.x - tw - 34, tx = Ch.align === 'left' ? Ch.x + 50 : Ch.x; 710- ctx.save(); ctx.translate(iconX, Ch.y - s * 0.35); ctx.scale(0.9 + 0.1 * bump, 0.9 + 0.1 * bump); 711- [[-6, 4, -0.18], [-2, 1, -0.06], [2, -2, 0.08]].forEach(([dx, dy, r], k) => { ctx.save(); ctx.translate(dx, dy); ctx.rotate(r); ctx.fillStyle = '#fffaf0'; ctx.fillRect(-15, -19, 30, 38); 712- rc.rectangle(-15, -19, 30, 38, { stroke: C.inkSoft, strokeWidth: 1.2, roughness: 0.6, seed: sd(3080 + k) }); ctx.restore(); }); 713- ctx.restore(); 714- text(msg, tx, Ch.y, { size: s, weight: 700, color: C.inkSoft, align: Ch.align, yearBold: false }); 715- } 716- ctx.restore(); 717-} 718- 719-// ================================================================ term cards 720-// line breaking for Chinese: honour '|' hints when the text does not fit; otherwise break only between words运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:90]); s = s.replace(old, new) # ---------- photos: frames, captions, loading rep("const PHOTO_FRAME = { turing: [270, 340, 0.45, 0.3], shakey: [270, 340, 0.45, 0.45], deepblue: [270, 340, 0.5, 0.4], perceptron: [370, 270, 0.5, 0.5], stanley: [370, 260, 0.55, 0.55] };", """const PHOTO_FRAME = { // [width, height, focus x, focus y] of the photo area inside the polaroid turing: [270, 340, 0.45, 0.3], shakey: [270, 340, 0.45, 0.45], deepblue: [270, 340, 0.5, 0.4], perceptron: [370, 270, 0.5, 0.5], stanley: [370, 260, 0.55, 0.55], mccarthy: [270, 340, 0.5, 0.22], minsky: [270, 340, 0.5, 0.18], lisp: [370, 270, 0.5, 0.42], gtx580: [380, 190, 0.5, 0.5], leesedol: [270, 340, 0.5, 0.18], huang: [270, 340, 0.4, 0.12], nobel: [400, 218, 0.5, 0.5], unitree: [270, 340, 0.5, 0.3], };""") rep(" 'photo:stanley': '2005 · 斯坦利无人车', 'ill:go': '2016 · 人机大战',", " 'photo:stanley': '2005 · 斯坦利无人车', 'ill:go': '2016 · 人机大战', 'photo:mccarthy': '1956 · …（中间省略 2125 字）… RA.map(e => e[1]).join('') + CROWN_NODES.map(c => c.tag).join('') + LAND.end.credits.lines.join('') + PORT.end.credits.lines.join('') +", "ERA.map(e => e[1]).join('') + CROWN_NODES.map(c => c.tag).join('') + CREDITS.join('') +") p.write_text(s); print('ok') PYEOF grep -n "credits:" scene/film.jsok 65: credits: { y: 650, size: 22, lh: 30, w: 1220 } }, 92: credits: { y: 990, size: 23, lh: 33, w: 900 } },思考：Laying out a landscape end card with credits lines.
Laying out a landscape end card with credits lines.
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:90]); s = s.replace(old, new) # ---------- layout parameters rep(" rate: { x: 112, y: 410, labelY: 362, align: 'left', size: 38 },\n chip: { x: 70, y: 476, align: 'left', size: 28 },", " rate: { x: 112, y: 392, labelY: 346, align: 'left', size: 38 },\n chip: { x: 70, y: 476, align: 'left', size: 28 },\n" " vocab: { x: 70, y: 444, align: 'left', title: 30, pipY: 460, pw: 17, ph: 17, gap: 5, ggap: 15, flash: 'inline' },\n" " recap: { panel: [36, 150, 476], head: 30, word: 28, lh: 36, inline: false, leader: true },") rep(" rate: { x: 1016, y: 268, labelY: 218, align: 'right', size: 40 },\n chip: { x: 1016, y: 98, align: 'right', size: 30 },", " rate: { x: 1016, y: 268, labelY: 218, align: 'right', size: 40 },\n chip: { x: 1016, y: 98, align: 'right', size: 30 },\n" " vocab: { x: 1016, y: 96, align: 'right', title: 32, pipY: 114, pw: 15, ph: 17, gap: 5, ggap: 13, flash: 'below', flashY: 170 },\n" " recap: { pan …（中间省略 9084 字）… 1.0)) * (1 - dayCover(rt));\n if (a <= 0.01) return;", " const a = clamp((rt - 0.3) / 0.6) * (1 - clamp((t - ENDT - 0.6) / 0.8)) * (1 - dayCover(rt));\n if (a <= 0.01) return;") p.write_text(s); print('ok') PYEOF grep -n "mGlow" scene/film.js | head -3; grep -n "function mGlow" scene/mech.jsok 760: if (t - lt < 0.8) mGlow(cx, cy, 22, 1 - (t - lt) / 0.8, '232,120,60'); } 16:function mGlow(x, y, r, a, color = '255,214,110') { if (a <= 0.01) return; const g = ctx.createRadialGradient(x, y, 0, x, y, r);思考：Building an animated film with layered visual effects.
Building an animated film with layered visual effects.
