Ankur Goyal: prediction

Transcript context

…By the way, before you continue, I have a rule for building Smalltalk which we can talk about separately. It should be easy to do those AI calls. Because if it's a big lift, if you have to edit five files, you're not going to do it. But if you can just sprinkle intelligence everywhere, then you're going to do it more. I totally agree. And I would say, that probably brings me to the next part of it. I'd say probably 25% of the remaining usage is what you could call a simple agent. Which is probably a prompt plus some tools. At least one, or perhaps the only tool is a rag type of tool. And it is kind of like an enhanced chatbot or whatever that interacts with someone. Then I'd say probably the remaining 25% are what I would say are advanced agents, which are things that you can maybe run for a long period of time or have a loop or do something more than that simple but effective paradigm. And I've seen a huge change in how people write code over the past six months. When this stuff first started being technically feasible, people created very complex programs that almost reminded me of studying math again in college. It's like, you compute the shortest path from this knowledge center to that knowledge center, and then blah, blah, blah. It's like, oh my god. You write this crazy continuation passing code. In theory, it's amazing. It's just very, very hard to actually debug this stuff and run it. Almost everyone that we work with has gone into this model that actually exactly what you said, which is sprinkle intelligence everywhere and make it easy to write dumb code. It's a prevailing model that is quite exciting for people on the frontier today. I dearly hope as a programmer succeeds, is one where what is AI code? It's not a thing, right? It's just, I'm creating an app, NPX, create next app, or whatever, like FastAPI, whatever you're doing, and you just start building your app, and some parts of it involve some intelligence, some parts don't. Maybe you do some prompt engineering, maybe you do some automatic optimization, you do evals as part of your CI workflow. I'm just building software, and it happens to be quite intelligent as I do it because I happen to have these things available to me. That's what I see more people doing. The sexiest intellectual way of thinking about it is that you design an agent around the user experience that the user actually works with rather than the technical implementation of how the components of an agent interact with each other. When you do that, you almost necessarily need to write a lot of little bits of code, especially UI code, between the LLM calls. The code ends up looking kind of dumber along the way because you almost have to write code that engages the user and crafts the user experience as the LLM is doing its thing. Guy Podjarny So here are a couple things that you did not bring up. One is doing the Code Interpreter agent, the Voyager agent where the agent writes code, and then it persists that code and reuses that code in the future.…

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