One guy, coding alone at 5am, built the fastest-growing GitHub repo in history. 194,000 stars. Faster than React, Linux, and Kubernetes combined.
OpenAI, with thousands of engineers and billions in compute, couldn’t build it first. Steinberger connected Claude’s API to WhatsApp in an hour one night in November 2025. He called it a toy. Three months later, Meta’s Zuckerberg is DMing him on WhatsApp and Altman is offering Cerebras compute to win him over.
The math tells the whole story. Steinberger was spending $10,000-$20,000 a month of his own money, operating at a loss, routing sponsorship dollars to dependencies instead of his own pocket. OpenAI spent $13 billion of Microsoft’s money. And the solo dev’s agent framework went more viral than anything OpenAI shipped.
Sam calling him “a genius with a lot of amazing ideas” is doing a lot of heavy lifting. This is an acqui-hire of a project that proved OpenAI’s biggest vulnerability: the agent layer doesn’t need to be built by the model provider. Any developer with an API key and a messaging app could build a more compelling agent experience than the companies training the models. Steinberger proved it.
“OpenClaw will live in a foundation as an open source project” sounds reassuring until you remember that Chrome technically has Chromium too. Steinberger himself made the comparison. The open source version gets maintenance. The real agent capabilities get folded into ChatGPT’s product roadmap.
Sold his last company PSPDFKit for $100M+. Spent three years doing ayahuasca and traveling. Came back, failed at 43 projects, then built the most important open source AI agent on project 44.
OpenAI hired the guy who proved you don’t need $10B to build the agent future. You just need to ship faster than the committee can approve a product spec.
🚀 Introducing NSA: A Hardware-Aligned and Natively Trainable Sparse Attention mechanism for ultra-fast long-context training & inference!
Core components of NSA:
• Dynamic hierarchical sparse strategy
• Coarse-grained token compression
• Fine-grained token selection
💡 With optimized design for modern hardware, NSA speeds up inference while reducing pre-training costs—without compromising performance. It matches or outperforms Full Attention models on general benchmarks, long-context tasks, and instruction-based reasoning.
📖 For more details, check out our paper here: https://t.co/HJiqzwnUV7
🚨 پاکستانی فوج نے گلگت بلتستان کا جغرافیہ بدل دیا
فوج نے تحریک انصاف کے کارکنوں کو اسلام آباد جانے سے روکنے کیلئے شاہراہ قراقرم کے اطراف میں پہاڑوں کو بارودی سرنگ سے اڑا دیا 🔥
#احتجاج_سے_انقلاب_تک@GBTourism_#نومبر24_یوم_انقلاب
BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”
As someone who still vividly remembers trying to solve crazy hard geometry problems at the New Zealand IMO training camp in Christchurch way back in 1990... it kind of blows my mind to see how good AI has become at this! AGI keeps getting closer. https://t.co/kx1gpZ675F
The two Chinese labs working on replicating LK-99 appear to have found a room-temperature superconductor.
At first blush, here's what's different from last time:
• it's more like "room temperature" than room temperature, the paper says 250K which is -10 F or -23 C. That's still HUGE IF TRUE, because we can get things that cold with liquid nitrogen
• we have actually discovered a superconductor at this temperature before, but it was at high pressure. This paper says it's potentially superconductive AT AMBIENT, NORMAL PRESSURE
• it has *already* replicated, with two separate labs in China confirming the results. Last time the big question was "will it replicate." And the answer this time seems to be "it already has"
10 masterworks of painting that inspired iconic movie scenes - a thread 🧵
1. “The Kiss” by Gustav Klimt - “Shutter Island”, directed by Martin Scorsese
Thrilled to share #Lyria, the world's most sophisticated AI music generation system. From just a text prompt Lyria produces compelling music & vocals. Also: building new Music AI tools for artists to amplify creativity in partnership w/YT & music industry https://t.co/CMttmLPjoC
Yesterday morning I was happy with myself inferencing llama2.c 10M param model at 18tok/s. This morning people in the PRs are running it at 3000+ tok/s by compiling a little different. Yesterday I kicked off a 44M train run to try slow it down. Now upgrading to GPT-1 sized ~110M.
Remember -- we are making progress on AI (though far more on applications than on generality, which remains largely a green field). The progress is significant in speed and magnitude. But the conventional wisdom of the tech community about current and near-future AI capabilities is delusional, which leads folks to make all kinds of stupid decisions.
This is not new -- the conventional bay area wisdom on AI has been delusional every single year since at least 2015.
I'm excited about Segment Anything released from FAIR today. It tackles an old problem (find objects in images) at large scale: trained on 11M images and 1B objects.
This is a new Foundation Model for Computer Vision - it recognizes any object in any context.
there will be a generation of “ai-natives” and i saw a glimpse of the coming divide a few weeks back (pre gpt4 public launch)
was meeting with a founder who had prerelease gpt4 access, and hand to god this all actually happened:
NEW — Pfizer CEO Albert Bourla Announces Acquisition of Cancer Treatment Biotech Seagen For $43 Billion
“1-in-3 people in the world are going to have cancer in their lifetime…This is something like the mRNA for vaccines, [but] this is for cancer.”