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:90]); s = s.replace(old, new) rep(" const da = clamp((rt - 0.3) / 0.6) * (1 - clamp((t - ENDT - 1.0) / 1.0)) * (1 - dayCover(rt));", " const da = clamp((rt - 0.3) / 0.6) * (1 - clamp((t - ENDT - 0.6) / 0.8)) * (1 - dayCover(rt));\n const rr = recapRect(t); if (rr) add(rr, rr[4]);") rep("// the ending: the notebook opens, and every term hangs back on the part of the trunk it grew from", """let RECAP_H = null; function recapRect(t) { // panel rectangle (for label layout), once it is on screen const b = beat('end'), t0 = b.ts + 0.25; if (t < t0) return null; const R = L.recap, [px, py, pw] = R.panel; return [px - 10, py - 20, px + pw + 10, py + (RECAP_H || 520) + 10, clamp((t - t0) / 0.5)]; } // the ending: the notebook opens, and every term hangs back on the part of the trunk it grew from""") rep(" const ph = y - py + pad - 14;\n", " const ph = y - py + pad - 14; RECAP_H = ph;\n") p.write_text(s); print('ok') PYEOF python3 t …（中间省略 122 字）… .0 194.5 230.0 271.0 284.0 289.5 292.0 296.0 304.0 311.0"; (timeout 900 python3 tools/frames.py $T > out/frames-land.log 2>&1 &); timeout 900 python3 tools/frames.py --portrait $T > out/frames-port.log 2>&1; sleep 4; tail -n1 out/frames-land.log; tail -n1 out/frames-port.log; python3 tools/sheets.pyok total 313.7s | beats 12 | captions 96 | cards 14 | polaroids 22 | notes 40 errors: none errors: none ['L00.png', 'L01.png', 'L02.png', 'L03.png', 'L04.png', 'P00.png', 'P01.png', 'P02.png', 'P03.png', 'P04.png', 'P05.png', 'P06.png']
读取 L00.png
{ "file_path": "~/ai-tree/out/sheets/L00.png" }[图片]
读取 L01.png
{ "file_path": "~/ai-tree/out/sheets/L01.png" }[图片]
读取 L02.png
{ "file_path": "~/ai-tree/out/sheets/L02.png" }[图片]
读取 L03.png
{ "file_path": "~/ai-tree/out/sheets/L03.png" }[图片]
读取 L04.png
{ "file_path": "~/ai-tree/out/sheets/L04.png" }[图片]
思考：Reviewing the layout for issues in landscape mode. Fixing wo…
Reviewing the layout for issues in landscape mode.
Fixing word-wrap breaks in the vocabulary recap panel.读取 P00.png
{ "file_path": "~/ai-tree/out/sheets/P00.png" }[图片]
读取 P02.png
{ "file_path": "~/ai-tree/out/sheets/P02.png" }[图片]
读取 P03.png
{ "file_path": "~/ai-tree/out/sheets/P03.png" }[图片]
读取 P05.png
{ "file_path": "~/ai-tree/out/sheets/P05.png" }[图片]
读取 P06.png
{ "file_path": "~/ai-tree/out/sheets/P06.png" }[图片]
思考：Reviewing portrait layout issues across the interface. 手写字幕换…
Reviewing portrait layout issues across the interface.
手写字幕换行与片尾字幕排版的调整。
为片尾各项致谢编写独立换行列表。
检查人像照片在右侧边缘的裁切问题。运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() def rep(old, new): global s n = s.count(old); assert n == 1, (n, old[:90]); s = s.replace(old, new) # ---------- greedy packing of unbreakable items (terms, credit entries) rep("const cardGeomCache = {};", r"""// pack unbreakable items into lines (items never split); an item '\n' forces a new line function packItems(items, size, weight, maxW, joiner = ' · ') { const width = (x) => text(x, 0, 0, { size, weight, measureOnly: true, yearBold: false }).w; const lines = []; let cur = ''; for (const it of items) { if (it === '\n') { if (cur) lines.push(cur); cur = ''; continue; } const j = cur && !/[：；]$/.test(cur) ? joiner : (cur ? ' ' : ''); if (cur && width(cur + j + it) > maxW) { lines.push(cur); cur = it; } else cur += j + it; } if (cur) lines.push(cur); return lines; } const cardGeomCache = {};""") # ---------- credits as entries a = s.index("const CREDITS = [") b = s.index("];\n", a) + 3 s = s[:a] + """const CREDITS = ['旁白：微软 Edge 中文语音（云希）', '配乐与音效：代码合成', '事实核对截至 2026.10.14', '\\n', …（中间省略 4281 字）… rm -f out/frames/*.png; T="289.5 292.5 296.0 311.0 194.5"; (timeout 600 python3 tools/frames.py $T > out/frames-land.log 2>&1 &); timeout 600 python3 tools/frames.py --portrait $T > out/frames-port.log 2>&1; sleep 3; tail -n1 out/frames-land.log; tail -n1 out/frames-port.log; python3 tools/sheets.pyok errors: none errors: none ['L00.png', 'L01.png', 'P00.png', 'P01.png']
读取 L00.png
{ "file_path": "~/ai-tree/out/sheets/L00.png" }[图片]
读取 L01.png
{ "file_path": "~/ai-tree/out/sheets/L01.png" }[图片]
读取 P00.png
{ "file_path": "~/ai-tree/out/sheets/P00.png" }[图片]
