Opensource

From Jarred Sumner on the PR:

Blog post with details coming soon.

It passes Bun's pre-existing test suite on all platforms (and fixes several memory leaks and flaky tests), the binary size shrinks by 3 MB - 8 MB, the benchmarks are between neutral and faster - and most importantly, we now have compiler-assisted tools for catching & preventing memory bugs, which have costed the team an enormous amount of development & debugging time over the years.

The codebase is otherwise largely the same. The same architecture, the same data structures. Bun still uses few 3rd party libraries. No async rust.

To try this, run:

bun upgrade --canary

Please do file issues if you run into any. If this thread gets crazy I will lock it.

Note:

• Still some optimization work to do before this lands in non-canary version.

• Still some cleanup work to do (which will come in a series of follow-up PRs)

Ambitious.

LLMs + a well thought out test suite they can verify against can go a long way.

From Andrej Karpathy:

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 :)

Been trying to think of ways to leverage this. There have been some amazing examples so far:

• Tobi, founder of Shopify made their template system 53% faster (keep in mind this has been around 20 years)

• Cheng Lou, made a leap forward in UI engineering with a text measurement algo in pure TS called pretext (I can’t tell if he used autoresearch specifically, but the footprints are there in the repo)

What else is autoresearch going to push forward?

Welp, that was fast.
From Sam Altman:

Peter Steinberger is joining OpenAI to drive the next generation of personal agents. He is a genius with a lot of amazing ideas about the future of very smart agents interacting with each other to do very useful things for people. We expect this will quickly become core to our product offerings. OpenClaw will live in a foundation as an open source project that OpenAI will continue to support. The future is going to be extremely multi-agent and it's important to us to support open source as part of that.

This has been the new hotness on the socials. But it's kinda just really good packaging for a local proxy to foundation models. Not saying it's nothing. Still might try it out. But I'm busy, yo.