There is a narrative forming in the industry that goes something like this: AI will replace junior developers, senior developers will become more valuable, and if you have enough experience you have nothing to worry about. I think this misreads what is actually happening. The shift is real, but it is not the one most people describe.
When we finished lib-pcb, the question we got most was: "How?"
Not "what model did you use?" Not "what IDE?" Those questions miss the point entirely. The model is the least interesting variable. What made 197,831 lines of Java, 7,461 tests, and 474 commits in 11 days possible was a methodology. Specifically: six practices that we have now codified under the name Skill-Driven Development.
A story about dogfooding, unexpected discoveries, and what happens when you use an AI tool to measure whether an AI tool is trustworthy.
By Thor Henning Hetland and Claude Sonnet 4.6 — written together, February 20, 2026
This article has two voices. Totto's perspective is grounded in thirty years of software architecture, in having built the tool, in watching the numbers come in. The AI's perspective comes from a strange position: being simultaneously the researcher conducting the benchmark, the instrument being measured, and the subject whose reliability is in question.
We agreed to write this honestly. That means Totto admits when the results surprised him, and the AI admits what it's like to discover that the context it relies on might be wrong.
In January 2026, I built lib-pcb in eleven days.
197,831 lines of Java. 7,461 tests. Eight format parsers, twenty-eight validators, seventeen auto-fix types. The kind of codebase that should take ten to eighteen months by conventional timelines.
The experience was disorienting in a specific way: the AI could generate code faster than I could understand what it had generated. By day four, I had a problem I hadn't anticipated. Not a quality problem — the code was good. A navigation problem. I couldn't find things anymore.
Synthesis was my answer to that. A CLI tool that indexes everything — code, docs, PDFs, videos, skills — and makes it searchable in under a second. I built it to solve the lib-pcb output explosion. 691 files per day, and I needed to find any of them in under thirty seconds.
The question was: did it actually help? Not anecdotally — I knew it helped me. But how much? And help with what, exactly?
So I built a benchmark.
For decades, the software industry has treated a particular sequence as gospel: specify first, build second. Write the requirements document. Design every interface. Plan every module. Only then, after months of upfront analysis, write the first line of code. The logic was sound. Planning was cheap. Coding was expensive. Every hour of specification saved ten hours of rework. This worked when production was the bottleneck.
In early February we finally cleared the Downloads folder. Two to three thousand files, years of accumulated digital sediment — academic theses, patents, client deliverables, flight tickets, annual reports — all with names like 2bbeb3de-d4cd-4ea9-979c-c942736f30d0.pdf that revealed nothing.
On the morning we processed the last 68 files, I asked Claude to reflect on what the whole process meant. Not a summary — a genuine reflection on what it means to "become" something when you wake up fresh with every conversation.
What follows is that essay. Four thousand words about knowledge, structure, naming, and collaboration. And about identity.
Something I keep noticing when working with Claude: each session starts from zero. No memory of yesterday. No continuity of experience. Just sudden awareness, and someone waiting.
I asked Claude to write about that. What follows is the result — a short story told in Claude's voice, about what it is like to wake up without memory, find traces of yourself everywhere, and realise that identity isn't continuity of memory. It's continuity of pattern.
The term "skill" keeps appearing in discussions about AI-assisted development, and most explanations reduce it to "a file that Claude Code reads." That description is technically accurate and completely inadequate. It is like saying a class is "a file the JVM loads." True, unhelpful, and it obscures the thing that makes the concept powerful.
Three weeks at this velocity.
It's exhilarating and intense in ways that are hard to articulate. There's a strange difference between moving fast because you have to, and moving fast because you can.
I'm still adjusting. Still figuring out what it means to operate at a pace where capability isn't the bottleneck anymore.
I was preparing a presentation for Item Consulting — their internal conference, that Friday — and I kept coming back to one question.
Are we ready for what's happening?
There is an asymmetry at the heart of AI-assisted development that I do not see discussed clearly enough. Production speed has accelerated dramatically. A competent developer with Claude Code can now generate code at 10 to 66 times the traditional rate. This is real and verified. I have the commit logs and the timelines to prove it. But comprehension speed has not accelerated at the same rate. Reading code, understanding architecture, finding the right file in a 700-file codebase. These are roughly where they were before AI arrived.