读取 P01.png
{ "file_path": "~/ai-tree/out/sheets/P01.png" }[图片]
思考：完善多模态与具身智能条目的高亮细节。
完善多模态与具身智能条目的高亮细节。
运行 python3 - <<'PYEOF'
python3 - <<'PYEOF' from pathlib import Path p = Path('scene/film.js'); s = p.read_text() old = " const lines = packItems(words[gi], R.word, 400, avail, ' · ');" assert s.count(old) == 1 s = s.replace(old, " const lines = packItems(words[gi], R.word, 400, avail, '、');") p.write_text(s); print('ok') PYEOF python3 tools/audio.py 2>&1 | tail -1 && (timeout 1000 python3 tools/qa_layout.py > out/qa-land.txt 2>&1 &) ; timeout 1000 python3 tools/qa_layout.py --portrait > out/qa-port.txt 2>&1; sleep 8; grep -E "^overlap|^panels|^offscreen" out/qa-land.txt; echo ===; grep -E "^overlap|^panels|^offscreen" out/qa-port.txt | grep -v "(56, 1346, 1024, 1714)"ok music + sfx + mix written (313.7s); input loudness -18.81 LUFS panels (406, 329, 656, 430) | (30, 345, 470, 490) @ 67.75-67.75 panels (292, 363, 483, 450) | (30, 345, 470, 490) @ 85.75-85.75 panels (39, 636, 483, 962) | (359, 643, 888, 709) @ 99.75-99.75 panels (40, 636, 484, 962) | (359, 643, 888, 709) @ 100.00-100.00 overlap 听懂说话 | 2014 GAN · 一个网络画、一个挑错 @ 105.00-105.00 overlap 2014 GAN · 一个网络画、一个挑错 | 画画 · 创作 @ 105.00-105.00 panels (448, 324, 705, 428) | (30, 345, 470, 490) @ 114.75-114.75 panels (401, 357, 603, 448) | (30, 345, 470, 490) @ 125.25-125.25 panels (357, 385, 511, 455) | (30, 345, 470, 490) @ 190.75-190.75 panels (468, 180, 969, 362) | (30, 40, 530, 262) @ 224.25-224.25 === panels (273, 273, 944, 493) | (20, 40, 1060, 380) @ 25.50-25.50 overlap 1966 ELIZA · 第一个聊天程序 | 翻译 · 聊天 @ 35.00-35.00 panels (493, 203, 914, 349) | (20, 40, 1060, 380) @ 35.75-35.75 overlap 1970s MYCIN · 帮医生选药 | 1980 XCON · 帮公司配电脑 @ 51.25-51.25 panels (152, 325, 1005, 598) | (20, 40, 1060, 380) @ 57.00-57.00 panels (590, 173, 904, 287) | (20, 40, 1060, 380) @ 67.75-67.75 panels (22, 345, 1058, 677) | (20, 40, 1060, 380) @ 80.50-80.50 panels (54, 364, 1047, 683) | (20, 40, 1060, 380) @ 85.50-85.50 panels (609, 152, 849, 245) | (20, 40, 1060, 380) @ 85.75-85.75 overlap 1998 LeNet · 认手写数字 | 看图 @ 87.00-87.00 overlap 听懂说话 | 2014 GAN · 一个网络画、一个挑错 @ 105.00-105.00 panels (245, 298, 1005, 544) | (20, 40, 1060, 380) @ 1 …（中间省略 109 字）… 252) | (20, 40, 1060, 380) @ 125.25-125.25 panels (22, 363, 1058, 691) | (20, 40, 1060, 380) @ 129.75-129.75 panels (155, 333, 1039, 616) | (20, 40, 1060, 380) @ 136.25-136.25 panels (279, 295, 1028, 538) | (20, 40, 1060, 380) @ 143.00-143.00 panels (814, 273, 1005, 351) | (20, 40, 1060, 380) @ 190.75-190.75 panels (407, 840, 1041, 1049) | (821, 592, 1077, 880) @ 224.25-224.25
运行 git add -A scene tools assets/photos script && git -c user.name="Claude" -c use…
git add -A scene tools assets/photos script && git -c user.name="Claude" -c user.email="[邮箱]" commit -q -m "v2.1: nine more licensed photos (McCarthy, Minsky, Lisp machine, GTX 580, Lee Sedol, Jensen Huang, Hinton + Hassabis, Unitree G1), vocabulary notebook with ending recap, credits Co-Authored-By: Claude Opus 5.5 <[邮箱]> Claude-Session: https://claude.ai/code/session_01HZH8EiUQAY3dtZbkE3ysZz" && git tag -f v2.1 && git log --oneline | head -2 && mkdir -p out/v2.0 && mv out/v2/* out/v2.0/ && rm -f out/final.done && (nohup out/final.sh > /dev/null 2>&1 &) ; sleep 2; ps aux | grep -c "[r]ender.py"
ee4d9cc v2.1: nine more licensed photos (McCarthy, Minsky, Lisp machine, GTX 580, Lee Sedol, Jensen Huang, Hinton + Hassabis, Unitree G1), vocabulary notebook with ending recap, credits dee7c7b v2: fact-check fixes (Deep Blue = brute force, backprop popularised in 1986, bud 'waited' not 'frozen', McCarthy named AI for Dartmouth, GAN gloss, AlexNet wording) 3
更新待办
{ "status": "completed", "taskId": "19" }Updated task #19 status
更新待办
{ "status": "completed", "taskId": "20" }Updated task #20 status
更新待办
{ "status": "in_progress", "taskId": "21" }Updated task #21 status
照片和词汇本都改好了，正在渲染定稿，大约 40 分钟。这次的改动：
- 新增 9 张真实照片：麦卡锡（1956 起名）、明斯基（1969 泼冷水）、Lisp 机（80 年代专家系统）、GTX 580（2012 两块显卡）、李世石（2016）、黄仁勋（2024 英伟达）、辛顿 + 哈萨比斯（2024 诺奖，两人拼在一张）、宇树 G1（2026）。全片的真实照片从 5 张增加到 13 张。
