AI & Technology

A few months back a friend shared Steve Yegge’s Welcome to Gas Town post with our group and asked where everyone is. The post lays out a ladder for the AI coding process - eight stages, from “barely using it” to “running your own orchestrator.” Stage 1: Zero or near-zero AI Stage 2: Coding agent in the IDE, permissions on Stage 3: Same agent, YOLO mode Stage 4: Wide agent in the IDE - code is mostly diffs Stage 5: CLI, single agent, YOLO Stage 6: CLI, multi-agent, YOLO - three to five in parallel Stage 7: Ten-plus agents, hand-managed Stage 8: Building your own orchestrator When my friend shared the original post, I was transitioning to Stage 6/7 so this post is a bit delayed because I wanted to share the state only when I’m comfortable where I’m at. For me each step was a process, way of working and ultimately a mindset leap. ...

When an AI product produces good output, three things had to go right. When it produces bad output, the cause is almost always in one of those three things. The model itself, Claude or GPT or Gemini or whichever, is the most visible variable. It’s rarely the most consequential. ...

After speaking with dozens of founders building AI-powered products, I’ve noticed a pattern. They’ll complain about model quality, debate between GPT-4 and Claude, or worry about hallucinations — but when I dig deeper, the real issue is simpler: they’re not controlling the temperature parameter. This single setting can dramatically change your results, yet most builders treat it as an afterthought or ignore it entirely. Understanding Temperature: The Technical Reality Temperature controls randomness in text generation. Here’s how it works: ...