做夢最棒了，最近 AI 帶來的 fantasy 實在太多ww note.sylee.dev/3m3yyjr3g4k2p

Evan Spiegel lists himself as “VP of Product at Meta” because of how many Snapchat ideas ended up shaping Meta’s products 😂

Leaving Meta and PyTorch I'm stepping down from PyTorch and leaving Meta on November 17th. tl;dr: Didn't want to be doing PyTorch forever, seemed like the perfect time to transition right after I got back from a long leave and the project built itself around me. Eleven years at Meta. Nearly all my professional life. Making many friends for life. Almost eight years leading PyTorch, taking it from nothing to 90%+ adoption in AI. Walking away from this was one of the hardest things I've ever done. But I'm leaving with a full heart. PyTorch handles exascale training now. It powers foundation models that are redefining intelligence. It's in production at virtually every major AI company. It's taught in classrooms from MIT to rural India. The tools I dreamed about making accessible? They are. The barrier to entry I wanted to lower? It's almost gone. To be clear, there’s so much more to do. As long as AI evolves at a breakneck pace, PyTorch will continue to play catch up. Obsessing over the yet-to-come sometimes makes us forget how much we’ve already done. To everyone who built this with me—who believed research should be joyful, that tools should be elegant, that open source changes everything—thank you. This wasn't my journey. It was ours. What's next for me? Something small. Something new. Something I don't fully understand yet. Something uncomfortable. I could have moved to something else inside Meta. But I needed to know what's out there. I needed to do something small again. I couldn't live with the counterfactual regret of never trying something outside Meta. It's very hard to leave. I probably have one of the AI industry’s most leveraged seats, I lead the software layer that powers the entire AI industry. Every major AI company and hardware vendor are on a speed dial. This kind of power is really hard to give up. But curiosity ultimately won out in my head. Keep making AI delicious and accessible. I'll be watching. Probably filing issues. Definitely staying involved. Is PyTorch going to be okay? I don't want to be doing PyTorch forever. I don't want to be like Guido or Linus— bound to a single thing for decades. Last November, coinciding with the birth of my daughter, I started planning my exit with Aparna. My goal was to leave PyTorch in a good and stable place. By this August, during the second half of my parental leave, I knew: Edward, Suo, Alban, Greg, John, Joe and Jana were ready. The team faced hard people, product, technical and organizational problems and didn’t feel the need to lean back on me to solve these for them (unlike in the past). The product story they crafted for the PyTorch Conference was coherent—really coherent. The things I'd flagged red were turning healthy. The project didn't need me anymore. Unlike 2020-2022 (when I stepped down to go do robotics and came back when Lin, Dima and Dwarak left), I have strong confidence that this time PyTorch is truly resilient. The most aligned culture carriers of PyTorch – Greg, Alban, Ed, Jason and Joe are at the decision table now, and people with strong value alignment – Suo, John and Jana have joined them at the table. And there’s a long list of equally value-aligned people willing to sit at the table should any of these people leave. There are many little things that make up my confidence on the people – John worked on Julia and open-source for a very long time (in fact we hacked a Torch.jl in 2015), Suo has been the strongest systems builder and strategic partner I’ve had for the past two years, and Jana worked on resilient core systems for a very long time, I’ve had long technical and organizational discussions with her over the past few months that give me confidence. And the product lineup and execution in 2025 should be sufficient evidence for any remaining doubt. I’m confident that this band of PyTorchers are going to do exceptionally well. PyTorch might change in flavor because I no longer impose my own taste from the top, but I’m confident that the values are going to stay intact and the product is going to be awesome. My time at Meta The early years of FAIR were absolutely magical. I was part of a small family of absolutely brilliant people building state-of-the-art AI out in the open. From working on GANs with Emily Denton, Rob Fergus, Leon Bottou, Martin Arjovsky and the (now legendary) Alec Radford to building Starcraft bots with Gabriel Synnaeve, to building the first FAIR Cluster with Howard Mansell, to working on object detection with Adam Lerer and Piotr Dollar, to building PyTorch. It was more fun than I can describe in words. 