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…readers for visually impaired people. And So that's another way. And then the third way is kind of a hybrid where you present the screenshot, but you also present like a textual summary of the output. And that's the one that I think will probably work best. What we're using is we're just using text at the moment. And that's just an implementation issue that we haven't implemented the. Visual stuff yet, but that's kind of like we're working on it now. Another thing that I should point out is we actually have two modalities for web browsing. Very recently we implemented this. And the reason why is because if you want to interact with full websites you will need to click on all of the elements or have the ability to click on all of the elements. But most of our work that we need websites for is just web browsing and like gathering information. So we have another modality where we convert all of it to markdown because that's like way more concise and easier for the agent to deal with. And then [00:36:00] can we create an index specifically for agents, maybe a markdown index or something like that would be, you know, would make sense. Oh, how would I make a successor to Swebench? So I mean, the first thing is there's like live code bench, which live code bench is basically continuously updating to make sure it doesn't leak into language model training data. That's easy to do for Swebench because it comes from real websites and those real websites are getting new issues all the time. So you could just do it on the same benchmarks that they have there. There's also like a pretty large number of things covering various coding tasks. So like, for example, Swebunch is mainly fixing issues, but there's also like documentation, there's generating tests that actually test the functionality that you want. And there there was a paper by a student at CMU on generating tests and stuff like that. So I feel like. Swebench is one piece of the puzzle, but you could also have like 10 different other tasks and then you could have like a composite [00:37:00] benchmark where you test all of these abilities, not just that particular one. Well, lots, lots of other things too, but Yeah, so that's a great question. So we have a basic coding and browsing agent. And I won't say basic, like it's a good, you know, it's a good agent, but it does coding and browsing. And it has instructions about how to do coding and browsing. That is enough for most things. Especially given a strong language model that has a lot of background knowledge about how to solve different types of tasks and how to use different APIs and stuff like that. We do have [00:38:00] a mechanism for something called micro agents. And micro agents are basically something that gets added to the prompt when a trigger is triggered. Right now it's very, very rudimentary. It's like if you detect the word GitHub anywhere, you get instructions about how to interact with GitHub, like use the API and don't browse. rigger is triggered. Right now it's very, very rudimentary. It's like if you detect the word GitHub anywhere, you get instructions about how to interact with GitHub, like use the API and don't browse. Also another one that I just added is for NPM, the like JavaScript package manager. And NPM, when it runs and it hits a failure, it Like hits in interactive terminals where it says, would you like to quit? Yep. Enter yes. And if that does it, it like stalls our agent for the time out until like two minutes. So like I added a new microagent whenever it started using NPM, it would Like get instructions about how to not use interactive terminal and stuff like that. So that's our current solution. Honestly, I like it a lot. It's simple. It's easy to maintain. It works really well and stuff like that. But I think there is a world where you would want something more complex than that. Yeah, I think the Anth, so the Anthropic MCP is like, a way to It, it's essentially a collection of APIs that you can use to interact with different things on the internet. I, I think it's not a bad idea, but it, it's like, there's a few things that bug me a little bit about it. It's like we already have an API for GitHub, so why do we need an MCP for GitHub? Right. You know, like GitHub has an API, the GitHub API is evolving. We can look up the GitHub API documentation. So it seems like kind of duplicated a little bit. And also they have a setting where [00:40:00] it's like you have to spin up a server to serve your GitHub stuff. And you have to spin up a server to serve your like, you know, other stuff. And so I think it makes, it makes sense if you really care about like separation of concerns and security and like other things like this, but right now we haven't seen, we haven't seen that. To have a lot more value than interacting directly with the tools that are already provided. And that kind of goes into my general philosophy, which is we're already developing things for programmers. You know, how is an agent different than from a programmer? And it is different, obviously, you know, like agents are different from programmers, but they're not that different at this point. So we can kind of interact with the interfaces we create for, for programmers. Yeah. I might change my mind later though. So we'll see. It's definitely not random. It's like, if you think it's more complex than it's. Like, just intuitively, it's more likely to fail. I've gotten a bit better at prompting also, so like, just to give an example it, it will sometimes fail to fix a GitHub workflow because it will not look at the GitHub workflow and understand what the GitHub workflow is doing before it solves the problem. So I, I think actually probably the biggest thing that it fails at is, um, er, that our, our agent plus Claude fails at is insufficient information gathering before trying to solve the task. And so if you provide all, if you provide instructions that it should do information [00:42:00] gathering beforehand, it tends to do well. icient information gathering before trying to solve the task. And so if you provide all, if you provide instructions that it should do information [00:42:00] gathering beforehand, it tends to do well. If you don't provide sufficient instructions, it will try to solve the task without, like, fully understanding the task first, and then fail, and then you need to go back and give feedback. You know, additional feedback. Another example, like, I, I love this example. While I was developing the the monitor website that I, I showed here, we hit a really tricky bug where it was writing out a cache file to a different directory than it was reading the cache file from. And I had no idea what to do. I had no idea what was going on. I, I thought the bug was in a different part of the code, but what I asked it to do was come up with five possible reasons why this could be failing and decreasing order of likelihood and examine all of them. And that worked and it could just go in and like do that. So like I think a certain level of like scaffolding about like how it should sufficiently Gather all the information that's necessary in order to solve a task is like, if that's missing, then that's probably the biggest failure point at the moment. Yeah, self, self improving. So I think the biggest reason, like the simplest possible way to create a self improving agent. The problem with that is to have a really, really strong language model that with infinite context, and it can just go back and look at like all of its past experiences and, you know, learn from them. You might also want to remove the bad stuff just so it doesn't over index on it's like failed past experiences. But the problem is a really powerful language model is large. Infinite context is expensive. We don't have a good way to [00:44:00] index into it because like rag, Okay. At least in my experience, RAG from language to code doesn't work super well. So I think in the end, it's like, that's the way I would like to solve this problem. I'd like to have an infinite context and somehow be able to index into it appropriately. And I think that would mostly solve it. Another thing you can do is fine tuning. So I think like RAG is one way to get information into your model. Fine tuning is another way to get information into your model. So. That might be another way of continuously improving. Like you identify when you did a good job and then just add all of the good examples into your model. Yep. Yeah, I mean, we're calling them workflows, but they're simpler. Yeah, I totally, I think it's possible. It's just, you know, not, not trivial at the same time. I'll explain the two curves. So basically, the base, the baseline is just an agent that does it from scratch every time. And this curve up here is agent workflow memory where it's like adding the successful experiences back into the prompt. Why is this improving? The reason why is because just it failed on the first few examples and for the average to catch up it, it took a little bit of time. So it's not like this is actually improving it. You could just basically view the this one is constant and then this one is like improving.…

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