Building

• Most of my time lately has been spent between my new startup and my new baby, but when I can squeeze in some free time I'm trying my hand at an autonomous programming agent based around Aider. First attempts with prompts I found from elsewhere were just mediocre, but I'm thinking something like this:
• Aside from Aider's internal repo map, I'll create a list of all the files in the repository and use an LLM to create a short description of each one.
• Core loop:
  • Get a task from plan.md
  • Use file_map.yaml to ask about relevant files, add these to Aider.
  • Generate a plan to implement this task. Lay out the interfaces between the frontend and backend, but do not generate any code except for type definitions.
  • Generate integration tests for the plan
  • Generate the frontend code based on the plan.
  • Generate the backend code based on the plan and the generated frontend code.
  • Check types and edit until type checks pass
  • Generate unit tests for the code
  • run/edit loop on tests
  • once tests pass, run format step
  • run postmortem step updating docs and lessons
  • update file_map.yaml file to describe any newly added files or files that
• As I make progress on this I may need to also come up with a system for developing detailed plan documents for each task. It can be done in ChatGPT/Claude but it gets a bit hard to manage all the different feature-specific chats all in one, and it would be nice to have this live alongside the actual coding agent and be informed by the repository structure.

Learning

Today I read StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows. This paper talks about using state machines to explicitly model the problem that an agent is trying to solve. They got great results, and I agree that it's a good way to go about solving a problem, but I'm not sure what's really new here. Maybe it's just my background, but using state machines to direct the high-level behavior of your agent feels like a very obvious thing to do, and a lot of people in the LLM space have been talking about it for a while now.

Learning

Today I presented a review of The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey at the Latent Space paper club. While it wasn't recorded, my slides are published on Google Slides.