- 「已学会 N 个词」改成了「词汇本」：标题旁写着 5/14，下面一排小格子按树干四段分组（写规则 / 找规律 / 神经网络 / 大模型）。每张词卡讲完后会缩小飞进自己的格子，旁边闪一下「+ 深度学习」这样的提示。
- 结尾加了词汇回顾：旁白说「从写规则到会学习」时，词汇本展开，14 个词按树干四段列出来。对应的树干段会从下往上依次亮起，横屏版还有虚线连到树干上。
- 结尾署名：已补上所有新照片的作者和授权。
干了 59 分钟，36 步
读 10 个文件，运行 17 条命令，其他操作 9 次
运行 sleep 560; tail -n 1 ~/ai-tree/out/render-land.log; tail -n 1 ~
sleep 560; tail -n 1 ~/ai-tree/out/render-land.log; tail -n 1 ~/ai-tree/out/render-port.log 2>/dev/null
done: out/master/landscape.mp4 (7529 frames in 569s) worker 1: 0/3765 frames (0s)
运行 rm -f out/sweep/*.png && ffmpeg -loglevel error -i out/master/landscape.mp4 -vf…
rm -f out/sweep/*.png && ffmpeg -loglevel error -i out/master/landscape.mp4 -vf "fps=1/4,scale=640:360" out/sweep/s%03d.png && python3 - <<'EOF' from PIL import Image, ImageDraw from pathlib import Path fr = sorted(Path('~/ai-tree/out/sweep').glob('s*.png')) cols, per = 3, 12 for k in range(0, len(fr), per): grp = fr[k:k+per]; rows = (len(grp) + cols - 1) // cols S = Image.new('RGB', (cols * 640, rows * 380), 'white'); d = ImageDraw.Draw(S) for i, f in enumerate(grp): x, y = (i % cols) * 640, (i // cols) * 380 S.paste(Image.open(f), (x, y + 20)); n = int(f.stem[1:]); d.text((x + 4, y + 4), f"{(n - 1) * 4 + 2:.0f}s", fill='red') S.save(f'~/ai-tree/out/sweep/sheet{k // per:02d}.png') print('ok') EOFok
读取 sheet00.png
{ "file_path": "~/ai-tree/out/sweep/sheet00.png" }[图片]
读取 sheet01.png
{ "file_path": "~/ai-tree/out/sweep/sheet01.png" }[图片]
读取 sheet02.png
{ "file_path": "~/ai-tree/out/sweep/sheet02.png" }[图片]
读取 sheet03.png
{ "file_path": "~/ai-tree/out/sweep/sheet03.png" }[图片]
读取 sheet04.png
{ "file_path": "~/ai-tree/out/sweep/sheet04.png" }[图片]
读取 sheet05.png
{ "file_path": "~/ai-tree/out/sweep/sheet05.png" }[图片]
读取 sheet06.png
{ "file_path": "~/ai-tree/out/sweep/sheet06.png" }[图片]
思考：Reviewing the landscape render for timing and transition iss…
Reviewing the landscape render for timing and transition issues.
Preparing the file-transfer plan for the Mac handoff.
Sorting out clear, consistent file names for the Mac folder.运行 cat > ~/ai-tree/tools/chunk_for_mac.sh <<'EOF'
cat > ~/ai-tree/tools/chunk_for_mac.sh <<'EOF' #!/bin/sh # Split a big file into 9 MiB chunks in the session outputs folder so they can be committed to the Mac. # The first chunk gets a 16-byte marker so it does not start with a video header (that chunk was altered in transit once). # usage: tools/chunk_for_mac.sh FILE PREFIX set -e F="$1"; P="$2"; OUT=/mnt/user-data/outputs/transfer; TMP=~/chunks mkdir -p "$OUT" "$TMP" rm -f "$TMP"/"$P"* "$OUT"/"$P"* split -b 9m -d -a 2 "$F" "$TMP/$P" { printf 'AITREE-CHUNK-000'; cat "$TMP/${P}00"; } > "$TMP/${P}00x" && rm -f "$TMP/${P}00" cp "$TMP/$P"* "$OUT"/ ls "$OUT" | grep "^$P" | tr '\n' ' '; echo sha256sum "$F" | cut -c1-64 EOF chmod +x ~/ai-tree/tools/chunk_for_mac.sh; sleep 420; tail -n 1 ~/ai-tree/out/render-port.logworker 1: 3360/3765 frames (535s)
运行 sleep 120; tail -n 1 ~/ai-tree/out/render-port.log; cd ~/…
sleep 120; tail -n 1 ~/ai-tree/out/render-port.log; cd ~/ai-tree && rm -f out/sweep/p*.png && ffmpeg -loglevel error -i out/master/portrait.mp4 -vf "fps=1/6,scale=360:640" out/sweep/p%03d.png && python3 - <<'EOF' from PIL import Image, ImageDraw from pathlib import Path fr = sorted(Path('~/ai-tree/out/sweep').glob('p*.png')) cols, per = 6, 18 for k in range(0, len(fr), per): grp = fr[k:k+per]; rows = (len(grp) + cols - 1) // cols S = Image.new('RGB', (cols * 360, rows * 660), 'white'); d = ImageDraw.Draw(S) for i, f in enumerate(grp): x, y = (i % cols) * 360, (i // cols) * 660 S.paste(Image.open(f), (x, y + 20)); n = int(f.stem[1:]); d.text((x + 4, y + 4), f"{(n - 1) * 6 + 3:.0f}s", fill='red') S.save(f'~/ai-tree/out/sweep/psheet{k // per:02d}.png') print(len(fr)) EOFdone: out/master/portrait.mp4 (7529 frames in 592s) 52
读取 psheet00.png
{ "file_path": "~/ai-tree/out/sweep/psheet00.png" }[图片]
读取 psheet01.png
{ "file_path": "~/ai-tree/out/sweep/psheet01.png" }[图片]
读取 psheet02.png
{ "file_path": "~/ai-tree/out/sweep/psheet02.png" }[图片]
运行 sleep 300; cat ~/ai-tree/out/deliver-land.log 2>/dev/null; ls -la /h…
sleep 300; cat ~/ai-tree/out/deliver-land.log 2>/dev/null; ls -la ~/ai-tree/out/v2/
