Ai-Workflow

Written with Claude. The direction I wasn’t using Last week I wrote about the spec-to-kill method. Short version: I had an MCP server I’d built six months earlier, cargo-culted into eleven tools, genuinely useful but Rube Goldberg. I couldn’t decide whether to clean it up or rip it out. So Claude and I specced the best possible version (handler separation, schema validation, four clean tools), and then I asked whether even the ideal version earned its keep. The answer was no. grep handled the dataset. The spec became the teardown blueprint. ...

Written with Claude. I was about to build the wrong filter I have a lot of GitHub issues on my field-service app. A hundred and sixty-one open, mostly written by me, about my own tool. Most of them I still want to fix. Some of them became stale before I got to them. ...

Written with Claude. The Swiss army knife you have to hold a certain way I run a field data collection platform for building assessments. The app has AI agents that look up equipment costs, write condition narratives, validate data, and generate reports. Those agents need to find cost records in a database, do deterministic math (no LLM arithmetic on dollar amounts), and write results back to a JSON file. ...

Written with Claude. The wall I keep hitting I’m building a field data collection app. SvelteKit PWA, offline-first, syncs equipment photos and condition data from a tablet back to a server where the analysis pipeline picks it up. Standard tooling for my day job as a mechanical engineer doing facility audits, property condition assessments, and energy assessments. ...

I built Memory Bank for my home infrastructure repo. Three months later, I finally applied it to the system that actually makes money. Then during a real project, the whole data architecture changed.