Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…

spent my 11-hour flight back from europe working on a very long report. started as a slack message but morphed into a several pages long doc. wifi was as shitty as it gets. after finally making it home i realized that the computer had forcefully restarted. opened slack: draft was gone :( hail mary: claude pls save me, no clue how but pls try it checked APFS snapshots, time machine, slack indexeddb, write-ahead logs, service worker / http caches, local storage, app logs, hibernation image... nothing. all gone but then... it realized i have alfred installed. so it checked the clipboard snapshots alfred keeps in sqlite. sad news: alfred clipboard memory gets deleted after 24h. aggressive retention policy. however! when sqlite runs DELETE, nothing gets actually deleted. it only marks pages as reusable, but it doesn't override the physical bytes. so claude decided to do a raw-scan of the db, reverse eng alfred data format, figure out the portion containing the timestamp, stitched everything back together across overflow pages... and handed me the exact final version of my report, the last one i cmd C'd all this, in a single shot ... day 200 of "what if you had an elite hacker you can ask anything to"

Freepik is now Magnific One platform to rewrite the rules of creativity

Freepik is now Magnific One platform to rewrite the rules of creativity

Freepik is now Magnific One platform to rewrite the rules of creativity

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autorese… Part code, part sci-fi, and a pinch of psychosis :)

Probably nothing 🤯 🤖 An AI agent that autonomously does sales prospecting on its own with GPT-4. Powered by BabyAGI from @yoheinakajima & run on @Replit. Imagine once you integrate it with @langchain tools like @HubSpot or Apollo. Tip: don't forget to hit pause it like me.