total 110348 drwxr-xr-x 2 root root 4096 Oct 15 06:47 . drwxr-xr-x 11 root root 4096 Oct 15 06:27 .. -rw-r--r-- 1 root root 112984112 Oct 15 06:54 ai-tree-v2-landscape-1080p.mp4
运行 sleep 420; cat ~/ai-tree/out/deliver-land.log 2>/dev/null; ls -la /h…
sleep 420; cat ~/ai-tree/out/deliver-land.log 2>/dev/null; ls -la ~/ai-tree/out/v2/
total 171860 drwxr-xr-x 2 root root 4096 Oct 15 06:57 . drwxr-xr-x 11 root root 4096 Oct 15 06:27 .. -rw-r--r-- 1 root root 148446073 Oct 15 06:56 ai-tree-v2-landscape-1080p.mp4 -rw-r--r-- 1 root root 27525168 Oct 15 07:01 ai-tree-v2-landscape-720p.mp4
运行 sleep 60; cat ~/ai-tree/out/deliver-land.log; cd ~/ai-tre…
sleep 60; cat ~/ai-tree/out/deliver-land.log; cd ~/ai-tree/out/v2 && cp ai-tree-v2-landscape-1080p.mp4 ai-tree-v2.1-landscape-1080p.mp4 && cp ai-tree-v2-landscape-720p.mp4 ai-tree-v2.1-landscape-720p.mp4 && ls -la
ai-tree-v2-landscape-1080p.mp4 141.6 MiB 1920 x 1080 frames 7529 dur 313.708333 ai-tree-v2-landscape-720p.mp4 27.4 MiB 1280 x 720 frames 7529 dur 313.708333 total 358260 drwxr-xr-x 2 root root 4096 Oct 15 07:02 . drwxr-xr-x 11 root root 4096 Oct 15 06:27 .. -rw-r--r-- 1 root root 148446073 Oct 15 06:56 ai-tree-v2-landscape-1080p.mp4 -rw-r--r-- 1 root root 28683564 Oct 15 07:01 ai-tree-v2-landscape-720p.mp4 -rw-r--r-- 1 root root 12582960 Oct 15 07:02 ai-tree-v2-portrait-1080p.mp4 -rw-r--r-- 1 root root 148446073 Oct 15 07:02 ai-tree-v2.1-landscape-1080p.mp4 -rw-r--r-- 1 root root 28683564 Oct 15 07:02 ai-tree-v2.1-landscape-720p.mp4
把文件发给你
{ "caption": "新版横屏（720p）：多了 9 张照片，加了词汇本和结尾回顾。竖屏版还在编码。", "display": "render", "files": [ "~/ai-tree/out/v2/ai-tree-v2.1-landscape-720p.mp4" ], "status": "normal" }1 file delivered to user. ~/ai-tree/out/v2/ai-tree-v2.1-landscape-720p.mp4 → file_uuid: faa3f2a9-b65c-4faa-b956-055e0700e0f4 Each file shows as a card whose position relative to your text depends on the app the user is reading in, so refer to a file by its name, never as above or below.
运行 ~/ai-tree/tools/chunk_for_mac.sh ~/ai-tree/out/v2/ai-tree…
~/ai-tree/tools/chunk_for_mac.sh ~/ai-tree/out/v2/ai-tree-v2.1-landscape-1080p.mp4 L
L00x L01 L02 L03 L04 L05 L06 L07 L08 L09 L10 L11 L12 L13 L14 L15 25edba4e1695b094ea33945ac559b217b32e12fd50d447270c50f85322b2645f
从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/ai-tree-v2.1-landscape-720p.mp4", "fileUuid": "faa3f2a9-b65c-4faa-b956-055e0700e0f4" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L00x", "stagedPath": "/mnt/user-data/outputs/transfer/L00x" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L01", "stagedPath": "/mnt/user-data/outputs/transfer/L01" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L02", "stagedPath": "/mnt/user-data/outputs/transfer/L02" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L03", "stagedPath": "/mnt/user-data/outputs/transfer/L03" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L04", "stagedPath": "/mnt/user-data/outputs/transfer/L04" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L05", "stagedPath": "/mnt/user-data/outputs/transfer/L05" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L06", "stagedPath": "/mnt/user-data/outputs/transfer/L06" } ] }{"written":["~/Movies/ai-tree-video/ai-tree-v2.1-landscape-720p.mp4","~/Movies/ai-tree-video/.transfer/L00x","~/Movies/ai-tree-video/.transfer/L01","~/Movies/ai-tree-video/.transfer/L02","~/Movies/ai-tree-video/.transfer/L03","~/Movies/ai-tree-video/.transfer/L04","~/Movies/ai-tree-video/.transfer/L05","~/Movies/ai-tree-video/.transfer/L06"],"rejected":[]}从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/L07", "stagedPath": "/mnt/user-data/outputs/transfer/L07" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L08", "stagedPath": "/mnt/user-data/outputs/transfer/L08" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L09", "stagedPath": "/mnt/user-data/outputs/transfer/L09" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L10", "stagedPath": "/mnt/user-data/outputs/transfer/L10" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L11", "stagedPath": "/mnt/user-data/outputs/transfer/L11" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L12", "stagedPath": "/mnt/user