Feedback-Loop

this post is part of a series about creating production-grade maintainable AI-first projects, using AI-first design patterns and frameworks. In the previous post I mentioned that an internal AI feedback loop will be central to all our AI-first design patterns. But “AI feedback loop” might mean different things to different people - so this lightweight post focuses on giving an example to make it concrete. We will implement a small (but realistic) project. The project is set up so the agent has an internal feedback loop - it has instructions that tell it to use a loop, and it has a clear way to create effective tests and run validations (the tests it creates, type-checking, linter). We’ll see how it makes mistakes, finds them and self-heals. ...

This mini-post is part of a series about good testing practices, which I also presented at a couple of conferences. Here it is in PyCon US 2023 Slow tests are not fun. In this post, I’ll talk about two ways in which they are not fun The bottleneck and the time bomb The feedback loop and the bug funnel The bottleneck and the time bomb The bottleneck here is where the tests take so long to run, that we have a long queue of tasks waiting to be merged to the main branch. (this assumes we’re merging tasks to the main branch one-by-one, and only after the tests pass. Other branching models have similar issues, but this is the simplest to explain) ...