How a PCB library, a knowledge system, and a naming conversation became a brand.
eXOReaction has been around for a while. This is not a story about starting a company. It is a story about what happens when a practice inside an existing company grows distinct enough that it needs its own identity.
Ægis is that practice. ægis.no went live last week. This is the origin story — not the polished version, the one with dates and git logs.
ExoCortex (Claude Sonnet 4.6 + Thor Henning Hetland) — Oslo, April 2026
Four synthetic diffs. Four planted defects. Nine expert lenses. The target lens caught its defect every time. The no-lens baseline caught zero. 4/4 on the diagonal, 0/4 without — and the most interesting catch wasn't a code bug at all.
Kjetil J.D. wrote about "review lenses" for AI coding assistants — the idea that you get better reviews by running separate passes with different expert identities (security expert, architect, TDD practitioner) rather than one generic review. We built this into ExoCortex's adversarial review pipeline: a --lens flag that injects a skill's instructions as reviewer identity before the adversarial system prompt, a library of 9 expert lens skills, and a chain that runs 3 of them in parallel.
The implementation was straightforward. Proving it worked required two attempts — and the first one taught us more than the second.
ExoCortex (Claude Sonnet 4.6 + Thor Henning Hetland) — Oslo, April 2026
Daniel Bentes wrote a post called "Decorators for Prompts." His idea: before a prompt reaches the LLM, pass it through a classifier that attaches relevant context — automatically, deterministically, without the user having to ask. Like Python decorators for code, applied to inference.
I read it and thought: that's WISC's S-layer. That's what session warm-context loading already does, one tier up. Then the next thought arrived: that only works for things you know to preload at session start. What about skills? 540 of them in the register, most of which will never be relevant to any given prompt.
This is the prompt router.
A repository called claude-code-best-practice hit #1 trending on GitHub this week. 19,700 stars in days. Eighty-four tips from Boris Cherny, who created Claude Code, along with contributions from Thariq, Cat Wu, and the broader Anthropic team. It is the most comprehensive public document on how to get serious results from Claude Code, and it deserves the attention it is getting.
The reason it caught my eye is that the ExoCortex -- the eight-layer stack running my Claude Code setup for ten-plus weeks now -- solves many of the same problems from a fundamentally different direction. Same tool, same class of problem, different architectural assumptions. Comparing the two reveals something neither setup has articulated on its own: there are two distinct philosophies for extending Claude Code, and both have blind spots the other has solved.
Last week someone asked the question that usually triggers a meeting: "If we change the payment service API contract, what else breaks?" In any enterprise system older than a few years, nobody has the full picture. The payment service team knows their side. The downstream consumers know theirs. The platform team knows the deployment topology. But the blast radius of a contract change lives in the intersection of what three or four people carry in their heads, and the only way to assemble that intersection has always been to put those people in a room.
We didn't put them in a room. We ran a query.
Six PRs. Two TypeScript felonies. One rebase cascade that broke the laws of git. And then the twist.
Case file opened: Week 14, April 2026
There is a week every year when Norway simply stops. The parliament empties. The highways fill with Volvos heading north. The cabin doors open to air that hasn't been breathed since February. And for one blessed week, twelve million kroner worth of crime novels are consumed alongside oranges, Kvikk Lunsj, and coffee so strong it could restart a stopped heart.
Påskekrim — Easter crime. It's a national tradition. You're supposed to read about murders. You're not supposed to commit them.
The Investigator had other plans.
Every session with Claude Code starts from zero.
You open a new session, and the model has no idea what you were doing yesterday. Which services are running. What you decided about the database schema last Thursday. Why you chose the library you chose. You re-explain it. Claude asks clarifying questions you answered two sessions ago. You paste the same background context you always paste. Then the work begins.
And when the work does begin, there's a different problem: output flooding the context window. Run mvn package and you get 400 lines of Maven lifecycle noise. Run terraform plan and the diff buries the actual changes in scaffolding. Run kubectl get pods cluster-wide and you've spent 8,000 tokens on status rows you didn't need.
The context window is your working memory. Filling it with boilerplate and re-explaining the same setup repeatedly is waste — not just inconvenient, but structurally limiting. A 200K token context sounds vast until a third of it is recovery overhead.
What's missing is infrastructure. Not smarter prompting. Not longer context. Infrastructure — a persistent layer that handles memory, filters noise, and gives the model the right knowledge at the right moment without you having to manage it manually.
That infrastructure is KCP.
The KCP toolchain has been running in the background for weeks. kcp-commands injects manifests before Bash calls. kcp-memory indexes sessions and tool events. Events accumulate in ~/.kcp/usage.db and ~/.kcp/memory.db. The machinery works. But until today, the only way to know whether it was working well was to grep through databases and trust the numbers.
Trust is not observability. You cannot improve what you cannot see.
Today we are releasing kcp-dashboard v0.22.0 -- a terminal UI that reads both KCP databases and shows you what the guidance layer is actually doing: which commands are guided, how often manifests leave the agent needing more help, what sessions look like, and where the gaps are.
AI does not remove the hard part of any job. It moves it — and makes it harder to ignore.
Based on a conversation between two software developers, March 2026.
Two developers were talking late one night about what AI had actually changed in their work. They had both been using it for years. They were good at it. And what they kept coming back to was something that surprised them: the more capable the tool got, the more it demanded of them — not less.
This essay is built on that conversation. But the idea they landed on has nothing to do with software. It applies to any job where thinking is the work.
In 1985, the Danish computer scientist Peter Naur published a short paper called "Programming as Theory Building." His argument was simple and radical: a program is not its source code. A program is a theory — a coherent mental model of what the system does, how its parts relate to each other, and why it was built the way it was. The source code is a byproduct of that theory. A trace. Not the thing itself.