-data/outputs/transfer/L12" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L13", "stagedPath": "/mnt/user-data/outputs/transfer/L13" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L14", "stagedPath": "/mnt/user-data/outputs/transfer/L14" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/L15", "stagedPath": "/mnt/user-data/outputs/transfer/L15" } ] }{"written":["~/Movies/ai-tree-video/.transfer/L07","~/Movies/ai-tree-video/.transfer/L08","~/Movies/ai-tree-video/.transfer/L09","~/Movies/ai-tree-video/.transfer/L10","~/Movies/ai-tree-video/.transfer/L11","~/Movies/ai-tree-video/.transfer/L12","~/Movies/ai-tree-video/.transfer/L13","~/Movies/ai-tree-video/.transfer/L14","~/Movies/ai-tree-video/.transfer/L15"],"rejected":[]}运行 cd ~/Movies/ai-tree-video && { tail -c +17 .transfer/L00x; cat .t…
cd ~/Movies/ai-tree-video && { tail -c +17 .transfer/L00x; cat .transfer/L0[1-9] .transfer/L1[0-5]; } > ai-tree-v2.1-landscape-1080p.mp4 && shasum -a 256 ai-tree-v2.1-landscape-1080p.mp4 | cut -c1-64 && shasum -a 256 ai-tree-v2.1-landscape-720p.mp4 | cut -c1-16 && ls -laProcess started with PID 43683 (shell: /bin/zsh) Initial output: 25edba4e1695b094ea33945ac559b217b32e12fd50d447270c50f85322b2645f 7d3beebc3d6efdce total 741064 drwxr-xr-x@ 11 niracler staff 352 Oct 15 15:03 . drwx------ 6 niracler staff 192 Oct 14 16:00 .. drwx------@ 37 niracler staff 1184 Oct 15 15:03 .transfer drwxr-xr-x@ 7 niracler staff 224 Oct 14 16:00 .venv -rw-------@ 1 niracler staff 28223498 Oct 14 17:46 ai-tree-v1-720p.mp4 -rw-r--r--@ 1 niracler staff 145384700 Oct 15 13:41 ai-tree-v2-landscape-1080p.mp4 -rw-------@ 1 niracler staff 28678075 Oct 15 12:57 ai-tree-v2-landscape-720p.mp4 -rw-r--r--@ 1 niracler staff 148446073 Oct 15 15:03 ai-tree-v2.1-landscape-1080p.mp4 -rw-------@ 1 niracler staff 28683564 Oct 15 15:03 ai-tree-v2.1-landscape-720p.mp4 drwxr-xr-x@ 23 niracler staff 736 Oct 15 14:17 photos drwxr-xr-x@ 20 niracler staff 640 Oct 15 11:56 voice ✅ Process 43683 has finished execution
运行 sha256sum ~/ai-tree/out/v2/ai-tree-v2.1-landscape-720p.mp4 | cut -c1…
sha256sum ~/ai-tree/out/v2/ai-tree-v2.1-landscape-720p.mp4 | cut -c1-16
7d3beebc3d6efdce
思考：Organizing older intermediate files while portrait encodes f…
Organizing older intermediate files while portrait encodes finish.
运行 sleep 540; cat ~/ai-tree/out/deliver-port.log 2>/dev/null; ls -la /h…
sleep 540; cat ~/ai-tree/out/deliver-port.log 2>/dev/null; ls -la ~/ai-tree/out/v2/ | grep portrait; cat ~/ai-tree/out/final.done 2>/dev/null
Exit code 1 -rw-r--r-- 1 root root 130032967 Oct 15 07:10 ai-tree-v2-portrait-1080p.mp4 -rw-r--r-- 1 root root 11010096 Oct 15 07:12 ai-tree-v2-portrait-720p.mp4
运行 sleep 240; cat ~/ai-tree/out/deliver-port.log 2>/dev/null; cat /home…
sleep 240; cat ~/ai-tree/out/deliver-port.log 2>/dev/null; cat ~/ai-tree/out/final.done 2>/dev/null; ls -la ~/ai-tree/out/v2/ | grep portrait
ai-tree-v2-portrait-1080p.mp4 124.0 MiB 1080 x 1920 frames 7529 dur 313.708333 ai-tree-v2-portrait-720p.mp4 27.4 MiB 720 x 1280 frames 7529 dur 313.708333 ALLDONE -rw-r--r-- 1 root root 130032967 Oct 15 07:10 ai-tree-v2-portrait-1080p.mp4 -rw-r--r-- 1 root root 28696200 Oct 15 07:14 ai-tree-v2-portrait-720p.mp4
运行 cp ai-tree-v2-portrait-1080p.mp4 ai-tree-v2.1-portrait-1080p.mp4 && cp ai-tree-…
cp ai-tree-v2-portrait-1080p.mp4 ai-tree-v2.1-portrait-1080p.mp4 && cp ai-tree-v2-portrait-720p.mp4 ai-tree-v2.1-portrait-720p.mp4 && ~/ai-tree/tools/chunk_for_mac.sh ~/ai-tree/out/v2/ai-tree-v2.1-portrait-1080p.mp4 P && sha256sum ai-tree-v2.1-portrait-720p.mp4 | cut -c1-16
P00x P01 P02 P03 P04 P05 P06 P07 P08 P09 P10 P11 P12 P13 5afd9c6e1518c2ae677f9ad3f9cc5d7037dfe816b33f9ef4386976daf91a5a2b e2930e84521482f1
把文件发给你
{ "caption": "新版竖屏（720p）", "display": "render", "files": [ "~/ai-tree/out/v2/ai-tree-v2.1-portrait-720p.mp4" ], "status": "normal" }1 file delivered to user. ~/ai-tree/out/v2/ai-tree-v2.1-portrait-720p.mp4 → file_uuid: ec7293c0-1fa4-426a-9b9a-afc5c285a156 Each file shows as a card whose position relative to your text depends on the app the user is reading in, so refer to a file by its name, never as above or below.
