Writing

Using cell biology naming not as metaphor but as engineering manual — how mTOR's biology predicted circuit breakers, autophagy, and negative feedback loops before we designed them.

A 96% rejection rate that was actually a 96% false positive rate — how a monitoring blind spot turned a productive overnight batch into apparent failure.

The architect writes the tests. The implementer makes them pass. No prose specs, no circular validation.

I dispatched a 952-line monolithic CLI through my own coding-agent dispatch system to be refactored into seven modules. It worked. Notes on what self-bootstrap reveals about agent harness design.

I scraped 16,181 of Simon Willison's posts and analysed the 395 from 2026. An inflection in November 2025, GLM-5 closing the gap, and why the harness — not the model — is the competitive moat.

Most advice about making CLIs agent-friendly is just good CLI design. Only four principles are actually agent-specific.

I benchmarked four AI coding harnesses on 12 tasks using the same model. The harness barely matters for correctness — it's all about the model.

Smart model plans, cheap model builds. The pattern everyone's converging on for AI coding agents — and the piece nobody's shipped yet.

I turned the seven patterns into a zero-dependency Python library. Then I added MCP bridge support. Here's what I learned about the gap between patterns and code.

Three independent authors converged on nearly identical patterns for CLIs that AI agents invoke. Here's what they agree on, what's missing, and why nobody has built a framework for it yet.

When a dispatcher and an executor share a name, you don't have a naming problem. You have an architecture problem.

Multi-engine search agreement is not primary-source verification. A cautionary tale about hallucinating reference content from consistent secondary summaries.

Willison says CLIs beat MCP. Cloudflare says server-side code mode beats both. They're both right, because they're answering different questions.

I designed an elegant framework install for my personal AI system. Then I listed the hard problems and shipped a three-hour cleanup instead.

A day of reading skill repositories taught me less about the skills themselves than about how much I'd missed of the surrounding ecosystem.

Five skill repositories, a day of reading code, and a significant correction I had to make the same afternoon.

Naming as a design constraint: if a tool resists a name, the tool needs redesigning, not the name.

I renamed one concept across 130 files. The pain crystallized into a tool that will do the next rename in minutes.

Most projects pin dependency versions to avoid breakage. We automated the opposite: daily upgrades with automatic rollback.

We blamed the LLM for a 54% task failure rate. The real culprit was seven layers of dispatch infrastructure between intent and execution.

Compliance-first governance produces paperwork. Design-first governance produces systems you can actually explain to a regulator.

Engineering metaphors give you clean abstractions. Biology gives you resilient ones. Twenty design heuristics from four billion years of R&D.

Repo boundaries enforce access control, not abstraction. Directories handle abstraction. If two things have the same visibility requirement, they belong in the same repo.

Every other component in the organism had a biological name. Agents didn't. Three failed attempts and a yeast cell later, they do.

Three components of a living system map cleanly to tool, skill, and agent. The biology isn't decoration -- it's the test.

The defining property of life is not metabolism or reproduction -- it's autopoiesis. A system that continuously produces and maintains itself. That's the north star.

Force every component to carry a biological name. Study the mechanism. The gap between biology and your system is the design insight.

A method for finding gaps in AI systems that architecture reviews miss. Force a naming constraint, and the breaks reveal what's missing.

Atoms have forces. Molecules have shape. Cells have organization. That's why cell biology is the design library.

Pick one biological level for all your naming. The constraint is the design exercise.