Gavin @goodloop

甩开膀子蹬

Tibo @thsottiaux

a day ago

Ladies and gentlemen... start... your... ENGINES. We are almost Tuesday and I promised a reset for Tuesday. Among some other things. See you soon.

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@jayleaton @thdxr same here

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I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.

Re-shoot an existing video from a new angle 🤯 Truly insane work from @theworldlabs, this new model is a game-changer. The initial clip was filmed with three cell phones on tripods - the model reconstructed a full view of the scene to reframe the video.

Así se ve un sueldo de 750.000 dólares al año: un tipo en camiseta blanca, un pizarrón y 2 horas y media. Stanford, CS336. Percy Liang construye un LLM desde cero. Lo que hay debajo de Claude y ChatGPT, y arranca por la parte que todos saltan: el modelo no lee tu texto, lee números. Anthropic paga ese sueldo a los ingenieros que entienden esa capa. Lo único que cobra Stanford son 2 horas y media de tu atención.

The most important skills in Building and Deploying AI Applications. x.com/i/article/2090…

DFlash 2 is here! Qwen3.8-27B at 70 tok/s on an M5 Max MacBook Pro. ⚡ Up to 4.6× the speed of autoregressive decoding, with the same output. This is the next generation of DFlash, seeded at Z Lab and upgraded at Inco AI. Get one more accepted token on every pass, for free! inco.ai/blog/dflash2/

说个暴论， 正在找工作的，学llm/ML都不如把下面这本书啃透彻。面试帮助绝对比你聊transformer, MoE大得多！ 不同意的，评论区battle.

太震撼了！一小时顶两年MBA！ 黄仁勋2011年斯坦福这场演讲，把创业、创新、公司重构的底层逻辑讲得清清楚楚，太值了 强烈推荐反复看

今天才知道，Google AI Pro 以上送 YouTube 会员了 这个活动力度不可谓不大

强烈推荐大家都去看这个跟workbudy产品负责人Jason的深度对谈播客， 另外关于腾讯workbudy，我说个暴论，你看100篇那些AI写的教程文章，都不如抽1个小时好好看看腾讯官方出品的这期播客，太他么深度了，干货拉满！ Jason分享了一个关键判断让我印象特别深：2021年他曾计划培养孩子学习编程，到2026年发现这个方向已经失效了，在AI的加持下编程本身不再是稀缺技能，洞察力、审美品味、逻辑清晰的语言表达能力，才是AI时代真正的核心竞争力。 还有这个文科生的故事，英语文学专业的前媒体人，一行代码都不会写，就因为自己攒了7000多首杜比全景声的歌，Excel卡到打不开，找外包又说不清楚需求，被逼得没办法开始摸AI编程工具。 你以为是AI两下给他把App生成出来了？根本不是， 他说前前后后开了40多个对话窗口，文件拆得乱七八糟，环境部署从头啃，踩了无数坑，磨了好几个月，才真的把那个杜比音乐App做出来，现在都更新到3.2版本，正经上架App Store了。 那他为什么没放弃？ 他说AI会在他做对一点点的时候，蹦出来一句“你这个思路很清晰，像个成熟的产品经理”。 卧槽，就这一下兄弟们应该懂吧， 我们这种在否定式教育里长大的东亚小孩，太吃这一套了。 很多时候你不敢开始，其实不是懒也不是笨，是脑子里那个“你不行”的声音太大了。 咱们从小到大做什么都先被挑错，刚迈出一步就有人说你这不对那不好，久了你自己都信了，你就是做不成事。 但是AI不judge你，不骂你，不会翻个白眼说这都不会，你做对一点它就真心实意夸你，直接把那个压了你几十年的声音盖过去了。 播客里WorkBuddy的产品负责人Jason说，2021年他还在规划以后要送孩子学编程，2026年回头看，这个想法已经不成立了。 我以前以为，只是因为写代码的门槛被AI踏平了，现在才懂，被踏平的根本不止是代码门槛。 以前我们想做个东西，不仅要会写代码，还要有颗大心脏——被bug卡一周，被前辈骂写的什么垃圾，被环境配置搞到崩溃，90%的人在第一步就被劝退了。 现在AI不仅帮你写代码、查bug，还给你当啦啦队，把你那点被打击了几十年、碎得渣都不剩的信心，一点点给你拼回来。 你看现在满屏都是MCP、Agent、Vibe Coding这些新名词，好像你三天不背就要被时代抛弃，大家课买了一堆，一个工具都不敢打开，但实际上哪有那么玄乎啊🤣 当然了，如果你从小在夸奖里长大，内心强大到根本不需要别人给你托底，可能get不到这个点。 但如果你和我一样，总怕自己做不好，怕出错被笑话，迟迟不敢迈出第一步，真的不用上来就想做个上架的App， 不如今天就花半小时，让AI帮你写个批量改文件名的小脚本，或者整理下你乱了半年的文件夹。 你会发现，你根本不是不行，只是之前从来没人，在你做对一点点的时候，认认真真跟你说一句“你做得真棒”🥹🥹 焦虑这东西，站在岸边看永远觉得水很深，脚伸下去才知道，原来不仅水不深，还有人在旁边扶着你，跟大家共勉！ 评论区是我对51分钟的文字版总结👇

@fiapp_pro 影响代码和分析能力么

@johnsome44 @m0d8ye 小米跟deepseek那个体感更好呢？

Anthropic 自己的团队，刚刚演示了如何搭建 production AI agents 37 分钟 免费 而且是由真正构建这套系统的 engineers 亲自讲 这不是又一个“AI 很强”的泛泛分享 而是直接展示： 怎么把 AI agents 从 demo，推进到 production 很多人过去 6 个月，还在手动管理每一个 workflow 自己拆任务 自己复制上下文 自己盯执行 自己检查结果 但 Anthropic 这次讲的是另一件事： 如何把这些 workflow 放到 autopilot 上 让 agent 不只是回答问题，而是参与执行、协作和持续推进任务 真正有意思的是： AI agent 的关键，不是单次生成得多漂亮 而是能不能稳定接住真实工作流 能不能连接 tools 能不能处理上下文 能不能持续执行 能不能进入 production 先看这个 37 分钟 workshop 未来真正拉开差距的，是谁会用 AI 提问，还是谁能把 AI agents 接进自己的生产系统？

0x_Miko @Mikocrypto11

4 months ago

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@aillusoryx 纯ai生成的，还是4dgs这样渲染的？

GTA 5 but it's in CHINA (Reimagined by AI)

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You can now use your ChatGPT subscription in the Zed agent, with the same usage and rate limits you benefit from in Codex directly. We're grateful that @openaidevs continues to support subscription-based access for third-party tools, even as others move toward usage-based billing.

Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights: The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons: 1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing. 2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc. 3. LLM knowledge bases as an example of something that was *impossible* with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc. I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3). The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base *and* 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to... Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors.

Stephanie Zhan @stephzhan

5 months ago

@karpathy and I are back! At @sequoia AI Ascent 2026. And a lot has changed. Last year, he coined “vibe coding”. This year, he’s never felt more behind as a programmer. The big shift: vibe coding raised the floor. Agentic engineering raises the ceiling. We talk about what it

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