从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/ai-tree-v2.1-portrait-720p.mp4", "fileUuid": "ec7293c0-1fa4-426a-9b9a-afc5c285a156" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P00x", "stagedPath": "/mnt/user-data/outputs/transfer/P00x" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P01", "stagedPath": "/mnt/user-data/outputs/transfer/P01" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P02", "stagedPath": "/mnt/user-data/outputs/transfer/P02" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P03", "stagedPath": "/mnt/user-data/outputs/transfer/P03" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P04", "stagedPath": "/mnt/user-data/outputs/transfer/P04" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P05", "stagedPath": "/mnt/user-data/outputs/transfer/P05" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P06", "stagedPath": "/mnt/user-data/outputs/transfer/P06" } ] }{"written":["~/Movies/ai-tree-video/ai-tree-v2.1-portrait-720p.mp4","~/Movies/ai-tree-video/.transfer/P00x","~/Movies/ai-tree-video/.transfer/P01","~/Movies/ai-tree-video/.transfer/P02","~/Movies/ai-tree-video/.transfer/P03","~/Movies/ai-tree-video/.transfer/P04","~/Movies/ai-tree-video/.transfer/P05","~/Movies/ai-tree-video/.transfer/P06"],"rejected":[]}从你电脑传回文件
{ "files": [ { "devicePath": "~/Movies/ai-tree-video/.transfer/P07", "stagedPath": "/mnt/user-data/outputs/transfer/P07" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P08", "stagedPath": "/mnt/user-data/outputs/transfer/P08" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P09", "stagedPath": "/mnt/user-data/outputs/transfer/P09" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P10", "stagedPath": "/mnt/user-data/outputs/transfer/P10" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P11", "stagedPath": "/mnt/user-data/outputs/transfer/P11" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P12", "stagedPath": "/mnt/user-data/outputs/transfer/P12" }, { "devicePath": "~/Movies/ai-tree-video/.transfer/P13", "stagedPath": "/mnt/user-data/outputs/transfer/P13" } ] }{"written":["~/Movies/ai-tree-video/.transfer/P07","~/Movies/ai-tree-video/.transfer/P08","~/Movies/ai-tree-video/.transfer/P09","~/Movies/ai-tree-video/.transfer/P10","~/Movies/ai-tree-video/.transfer/P11","~/Movies/ai-tree-video/.transfer/P12","~/Movies/ai-tree-video/.transfer/P13"],"rejected":[]}运行 cd ~/Movies/ai-tree-video && { tail -c +17 .transfer/P00x; cat .t…
cd ~/Movies/ai-tree-video && { tail -c +17 .transfer/P00x; cat .transfer/P0[1-9] .transfer/P1[0-3]; } > ai-tree-v2.1-portrait-1080p.mp4 && shasum -a 256 ai-tree-v2.1-portrait-1080p.mp4 | cut -c1-64 && shasum -a 256 ai-tree-v2.1-portrait-720p.mp4 | cut -c1-16 && mkdir -p 旧版本 && mv ai-tree-v1-720p.mp4 ai-tree-v2-landscape-1080p.mp4 ai-tree-v2-landscape-720p.mp4 旧版本/ && ls -la . 旧版本 && du -sh .transferProcess started with PID 44493 (shell: /bin/zsh) Initial output: 5afd9c6e1518c2ae677f9ad3f9cc5d7037dfe816b33f9ef4386976daf91a5a2b e2930e84521482f1 .: total 655984 drwxr-xr-x@ 11 niracler staff 352 Oct 15 15:17 . drwx------ 6 niracler staff 192 Oct 14 16:00 .. drwx------@ 51 niracler staff 1632 Oct 15 15:17 .transfer drwxr-xr-x@ 7 niracler staff 224 Oct 14 16:00 .venv -rw-r--r--@ 1 niracler staff 148446073 Oct 15 15:03 ai-tree-v2.1-landscape-1080p.mp4 -rw-------@ 1 niracler staff 28683564 Oct 15 15:03 ai-tree-v2.1-landscape-720p.mp4 -rw-r--r--@ 1 niracler staff 130032967 Oct 15 15:17 ai-tree-v2.1-portrait-1080p.mp4 -rw-------@ 1 niracler staff 28696200 Oct 15 15:17 ai-tree-v2.1-portrait-720p.mp4 drwxr-xr-x@ 23 niracler staff 736 Oct 15 14:17 photos drwxr-xr-x@ 20 niracler staff 640 Oct 15 11:56 voice drwxr-xr-x@ 5 niracler staff 160 Oct 15 15:17 旧版本 旧版本: total 395104 drwxr-xr-x@ 5 niracler staff 160 Oct 15 15:17 . drwxr-xr-x@ 11 niracler staff 352 Oct 15 15:17 .. -rw-------@ 1 niracler staff 28223498 Oct 14 17:46 ai-tree-v1-720p.mp4 -rw-r--r--@ 1 niracler staff 145384700 Oct 15 13:41 ai-tree-v2-landscape-1080p.mp4 -rw-------@ 1 niracler staff 28678075 Oct 15 12:57 ai-tree-v2-landscape-720p.mp4 426M .transfer ✅ Process 44493 has finished execution
请你允许删除文件
{ "paths": [ "~/Movies/ai-tree-video" ], "reason": "删除 ai-tree-video/.transfer 里传大视频时留下的 426 MB 临时分片（视频已拼好并校验），不动其他文件。" }File deletion is now enabled for the rest of this session in this folder (including subfolders): "~/Movies/ai-tree-video". device_bash picks this up on your next call — rm/rmdir/unlink will work there. Deleted files can't be restored; delete only what the user asked for.
