Four days building a real product with Claude Code — 180 commits, from scratch. What it reveals: the intoxicating speed, the traps AI creates on its own, what tests change when you're not writing the code yourself, and how far a PM can go without leaving their lane. A field report, not a doctrine.
Do you lose the thread the moment you reopen a conversation with an AI on a subject that stretches over time? This article lifts the hood on a structured memory system: the difference between the goal and the attention, the directives, the offline queue, the three memories (journal, synthesis, checkpoints) and the cycle of a session. Enough to understand how an AI can truly remember a mission.
Classic RAG starts from scratch with every question. By building an AI Wiki — a persistent wiki maintained by an LLM — you capitalize on domain knowledge once and for all. Atomic notes, links between concepts, contradiction detection: knowledge is compiled, not rediscovered. Lessons learned from 950 notes extracted from industrial maintenance sources, with calibration insights and concrete use cases.
Drowning in email and losing track of the threads in flight? By handing the structuring over to Claude — one file, a few directives, then a gradual split — I turned 500 building-management emails into a queryable knowledge base. Seven steps, from the vague directive to a multi-file system, without ever needing a complex framework.
A wasteful skill locks you out after 3-4 runs instead of 8-10. Discover the 5 silent traps that saturate your quota, how to audit in 3 simple phases, and how to gain 40% more capacity without changing your model. Productivity regained.
No more re-explaining the context at every session. Claude automatically retains what you did, the decisions made, the problems encountered — per project, inside your git repo, with no database. Portable, versionable, under $1/month. Here's how to set this system up in less than an hour.