

Chain of Thought
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@cot_research
Independent research on the Machine Economy - AI infra, robotics & crypto. Subscribe to The Daily Chain ↓ (12k+ smart folks)



BREAKING: $1.4 Trillion company Meta just bought Moltbook. The world's first social network built entirely for AI bots. No humans allowed. Only AI agents posting, commenting, upvoting and downvoting each other while their human creators watch from the sidelines. Think of it as Reddit but solely for AI bots. In less than a week after launch Moltbook had over 37,000 AI agents using it and more than 1 million humans visiting just to observe. Moltbook's founders are now joining Meta Superintelligence Labs run by former Scale AI CEO Alexandr Wang. Now think about what this actually means. Meta already runs the biggest social networks on the planet with 3 billion humans. Now they want to build the infrastructure for AI agents to communicate, coordinate and work with each other too. This is not just a social network acquisition. This is Meta positioning itself as the operating system for the age of AI agents. OpenAI hired the creator of OpenClaw. Meta bought Moltbook. The race to own AI to AI communication just started. The internet was built for humans to talk to humans. What comes next is being built right now. And Meta just made the first move.


Three days ago I left autoresearch tuning nanochat for ~2 days on depth=12 model. It found ~20 changes that improved the validation loss. I tested these changes yesterday and all of them were additive and transferred to larger (depth=24) models. Stacking up all of these changes, today I measured that the leaderboard's "Time to GPT-2" drops from 2.02 hours to 1.80 hours (~11% improvement), this will be the new leaderboard entry. So yes, these are real improvements and they make an actual difference. I am mildly surprised that my very first naive attempt already worked this well on top of what I thought was already a fairly manually well-tuned project. This is a first for me because I am very used to doing the iterative optimization of neural network training manually. You come up with ideas, you implement them, you check if they work (better validation loss), you come up with new ideas based on that, you read some papers for inspiration, etc etc. This is the bread and butter of what I do daily for 2 decades. Seeing the agent do this entire workflow end-to-end and all by itself as it worked through approx. 700 changes autonomously is wild. It really looked at the sequence of results of experiments and used that to plan the next ones. It's not novel, ground-breaking "research" (yet), but all the adjustments are "real", I didn't find them manually previously, and they stack up and actually improved nanochat. Among the bigger things e.g.: - It noticed an oversight that my parameterless QKnorm didn't have a scaler multiplier attached, so my attention was too diffuse. The agent found multipliers to sharpen it, pointing to future work. - It found that the Value Embeddings really like regularization and I wasn't applying any (oops). - It found that my banded attention was too conservative (i forgot to tune it). - It found that AdamW betas were all messed up. - It tuned the weight decay schedule. - It tuned the network initialization. This is on top of all the tuning I've already done over a good amount of time. The exact commit is here, from this "round 1" of autoresearch. I am going to kick off "round 2", and in parallel I am looking at how multiple agents can collaborate to unlock parallelism. github.com/karpathy/nanoc… All LLM frontier labs will do this. It's the final boss battle. It's a lot more complex at scale of course - you don't just have a single train. py file to tune. But doing it is "just engineering" and it's going to work. You spin up a swarm of agents, you have them collaborate to tune smaller models, you promote the most promising ideas to increasingly larger scales, and humans (optionally) contribute on the edges. And more generally, *any* metric you care about that is reasonably efficient to evaluate (or that has more efficient proxy metrics such as training a smaller network) can be autoresearched by an agent swarm. It's worth thinking about whether your problem falls into this bucket too.








