AI

An MCP bridge from Claude Desktop to MAME gave the model a full development loop on a 1981 TI-99/4A: write TMS9900 assembly, compile a cartridge, launch it, look at a screenshot, try again. It knew the memory maps and VDP registers cold. It had never run a single instruction on the hardware.

That gap showed up fast. Direct memory writes came back mangled by the datamux, a bus width converter the documentation mentions but never warns you about. A green backdrop came out purple because the console's own startup routine was fighting the code. A dozen iterations later, the logo was on screen in light red, since the 15-color palette has no orange.

The interview industrial complex broke in two directions. The FAANGs over-optimized until leetcode prep became its own industry; startups never built the rigor in the first place and hired for velocity. Both missed the same thing. Code was never the hard part. Knowing what to build and why was always the hard part.

I started as the engineering department at a startup and hired every person who came after, and I filtered for how people reason through ambiguity rather than how they perform while someone watches them type. Some people thought that wasn't rigorous enough. Then AI commoditized code production and the startup math got brutal: one thinker with AI tools does what used to take a thinker plus two implementers. Here's what I hire for now, and how the interviews changed.

The 10x productivity narrative assumes AI has changed the equation. It has, but not where most people are looking. AI makes architected work faster; unarchitected work just fails faster.

The shift happened for me while debugging authentication across three platforms and three languages — two days of coordination compressed into an hour. From there it became a method: a workspace where the model sees the whole stack at once, MCP connections to the real database instead of guesses about it, and planning documents that get stress-tested, argued with, and revised right up until the feature ships. What the method still needs is someone who already knows what good output looks like.

A guide to getting the most out of Copilot as a Drupal sidekick, built around knowing where its strengths stop. It will churn out boilerplate all day, but it is not a stand-in for actually understanding the code you are shipping.

That opening line matches my own experience with AI coding tools and autocompletions pretty closely. They are genuinely handy, and I would not want to give them up. But much like AI text and images, they will also confidently hand you complete nonsense, and noticing that is still your job.