I am not doing any of this. It becomes obsolete in literally weeks, and it also doesn't work 80% of the time. Like why write a mcp server for custom tasks when I don't know if the llm is going to reliably call it. My rule for AI has been steadfast for months (years?) now. I write (myself, not AI because then I spend more time guiding the AI instead of thinking about the problem) documentation for myself (templates, c…
A perspective which has helped me is viewing LLM-based offerings strictly as statistical document generators, whose usefulness is entirely dependent upon their training data set plus model evolution, and whose usage is best modeled as a form of constraint programming[0] lacking a formal (repeatable) grammar. As such, and when considering the subjectivity of natural languages in general, the best I hope for when using…
EG: Step 1: Define problem in PROBLEM.md Step 2: Ask agent to gather scope from codebase and update PROBLEM.md Step 3: Ask agent to create a plan following design and architecture best practices (solid, etc) and update PROBLEM.md Step 4: Ask agent to implement PROBLEM.md