writing · 90 essays
Essays on engineering leadership, the agentic SDLC, platform engineering, and enterprise AI — what actually constrains software delivery.
// featured · September 22, 2026
Every default, error message, and exception communicates how work gets done. The question is whether that's the lesson you intended.
Read essay$ vinny init agentic-sdlc → spinning up reasoning loop ✓ spec generated · 217 lines ✓ tests scaffolded · 14 cases ⚠ coverage gap detected · branch /auth → prompting refinement ✓ branch coverage · 96% // shipping behavior, not just code
Omarchy installed on a drawer laptop in two minutes and everything worked. The reason is a platform lesson: choose the defaults, keep the escape hatches, and make the system legible to the agent.
The frontier labs are asking to slow down, and I think most of them mean it. Bruce Yandle's 1983 theory explains why sincerity isn't the question that matters. The question is who can afford the rules that follow.
Kiro's frontier engineering principles describe how to work well with agents. Making that practice dependable across a team means treating the shared workflow as a software release, with a version, a scope, an owner, and tests for when it should stop.
Coding agents can reduce implementation effort without reducing every obligation that follows. Revisiting Richard Gabriel's argument raises a different question: which design is simplest to verify, use, change, and operate, and for whom?
An AI tool can save a developer an hour while the delivery journey consumes most of the saving. The developer experience ledger follows the work across that boundary, so platform teams can tell what they removed and what they merely moved.
Sixteen months of building and writing taught me that AI makes implementation cheaper while raising the value of clear intent, trustworthy platforms, operational understanding, and human judgment.
The obvious version of AI developer experience is a chatbot in the portal. The better version diagnoses the failure, repairs what the platform owns, and reruns to green before the developer opens the log.
Open weights and closed models are moves in a fight over where margin survives. For enterprise teams, the durable assets are the evaluations, context, tool contracts, and audit evidence that keep model providers replaceable.
A gate proves a standard ran. It does not prove anyone can operate the result at 3 a.m. Operability has to become part of the spec, and the evidence has to survive the merge.
A runaway collection of agents, local models, and browser processes pushed my Mac past the point where Activity Monitor was useful. So I built System Headroom, and Apple's sandbox ended up shaping the product more than I expected.
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