AI

A CSV import that reported failure but had actually succeeded, a 60-second load balancer timeout, and fifty sequential calls to the Mailgun API stacked up inside a loop. The old version of this debugging session takes two hours on a good day: dashboard to codebase to database client and back, assembling the picture piece by piece and holding it all in your head.

With New Relic's MCP server, MongoDB's, and the full codebase live in one Claude Code session, the picture assembles itself. Diagnosed, fixed, and deployed in a single sitting, along with a silent behavior gap no alert would ever have flagged, because from the system's perspective nothing was wrong.

The Star Trek Communicator & Away Team Action Playset shipped for webOS in 2009: fourteen characters, hundreds of voice clips, a draggable phaser dial, and a communicator grill that actually opened and closed. Then Palm died, and the app went with it.

Sixteen years later I handed the whole thing — Mojo scene assistants, sprite sheets, 8.5MB of assets — to Claude Code and played domain expert while it wrote every line of React. Here is how the port went: the invisible overlay swallowing clicks, the border-image-slice fill keyword nobody remembers, the fifteen years of border-box muscle memory that turned out to be exactly wrong, and the phaser overload easter egg, restored.

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.