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. ConceptCoding 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. ConceptAI + Good Architecture = Quality CodeAI doesn't change the fundamentals of good software — it amplifies whatever foundation you give it.
  3. ConceptBuilding software products with AIMindset shifts and practical approaches for building software products with AI assistance.
  4. ConceptDesigning Agentic WorkflowsThe fundamentals of designing, evaluating, and debugging agentic AI workflows.
  5. ConceptDesigning Agents as Teammates Not ToolsEffective agentic AI requires treating agents as teammates who need proper context and experience design, not as tools you configure with the right parameters.
  6. LessonSoftware 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.
  7. LessonThere'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.
  8. ConceptWe 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.
  9. LogPlanning (Agentic Pattern) with code improves performanceLetting the LLM express its plan in software code that you can just execute can be a very powerful way to let it write rich plans.
  10. ConceptAgents can be evaluated objectively or subjectivelyCan the evaluation condition be expressed mathematically? If so, we can evaluate with code. If it can't, it's a task that requires non-deterministic thinking.
  11. ConceptAgentic 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.
  12. Concept"LLM as a judge" grading with a rubric gives more consistent results when evaluating Reflection (Agentic pattern)"LLM as a judge" can help by providing a rubric that the LLM can use in the reflection prompt to judge the output.
  13. LogReflection (Agentic Pattern) can use external feedback from tools to improve outputBy using a tool for web research, the LLM can adjust course with the external information.
  14. ConceptAgentic design patternsReflection, tool use, planning and multi-agentic workflows.
  15. ConceptAgentic AI can be less or more autonomousFrom all steps predetermined to agents that make many decisions autonomously and can create new tools on the fly.
  16. ConceptAgentic AI works better for well know, linear processesAgents work better with a clear, step-by-step process and standard procedures to follow. Its job gets harder if the steps are not known ahead of time.
  17. ConceptSoftware 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.
  18. LessonCoding 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.
  19. ConceptContext compression for better code generationResearch, planning, implementation: an iterative process aimed to improve the AI context.