X

Io starts now!

Io starts now!

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

BREAKING: @karpathy has joined Anthropic

It’s #GoogleIO Day One. Who’s ready to see what’s coming to Gemini? Livestream starts here at 10am PT: nitter.net/i/events/2053241348807…

Anthropic will not be destroyed. Their AI+harness goes far beyond coding and Opus 4.7 is still better than Composer 2.5, albeit a lot more expensive. Cursor is however an important piece of the puzzle to make Grok much better.

还有一百多就五千订阅了，不知道一觉醒来会不会有惊喜。我经常不按常理出牌，就提前写好庆祝5k订阅达成吧，哈哈🎆 我主业是一个AI架构师，也是一支烟花AI社区的联创，从23年至今大概积累了40个垂直的AI社群，大家都很纯粹 全都是免费的社群，基本上都是研发，产品和创业者和行业大佬，也欢迎大家一起进群交流，可以在这里登记信息，我会邀请大家进群 （长期有效） hqexj12b0g.feishu.cn/share/b… 虽然X算法偶尔抽风 还会误杀，不可否认X上的算法还算公平的，之前我分别在公众号，小红书和抖音尝试了蛮久自媒体，也是差不多的输出，最终还是X上正反馈更多一些，其他的平台都一言难尽。 争取今年做到一万粉。谢谢订阅我的朋友们，以后继续输出更多干货！ 不过相当于X的收获，最大的惊喜是之前开源的fireworks-tech-graph 快7k star 了，靠神佬等众多大佬的喜爱转发，基本全靠X平台的传播，也合并了不少PR，我基本上没有在国内自媒体宣传过这个项目，完全靠自来水推荐，非常幸运可以感受到了流量加持后项目开源。 不过流量来得快去的也快，我内心也算比较平静，大大小小写了快20个开源项目，由于我懒得宣传，基本上是我自己在用，有几个harness 相关的skill 真的不错，大家可以去看下，总有一款你喜欢。

谷歌 Gemini Omni Flash 视频编辑测试。 你们应该能猜到我原始视频是在哪儿录的，反正效果远不如 SeeDance 2.0 Video

🚀 Excited to release LongLive 2.0! 🎬 An end-to-end infrastructure for long video generation, with FP4 and parallelism at the core of both training and inference. ⚡45.7 FPS generation speed on 5B model⚡ ✨ LongLive 2.0 supports real-video training, few-step distillation, multi-shot training/inference, sequence-parallel acceleration, NVFP4 KV cache, and async VAE decoding deployment. 🧩 To our knowledge, this is the first open-source 4-bit long video generation infra that covers both training and inference. 🙌 Welcome to check it out, try it, and share feedback! 🔗 Code: github.com/NVlabs/LongLive 📰 Paper: huggingface.co/papers/2605.1… 🎥 Demo: nvlabs.github.io/LongLive/Lo… #LongVideoGeneration #VideoGeneration #Realtime #AIInfra #EfficientAI #FP4 #Parallel #NVIDIA Video

x.com/i/article/205536179511…

哇！ 谷歌新视频模型 Gemini Omni Flash 已经上线 FLow

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

A Stanford mathematician spent forty years watching one brilliant student after another crash into hard problems. Not because they weren’t smart. But because no one had ever taught them what to actually do “before” they started solving. His name was George Pólya. In 1945, he published “How to Solve It”. The book sold over a million copies and has never gone out of print. Even Marvin Minsky, who built the first neural network, said that everyone should read it. Yet most people still haven’t heard of it. What Pólya kept seeing was the same failure pattern, again and again: The moment a difficult problem appears, students get anxious and immediately start calculating. Not because calculating is the right first step, but because doing somethingfeels much more comfortable than sitting with “I don’t know.” They end up working hard in completely the wrong direction. The step he found most neglected was this: Truly understand the problem first. Not just skim it. Not just think “this looks familiar.” His test was simple but ruthless: Can you restate the problem in your own words without looking at the original? If you can’t, you don’t actually understand it yet. Most people skip this step entirely. They jump straight into execution and then get stuck on a problem they never truly grasped. Pólya outlined four steps for solving problems. But in real life, the two that matter most are usually the first and the last: 1. Understand the problem deeply 2. Devise a plan (if you’re stuck, try solving a simpler version first and bring the insight back) 3. Carry out the plan 4. Look back — verify, generalize, and reflect The people who get truly good at this aren’t the ones who practice more. They’re the ones who’ve learned to slow down when every instinct is screaming at them to just start calculating — especially at the beginning, and again at the end. What struck me most after reading this: We assume hard problems are difficult because they are. Most of the time, it’s simply because we never took the time to truly understand them.

I’ve been invited by Google to attend its annual I/O conference as part of the Builders Program, and I’m incredibly excited. It’s my first time at Google, and this time I brought a camera with me to capture the experience and create a recap video afterward. During the event, I’ll be conducting two fascinating interviews with Google employees, focusing on AI. These will be published at a later date. I’ve already met some amazing people from the community. Here’s to two unforgettable days!

We raised $3M to build Lucius AI - the Context Layer for Your Organization. Backed by Future Capital Discovery Fund, we’re tackling a problem we kept running into ourselves: Individuals ship 10× faster with AI. Organizations don't. Over 30% of your team's time is spent rebuilding context someone already had. It shows up everywhere a decision was already made but can't be found again - community operations, customer support, pre-sales reception, sales research, project management, internal collaboration. We're building Lucius to close that gap. Video

Any tool-calling model on OpenRouter can call web search and web fetch agentically. The model decides when to search, what to search for, and how many times. We've added @p0 as a new web search provider. Learn more: openrouter.ai/announcements/…