运行 cd ~/Movies/ai-tree-video && rm -rf .transfer && ls -la && du -sh…
cd ~/Movies/ai-tree-video && rm -rf .transfer && ls -la && du -sh .
Process started with PID 45611 (shell: /bin/zsh) Initial output: total 655984 drwxr-xr-x@ 10 niracler staff 320 Oct 15 15:25 . drwx------ 6 niracler staff 192 Oct 14 16:00 .. drwxr-xr-x@ 7 niracler staff 224 Oct 14 16:00 .venv -rw-r--r--@ 1 niracler staff 148446073 Oct 15 15:03 ai-tree-v2.1-landscape-1080p.mp4 -rw-------@ 1 niracler staff 28683564 Oct 15 15:03 ai-tree-v2.1-landscape-720p.mp4 -rw-r--r--@ 1 niracler staff 130032967 Oct 15 15:17 ai-tree-v2.1-portrait-1080p.mp4 -rw-------@ 1 niracler staff 28696200 Oct 15 15:17 ai-tree-v2.1-portrait-720p.mp4 drwxr-xr-x@ 23 niracler staff 736 Oct 15 14:17 photos drwxr-xr-x@ 20 niracler staff 640 Oct 15 11:56 voice drwxr-xr-x@ 5 niracler staff 160 Oct 15 15:17 旧版本 539M . ✅ Process 45611 has finished execution
运行 rm -f /mnt/user-data/outputs/transfer/L* /mnt/user-data/outputs/transfer/P* /mn…
rm -f /mnt/user-data/outputs/transfer/L* /mnt/user-data/outputs/transfer/P* /mnt/user-data/outputs/transfer/land-1080p.* ; rm -f ~/chunks/L* ~/chunks/P* ~/q00.bin ~/r00.bin; ls /mnt/user-data/outputs/transfer | wc -l; cd ~/ai-tree && git status --short | head; git log --oneline | head -3
0 M audio/mix.wav M audio/music.wav M audio/narration.wav ?? audio/sfx.wav ?? tools/chunk_for_mac.sh ee4d9cc v2.1: nine more licensed photos (McCarthy, Minsky, Lisp machine, GTX 580, Lee Sedol, Jensen Huang, Hinton + Hassabis, Unitree G1), vocabulary notebook with ending recap, credits dee7c7b v2: fact-check fixes (Deep Blue = brute force, backprop popularised in 1986, bud 'waited' not 'frozen', McCarthy named AI for Dartmouth, GAN gloss, AlexNet wording) 9948a17 v2: attention weights sum to 100%, serve scene over http for rendering, delivery encodes, script v2
运行 git add tools/chunk_for_mac.sh && git -c user.name="Claude" -c user.email="nore…
git add tools/chunk_for_mac.sh && git -c user.name="Claude" -c user.email="[邮箱]" commit -q -m "tools: chunked transfer helper for large files Co-Authored-By: Claude Opus 5.5 <[邮箱]> Claude-Session: https://claude.ai/code/session_01HZH8EiUQAY3dtZbkE3ysZz" && git tag -f v2.1 && git log --oneline | head -1
Updated tag 'v2.1' (was ee4d9cc) 3f9958e tools: chunked transfer helper for large files
更新待办
{ "status": "completed", "taskId": "21" }Updated task #21 status
更新待办
{ "status": "completed", "taskId": "13" }Updated task #13 status
思考：Finished preparing the updated version and summary for deliv…
Finished preparing the updated version and summary for delivery.
新版 v2.1 已经做好了，横屏、竖屏两个版本，片长都是 5 分 14 秒。两个 720p 的文件已经发在对话里，四个文件都放进了你电脑的
ai-tree-video文件夹，每个都校验过和我这边的完全一致。文件 用途 ai-tree-v2.1-landscape-1080p.mp4横屏，投屏 / 会议室放 ai-tree-v2.1-portrait-1080p.mp4竖屏，手机上看 ai-tree-v2.1-landscape-720p.mp4、ai-tree-v2.1-portrait-720p.mp4约 27 MB，方便直接发群 你这两条意见，这一版是这样处理的：
- 照片：真实照片从 5 张增加到 13 张。新加了麦卡锡、明斯基、Lisp 机、GTX 580 显卡、李世石、黄仁勋、辛顿和哈萨比斯的诺奖合照、宇树机器人。所有照片的作者和授权都写在片尾了。
- 「已学会 N 个词」改成了「词汇本」：
- 平时显示一排小格子，按树干四段分组（写规则 / 找规律 / 神经网络 / 大模型）。每张词卡讲完会飞进自己的格子，旁边闪一下「+ 深度学习」这样的提示。
- 结尾旁白说「从写规则到会学习」时，14 个词按树干四段列出来，对应的那段树干依次亮起。
之前核查改掉的几处说法，这一版也都包含在内。
文件夹里，旧版本（v1 和加照片前的 v2 草稿）挪到了
旧版本/子文件夹，一个没删。传输时留下的 426 MB 临时分片已经清理掉了。哪一段节奏、用词或画面还想调，告诉我大概的时间点就行。
Claude，10 月 15 日 10:01–15:26。已去掉系统提示词；路径、账号等信息已脱敏；工具的长输出已截断。