Autoresearch clearly explained (why it matters)
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What is Autoresearch and why is it significant?
Andrej Karpathy, I mean, one of the godfathers of AI has just launched something called auto research and auto research is a huge deal and it's going viral on Twitter. And I just wanted to do an episode where I can explain to you in the clearest way possible. what it is, what are the use cases, how to make money from it, how to be more productive with it, how to create impact with it. And by the end of this episode, I'm going to give you a bunch of different ideas, use cases for how to use auto research. I'm going to explain it to you in the most clear way possible. And at the end, I'm going to tell you how you can actually get started with it. So let's go right into it.
So what is auto research? Well, it's like having a super nerd robot intern that runs science experiments on AI models for you all night without you doing the boring stuff. I mean, sounds intriguing, right? So how do you actually... program it or get started with it? Well, the first thing is you've got to give it a goal. So you can say something like, make this small AI model smarter. That's the goal. And then an AI agent will actually plan what to do, like different settings, code changes, edits the Python code for you, runs a short training experiment on a GPU, for about five minutes. It reads the results, and then it decides what to change next and to repeat the loop. So in some ways, if you've seen my video on the Ralph loop, where it basically would do engineering 24-7 and you'd wake up to new stuff happening, in simplest terms, that's what
Auto research is helping it do. You give it a goal. The AI agent does a thing. You tell the AI what better means. Cheaper leads, more clicks, higher sales, better model school. And then the AI keeps changing things. testing them, and it only saves the changes that improve. So what's really cool about it is you wake up, you grab the best version, and then hopefully you turn it into something you charge for or you give it away. I saw this tweet by Toby, who's the CEO and co-founder of Shopify. Auto research works even better for optimizing any piece of software. Make an auto folder, add a program MD. That's just a markdown file, which is really the foundation of how you're going to be using auto research, and a bench script, make a branch and let it rip.
That's why I started paying attention to auto research. When Andre Karpathy, Legend, and Toby and more people started playing with it, I'm like, okay, I've got to pay attention. I created this little visual for how to think about what auto research is. You set the goal, the AI plans an experiment, It edits and trains the code and settings. It runs a short training on a GPU. By the way, this is an important... I should mention that you need a... a nvidia chip to actually run auto research or you can do it in the cloud i'll talk about this at the end of the episode but you you know you do need that you can't just run it on let's say you have a macbook m1 or something like that it reads metrics it says is it a better result if it's if it's not it's gonna log the attempt and it's gonna discard the config if it's yes it saves it to the config um and then just plans a different experiment and it just you know hopefully gets better on your goal whatever it is so uh
Let's get into... We're going to get into some of the ideas, business ideas around it. But right before that, I just want to say here's a simple mental model for how I'm thinking about auto research. So... Imagine you have a research boss you can boss around. Number one, you write a clear task.
How does the Autoresearch loop function?
So for code experiments, maybe it's improve this model test score. For business, figure out the top five competitors for product XYZ and make a short report. Step two is you give the bot access to the code, a GPU for ML experiments. You obviously need to give it access to the internet and documents if you're doing reading tasks. The bot then runs a loop. So it plans, it acts, Meaning it might run code or search, it reads results, it updates the plan, and then you just come back later.
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