2015 and 2016 were probably the most productive and professionally enjoyable years of my life. I’ll probably romanticize this period of my life forever. When I joined FAIR, I had massive impostor syndrome, and the first 3 months were very very difficult. I can’t credit Andrew Tulloch enough for being the most thoughtful, kind and welcoming mentor, without whom I wouldn’t have made it. I’m so damn bullish for Meta just from the fact that he’s back. --- My time on PyTorch was special. I loved every part of building it—designing it, managing it, being the PM, TL, comms lead, doc engineer, release engineer, squashing bugs, growth hacking, turning it into a coherent product with hundreds of people, transitioning it to industry stakeholdership – the whole nine yards. To the core PyTorch team at Meta: the engineers, researchers, open-source maintainers, docs writers, CI infrastructure folks, hardware partners, the community builders. To the hundreds more inside and outside Meta—thank you. You turned a library into a movement. There are too many people to credit and thank, but I can't not mention Adam Paszke, Sam Gross, Greg Chanan, Joe Spisak, Alban Desmaison, Edward Yang, Richard Zou, Tongzhou Wang, Francisco Massa, Luca Antiga, Andreas Köpf, Zach DeVito, Zeming Lin, Adam Lerer, Howard Mansell and Natalia Gimelshein. And Schrep. They made the launch happen. And so many more people became centrally important later: Lu Fang, Xiaodong Wang, Junjie Bai, Nikita Shulga, Horace He, Mark Saroufim, Jason Ansel, Dmytro Dzhulgakov, Yangqing Jia, Geeta Chauhan, Will Constable, Briah Hirsh, Jane Xu, Mario Lezcano, Piotr Balecki, Yinghai Lu, Less Wright, Andrew Tulloch, Bruce Lin, Woo Kim, Helen Suk, Chris Gottbrath, Peng Wu, Joe Isaacson, Eli Uriegas, Tristan Rice, Yanan Cao, Elias Ellison, Animesh Jain, Peter Noordhuis, Tianyu Liu, Yifu Wang, Lin Qiao and hundreds more. It’s criminal of me to not take the space to list out everyone else I should be mentioning here. PyTorch is nothing without its people ❤️. The most joyful moments of building PyTorch was meeting users eager to share their happiness, love and feedback. I remember a grad student coming to me at Neurips 2017, in a slurring emotional voice he said he’d been trying to make progress on his research for 3 years but within 3 months of using PyTorch he made so much progress that he was ready to graduate. That moment made it tangible that what we do matters, a lot, to a lot of people, even if you don't constantly hear from them. I do miss the intimacy of the PyTorch community, with a 300 person conference that felt like an extended family gathering, but I feel that’s a small price to pay considering the scale of impact PyTorch is truly having today – yes the Conference is now 3,000 people where market-moving deals get brokered, but it’s helping orders of magnitude more people to do their best AI work. I miss the intimacy, but I'm proud of that growth. --- To Mark Zuckerberg and Mike Schroepfer, who believed that open-sourcing is fundamentally important and is a sound business strategy. This is so hard to understand for most people within the course of business, but we’ve run lock-step on this strategy without ever having to discuss it. Without you two, neither FAIR nor PyTorch would’ve happened. And those mean so much to me. To Yann LeCun and Rob Fergus, for building the magical early FAIR that I so revere. To Aparna Ramani, a leader that I find so rare at Meta in her ability to hold a really high bar for the org, technically brilliant with the span to discuss deep infra systems and industry-strategy within the same conversation and for being an absolute execution-machine! I’ve learned so much from you. To Santosh, Kaushik, Delia, Oldham and Ben for being so welcoming to Infra. For someone coming over from FAIR with a wildly different culture, you all made me feel at home and made me part of the family, and thank you for that. To all my managers who've championed me through the PSC video game – Serkan, Howard, Jerome, Abhijit, Yoram, Joelle, Aparna and Damien – I owe you a lifetime of drinks. --- Signing off for now. —Soumith

