AI Native development principles and practices | Patrick Debois

Thank you! I'm not sure if we go back to the roots of DevOps, to be honest. I want to go out to the edges and the new things. That's probably more fitting for me. The intro said 'principles and patterns'. I haven't gotten to principles yet. I'm just going to talk about patterns. Why the horses? The mist is there; we don't know yet what AI is going to give us. That's one way of looking at it. The other way could be the four horses of the apocalypse. So, that could be as well. Also, because unicorns don't exist, and we're trying to make everything work right now. All right, let's kick it off. Development, Copilot... There was an earlier presentation on this. That's what most people think about. Like, 'Tab completion, very easily'. But we'll move quite quick. The chat came. We're going to do copy and paste. That was the next thing. 'Oh, great. Suggestions'. Why are we copying and pasting? Why can't we just say 'Apply' and done? Okay, we got more confident on doing this. Why only have the one completion? Why not do multiple lines, completions at the same time? And I'm trying to build up the whole thing. Now it's predicting where my next cursor should be. Like, I'm editing here. 'Just go there, start your work here'. It started understanding our code bases. It was a little bit of a funny thing. Everybody was talking about fine-tuning. We kind of went off that track and said, 'Let's look at our local code bases'. It was funny, because the big companies asking for fine tuning were the first ones to say, 'Not on our code'. So, this was kind of the solution: doing it locally, keeping it locally. But we went to one file or a tab completion to multiple files now being suggested. So, it's just taking more things, like the terminal feedback. It's not just your code completion. It looks at our browser. Also cool, right? It basically understands our loop: code, browser, open, taking everything into account. Generating tests for test coverage, yes. Although it's a little bit tricky, writing the test for itself, 'Who watches the watchers?' But that aside, it is definitely helping us with that as well. And then came reasoning models. So, instead of just doing the completion and the code, it started more thinking. Obviously, it's not thinking, but kind of breaking down our problems and reasoning what we should do, and then doing the suggestions. And one of the tools, I don't know if you've come across it, Devin, made a big splash saying, 'We can just do that continuous loop'. It actually looks at the terminal, opens the browser, kind of does that stuff. So, we've come a long way from that simple tab completion that most people know Copilot, where we are now at with the tooling. And then, why have one Devin, right? You can have multiple Devins work at the same time on your code base, maybe on this directory, maybe on this part. Good as well. My main point is, it's not anymore about the LLM. It is expanded from LLM, adding the RAG to the local indexing, to the functions that it's calling. And it just went on and on. That technology is very interesting, but we're not going to talk about it today. But it just gives you a sense that... Like, when people discuss, 'Model A is better than B'. That's great, but there's a lot of other factors that go into making this work. And there is a belief or a hope, much like we had with autonomous vehicles, that one day it will drive for us, completely autonomous. Now, I don't know... In some cases, it succeeds quite well. In other cases, we're still not there yet. But look at the money that we've invested in there. But I do think that generative AI is actually different from the traditional AI this is built on. Generative AI is inherently prone to errors. So, we should treat it differently. I'll come back to that later. And now we see new technology bring new technology roles. We're not all the experts, because nobody was the expert before in that new technology, which is real.