π SUMMARY This call captured the community at its best: an informal, rotating mix of peer coaching, technical show-and-tell, and open Q&A where members traded real-world lessons on AI tooling, infrastructure, and side projects. Two big themes ran through the session. First, moving beyond slow sequential LLM loops toward typed, parallel-evaluation models (GEV/JEV via TypeSafe) for classification and judgment tasks, with concrete use cases from council vote tracking to forensic case analysis. Second, building and hardening real products: a security-rebuilt digital signage SaaS, a Supabase-to-Convex migration, an open-source CRM, a HIPAA-sensitive forensic-psychiatry tool, and Patrick's impressive setup deploying Claude-based support skills to ~2,000 enterprise users alongside a self-diagnosing personal agent. Whether you came for the architecture debates, the one-prompt side builds, or the school-event AI booth preview, there was something practical to take away β and plenty of the friendly banter that makes these calls worth showing up for. π‘ KEY INSIGHTS Patrick Chouinard used ChatGPT for an election decision by describing leadership priorities only (not parties) and asking for the government configuration that maximizes them β found it "surprisingly accurate." Paul Miller: dual-GPU home servers splitting model loads are cheaper than one large GPU. Paul Miller: for GEV/JEV-type models, best practice is breaking large questions into many small ones routed to appropriately-sized LLMs rather than one big complex prompt. Paul Miller: JEV is useful for categorizing large corpora (e.g., council documents) cheaply to detect votes/topics. Paul Miller: JEV beats a standard loop because it evaluates a batch of typed questions in parallel against the same state in one call β no sequential latency and no "context rot" from one question bleeding into another; adding questions barely changes response time. Paul Miller's own approach: "TypeSafe first, then train my own model."