Unit tests passed. Every one of them. Green across the board.
And then we ran the parser against real legacy Gerber files — files from actual PCB designs, exported by real design tools used by real engineers over the last twenty years — and the success rate was 25%.
Three out of four failed.
For thirty years I have broken work into tasks. Decompose the feature into subtasks, estimate the hours, write the code, move the ticket. The unit of progress was the line of code. The measure of a good day was how much I shipped. That loop was so deeply embedded in how I worked that I did not notice it was a loop. It was just what development meant.
Then I started delegating implementation to AI, and the loop broke. Not gradually. In about a week.
The first reaction is always disbelief.
"That's not possible." Or: "That only works for trivial problems." Or the politer version: "That must be very rough code."
So here are the numbers. Not estimates. Actuals.
Everyone tells the same story about AI-assisted development. AI generates code fast, so you ship faster. Straightforward. Compelling. Wrong.
The actual productivity gain from AI does not come from generation speed. It comes from verification infrastructure that makes it safe to accept AI output at scale. The counterintuitive truth: the team that writes the most tests ships the fastest. Not despite the testing. Because of it.
The plan was to spend a weekend validating whether a complete PCB design library was actually buildable at AI velocity.
Not a prototype. Not a demo with curated inputs. Something that could consume real Gerber RS-274X files — the manufacturing format that PCB designers actually export from KiCad, Altium, Eagle — parse them completely, and produce manufacturing-ready outputs.
I have been writing software for thirty years. In that time I have sat through thousands of daily standups, hundreds of onboarding sessions, and more planning ceremonies than I care to count. Most of them existed for one reason: transferring context from people who had it to people who did not. The new developer needs to know how the deployment pipeline works. The team lead missed yesterday's discussion about the API change. The architect needs to understand why the data model looks the way it does before approving the next feature.
These are not bad reasons to meet. But they are expensive reasons. And increasingly, they are avoidable ones.
You'd think parsing a part number would be trivial.
A string of characters. Match it against a pattern. Done.
Then you actually try to do it across a real component database, and you discover how much institutional knowledge is packed into those strings — and how little of it is documented anywhere.
Over the years I've tried a lot of organisational models. Standups. Retrospectives. Timesheets. Sprint planning. The full suite.
At eXOReaction, we've converged on something different. It's not a framework with a name. It's more a set of positions — things we've decided, and held to, even when they make people uncomfortable.
In 2017 I wrote a short piece out of frustration — a street-smart survival guide for developers tired of missing deadlines despite giving themselves plenty of time. Eight years later the problem hasn't changed. If anything it's worse: we have more tools, more planning methods, more retrospectives, and we still end up in last-minute panic on projects we thought we had under control.
I revisited the original piece in November 2025 with a bit of help from AI — research, narration, the lot. Here's the framework, written out properly.
Frøya describes herself as a cartographer of potential — not as the QA manager she was built to be.
The distinction is instructive. A QA manager checks definitions against standards. A cartographer is always working at the edge of the known, building the map as the territory reveals itself.