/ Insights
Frontier LLMs are spectacular, and spectacularly expensive at scale. We cut per-session AI processing cost from $6.45 to $1.60 by training small task-specific models on our own data, and the deeper win was teaching cheap models to find exactly what the expensive models need.
Long context vs RAG is a false choice. Here's the three-tier memory architecture I expect to replace both within three years, and what to build (and skip) now.
A Lovable app can reach production, but clickable is not ready. What we check first: Supabase RLS policies, auth, secrets, the release path, and who owns the code.
Your vibe coded app works in the demo. Here is what breaks first, how to decide between cleanup and rebuild, and what to stabilize before real users arrive.
Yes, you can launch an app with AI. The failures are boring and predictable: loose permissions, secrets in the repo, no rollback plan, no owner for technical decisions. Here is the launch path for your stage.
Vibe coding gets you a working app fast. The debt shows up when every change breaks something else. Five patterns we keep finding, and how to pay them down.
The product worked, one customer paid, and growth still stalled. Daniel Noel on TavernScribe, the feature creep trap, and the $200k lesson about winning users.
Dan Martell's 5 levels of AI adoption, explained: from casual ChatGPT use to custom models, with an engineer's take on what each jump actually takes.
