RecapFlow : March 31st Coaching call analysis
📝 SUMMARY A wide-ranging and practically dense call covering Claude Code plugins, self-hosted AI infrastructure, compliance for financial clients, scope creep management, and the long-term future of programming. The strongest recurring themes were the importance of asking good questions, the discipline required to manage fast-moving client expectations, and the growing viability of running AI workloads on local hardware for data-sovereign use cases. Everything Claude Code and the Codex plugin stand out as immediate priorities for anyone building seriously with Claude Code. 💡 KEY INSIGHTS Use Claude to rewrite emotionally charged emails before sending. Write what you actually want to say unfiltered, then ask Claude to rewrite it professionally. Patrick, Ryan, and Morgan all do this independently. Patrick described it as eliminating email-related stress entirely. The last mile problem is real and expensive. Getting to 80% quality is easy, 90% is hard, and 95% feels nearly impossible — especially as clients raise expectations after seeing early success. Scope creep has become app creep and system creep. Because AI enables rapid delivery, clients immediately want more. Asking "is this on the critical path?" and "does this affect an OKR?" is now more important than ever. When clients start building with Claude Code themselves, they bring partially-built, insecure repos and expect contractors to consolidate and productionize them — often without pausing their own development. The most valuable skill for working with AI is knowing how to ask good questions. Formulating precise, contextual questions is described as old-school BA-type knowledge that is now more valuable than ever. Curiosity is the single most important skill to cultivate. Patrick's direct recommendation for anyone entering the field. Patrick's mental model for AI adoption: the current moment mirrors the mainframe-to-PC transition. Today's AI interfaces are like dumb terminals connecting to centralized compute. A small group of specialists are building deeply now. Mass adoption and personalized local models will follow, just faster than before.