ai notes.

Every note in one place. Concepts I wanted to explain to myself, lessons from building with agents, and the odd line from the log.

  1. Coding Patterns Stop AI ForgetfulnessUsing a strict coding pattern helps an AI write better code by giving its limited short-term memory a clear blueprint to follow.
  2. Software engineers won't need to write and read code only when agents will embed good engineering practices in their DNAAs long as we have to tweak the code ourselves, I don't think much will change. In fact, good engineering practices help AI generate better solutions.
  3. There's not difference in maintainability in AI vs Human generated codeLLMs are great at pattern recognition, so as long as the codebase is well designed and architected, it'll produce high-quality output.
  4. We should invest in AX (Agents Experience)AI agents are becoming our new teammates: we should treat them as a new entity that requires its own UX to work properly, a bit like DX.
  5. Agentic workflows are only as good as their weakest component + error analysisAgentic workflows follow the same principle as Theory of Constraints: look at the traces, count the errors, fix the bottleneck.
  6. Software systems integrating with AI still benefits from good software architectureAI is infrastructure, not architecture. The good practice is still to create an abstraction exposing a stable API.
  7. Coding with LLMs makes me think moreWhen working with AI-assisted tools like Cursor, I'm forced to think more about what I want the agent to do, so that it doesn't get lost.
  8. Context compression for better code generationResearch, planning, implementation: an iterative process aimed to improve the AI context.