感受到 AI 協助下 barefoot engineer 的威力，真的很多之前不敢想的事都可以作到了

毫不意外的延期，原本調檔期的遊戲可以改回來了 😅

HackerNews 上今天很火的一个产品： book.sv/ 是一个基于3亿 Goodreads 评论训练的个性化图书推荐系统。 通过输入你已读的书籍，你将获得更加精准的推荐。只需提供3本以上书籍，就能获得最佳推荐体验。 特别原始的界面（Bootstrap框架！好熟悉的黑色 Header），现在书名也是只有英文的，但是我很喜欢，输入了几本我喜欢的书，推荐的都很精准。推荐给爱读书的朋友。 作者还简介了网站的技术细节： book.sv/how-it-works 作者不满亚马逊收购后 Goodreads 的推荐质量低下及难以找到同好用户，于是自建网站 book.sv 数据： * 仅抓取公开用户的「已读」书架，且用户至少有 5 本书。 * 抓取用户 ID 1～1.93 亿（8 月初至 10 月初），最终获得 4300 万合格用户。 * 每用户随机取最多 64 本书子序列，总计约 30 亿交互，训练/测试拆分后模型实际使用 13 亿交互。 技术栈： * 预处理：NVTabular（分组、归一化、ID 重映射）。 * 搜索：Meilisearch。 * 推理：Triton 服务器。

Our 7th gen TPU Ironwood is coming to GA!  It’s our most powerful TPU yet: 10X peak performance improvement vs. TPU v5p, and more than 4X better performance per chip for both training + inference workloads vs. TPU v6e (Trillium). We use TPUs to train + serve our own frontier models, including Gemini, and we’re excited to make the latest generation available to @googlecloud customers.

特斯拉股東會重點總結 Musk薪酬計劃獲批准：股東以超過75%支持率通過Elon Musk的薪酬方案，包含12階段股票選擇權，若達成8.5兆美元市值與營運里程碑，可讓Musk持有公司超過20-25%投票權。 Optimus人形機器人：會議核心話題，Musk稱其可消除貧窮、提供優質醫療（甚至超越人類外科醫生），預測未來將有「數十億台」Optimus。Tesla將加速生產，視為史上最快擴產複雜產品。 FSD與新車款：Musk稱FSD即將完全解決，下版可靠性高到可「睡覺醒來已抵達目的地」。正式公布Cyber Cab無方向盤/踏板機器人計程車；新Roadster即將揭曉，稱為「史上最令人興奮產品」。 技術與製造創新：介紹AI5自製晶片，效率高、成本僅競爭對手10%以下，用於FSD與Optimus。Cyber Cab生產時間僅5秒，接近消費電子水準。閒置Tesla車輛可組成「分散式AI推論車隊」，車主每月賺取100-200美元。 其他業務更新：Tesla Semi：2026年起量產，目標年產5萬輛。 能源儲存：Megapack電池可「翻倍美國能源產出」，無需新建電廠；目前80GWh產能，2026年再增50GWh。 中國FSD：目前僅部分批准，全批准預計2026年2-3月。 AI哲學警示：Musk以嚴肅語調結束，警告若AI超越人類總智能，「人類難以掌控」，強調需確保AI「友善」。

