Autoresearch, Agent Loops and the Future of Work
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The AI Daily Brief: Artificial Intelligence News and Analysis
25 min
2 speakers
3 chapters
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What is the main topic discussed in this episode?
Today we're discussing what Andrej Karpathy's weekend project about auto research can tell us about the future of work. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, AIUC, Blitzy, and InsightWise. To get an ad-free version of the show, go to patreon.com slash ai-dailybrief, or you can subscribe on Apple Podcasts. If you are interested in sponsoring the show, send us a note at sponsors at ai-dailybrief.ai. Also on aiDailyBrief.ai, in addition to finding out about all of the different things going on in the AIDB ecosystem, I would point you specifically to number three, our newsletter. We very strangely for a very long time have not had a newsletter. And part of the reason for that is that I was never sure exactly what we would add that was different than what the other good AI newsletters out there offered.
However, I was finally convinced that there was something very simple that many of you wanted, which was just links to the stuff that I had mentioned in the show that day. And so our newsletter is back. Appreciate everyone who has signed up for it since we relaunched it. If you want a quick, easy index for what that day's AI Daily Brief had and all the links to the relevant articles and content that are mentioned there, again, you can sign up with a link from AIDailyBrief.ai. Now today we are talking about a new project from Andrej Karpathy called Auto Research. And you might notice that we are doing an entire episode about this, instead of our normal division into the headlines and the main episode.
It's because I think that this topic is actually even more significant than it seems on the surface of it. One would be tempted to think that all of us nerds were just getting overexcited because Andrej Karpathy, who is held in such esteem, released the new GitHub repository. And while that is certainly true, there is something bigger going on here. You might remember a couple months ago me talking about something called Ralph Wiggum. Ralph is, in simplest terms, a software development loop that keeps running, building software in an iterative and persistent way by looping the same instructions over and over and over again. It's named after Simpsons character Ralph Wiggum for his lovable and indomitable persistence despite whatever's going on around him.
Now, we'll talk more about Ralph in a little bit, but the key concept to take away is this idea of an iterative loop. Carpathy's auto-research is also at core about an iterative loop, and I think combined what you have is arguably a new type of work primitive. Primitives are the basic building blocks of work that are so fundamental that they show up everywhere, across roles and industries, and that people reach for automatically once they have it. New ones don't come around very often, and so this idea that agentic loops might be one is, I think, worthy of some serious scrutiny. But let's talk about what Andre actually released first, and then we will come back to that. On Saturday, Andre, who was on the founding team at OpenAI, and who was previously the director of AI at Tesla, and who you might remember from coining such terms as vibe coding last February, and who has now suggested we are in a different era of agentic engineering as of this February, again tweeted on Saturday, I packaged up the auto research 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 around 630 lines of code, then the human iterates on the prompt, .md, and AI agent iterates on the training code, .py. The goal is to engineer your agents to make the fastest research project indefinitely and without any of your own involvement. In the image, which he shared alongside it, every dot is a complete LLM training run that lasts exactly five minutes.
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