根據過往經驗看來，Meta 應該很大程度是睜一隻眼閉一隻眼，有錢賺就好了

Elon Musk 都說有趣？ $AMZN Andy Jassy 每一家雲端服務供應商都面臨相同的 AI 基礎設施挑戰：晶片必須緊密排列以快速交換資料，但這會產生極高熱量，帶來前所未有的散熱需求。 我們需要一個策略性解決方案，能讓我們在不等待新建資料中心的前提下，利用現有的氣冷式資料中心轉向液冷技術。同時，方案必須能快速部署，讓客戶在我們逐步轉向設施級液冷時，就能立即獲得強大的 AI 能力。 想像一棟房子只有一間向陽的房間需要冷氣，其他空間自然保持涼爽——這就是我們想達到的目標：在同一設施內靈活部署液冷與氣冷機櫃，實現最高效率。 市面上的選項並不理想。要嘛得等待專用液冷設施完工，要嘛採用現成的解決方案，卻無法擴展或滿足我們的獨特需求。兩者都無法服務客戶，因此我們做了 $AMZN 常做的事……我們自己發明了一套解決方案。 我們的團隊設計並推出 In-Row Heat Exchanger（IRHX，機櫃內熱交換器），採用「直接對晶片」冷卻方式，在晶片上安裝「冷板」。冷卻液在密封的閉迴路中流動，持續帶走熱量，完全不增加用水量。這讓我們能在同一設施內同時支援傳統工作負載與高需求的 AI 應用。到 2026 年，我們的液冷容量將成長至機器學習總容量的 20%以上，而目前已達 multi-gigawatt 規模。 雖然液冷技術本身並非獨創，但我們的作法卻是。我們打造了一套高效能、可在 120 個可用區域、38 個地區快速部署的系統，這一點極具意義。因為市場上不存在這樣的解決方案，我們開發出一個能在 更小空間內提供更大液冷容量 的系統，同時保持靈活度與效率。 我們的 IRHX 可支援多種需要液冷的機櫃，比全氣冷設施少用 9% 的水，並比市售方案提升 20% 的電力效率。正因是內部自行研發，我們能在 數月內** 於任何資料中心部署，為客戶打造一個可服務數十年的靈活基礎。 大規模重新想像與創新，是 $AMZN 長期以來的傳統，也是我們能在技術基礎設施、資料中心創新、永續發展與韌性上領先全球的原因。我們還沒完成……為了客戶，還有更多值得發明的東西。

超過 75%的 $TSLA 股東 同意Elon Musk 的8780億美元的薪酬方案 🚀 Elon Musk 我試著找出某種方式， $TSLA 股東能參與 SpaceX。 我一直很認真思考如何讓人們獲得SpaceX股票的管道； 也許在某個時間點，SpaceX應該成為一家上市公司，儘管公開上市有許多缺點。 訂閱免費電子報： 準備好迎接 SpaceX 的 IPO ? basm.substack.com/p/spacex?r…

BREAKING 🚨: Comet browser got a major upgrade for its agentic capabilities. - It can work longer and on more complex jobs across larger periods of time. - It performs 23% better than its predecessor. Longer context was a top request from all the users! Now it can write a weekly recap newsletter for me in one shot as well as seamlessly work with spreadsheets. Testing time 👀

BREAKING: Tesla has officially announced that Cybercab production will start on April 2026.

Musk在股東會上喊說可能會不得不將自建大型晶圓廠，可能會和Intel合作 INTC盤後聞訊大漲 果然，御三家全部都喊上了，你們還不給我更便宜的deal。

Tesla shareholders just gave Elon Musk a $1 trillion incentive 75%+ of shareholders voted YES. What he must deliver over 10 years: • Grow $TSLA to $8.5T market cap (466% increase from $1.4T today) • 20M vehicles delivered (cumulative) • 1M Optimus humanoid robots sold • 1M robotaxis in operation • 10M Full Self-Driving subscriptions If successful, he becomes the world’s first trillionaire with 25% ownership 👀

Livestream starts at 3pm CT x.com/i/broadcasts/1LyGBXwkN…

没缘由，跌得急，无关企业基本面，钱也没地方去，苹果 $AAPL 瞬间成避险工具了，哈哈哈。

This is not deboosting links. It’s so deeplinks fallback to the old webview and open externally. Otherwise, they would break in the new inline webview on iOS.

Here is the latest list of domains that X has excluded from using the new in-app link viewer on iOS

認知戰專區 (教學範例) 一個來自俄羅斯抖音帳號，上傳多個烏克蘭士兵在前線哭哭的影片，實際上都是AI人工智慧生成的假影音 俄羅斯巨魔工廠利用深偽技術(Deepfake)和AI技術偽造內容，發佈在網路上流傳和誤導輿論，明年台灣地方大選這些都是可能的中國介選手段 #認知戰 #深偽技術 #人工智慧 #社群平台

人間は「食料」、だから貯蔵する――狂犬病×ヒグマ“最凶暴化”の恐怖　サハリンの惨劇が日本に警鐘（集英社オンライン） topics.smt.docomo.ne.jp/amp/… 日本最近野生熊的問題真的越來越嚴重，台灣的猴子跟野貓野狗的問題也是，覺得這世界最不需要的就是只長在嘴巴上的愛心😐