26 posts
I built a method as a public repo and the product that runs it as two private ones, and none of them treated the others as a source of truth. The domain enum lived in four places. A persona drifted between its lens file and its API contr…
Three months ago I laid out spec-driven development and the folder architecture that makes it work. Most of the playbook held up in daily production use. Three ideas that essay never mentioned turned out to matter more than anything in i…
I hit the Fable usage cap twice in under 48 hours and nearly ran out the total token limit. The plan that would have prevented it was published on this blog a month ago. Here is why it failed anyway, where the plan lives now, and what th…
Spec-driven development, pointed at a life. Why my principles and goals live as markdown an AI agent reasons over, and the one rule that makes handing an agent your life safe: it can challenge the record, but it never writes it.
I built a quant research platform, then built an agent to operate it: a scheduled Claude session that reads the boards, keeps a pre-registered track record, and texts me three times a day without ever saying buy.
I built a self-healing RAG pipeline, a guardrails gateway, and an eval gate as one system, then threw 44 adversarial questions at it. Zero hallucinations, because the most important thing it does is refuse. Here is how trust got built in…
A year of building and operating a small fleet of finance and content products almost entirely through an AI coding agent. What worked, what was hard, the honest failures (including a flagship signal that measured nothing and an edge tha…
Prompt caching looks like a flag you flip for a cheaper bill. It is really the reuse of a stored prompt prefix, governed by three rules, and applying it across four parts of my own system showed where it pays, where it quietly does nothi…
Every system that fuses signals into one consequential number has a fault line: the data you trust enough to composite into a grade versus the data you only trust enough to watch. How I drew that boundary in my personal finance engine, a…
Four days after I said goodbye to Opus, an export-control directive pulled Fable 5 offline and the fallback became the workhorse again. What I shipped in the window, what it cost, and the model-tiering plan for when Fable comes back.
In April I published one week of SDD production numbers. The same data trail rerun for June 1 through 10 shows the velocity curve: 309 PRs opened, 293 merged, about 185 production deploys, and one footnote about outrunning GitHub Actions…
I handed a backlog to Claude Fable, told it once it could merge, and let it run. It shipped seventeen items across five repos. The line that mattered was not in the work it finished. It was in the work it refused to touch.
Anthropic shipped Claude Fable 5 and Mythos 5: same model, two names, one safeguard layer apart. What the new frontier model means for running agents in production.
Dumping the whole corpus into an AI agent makes it worse, not better. The fix is architectural: each task loads a curated slice, not everything you have. Here is the method, and the same move at three different layers: specs, sensor data…
A small ARM box that started as a local LLM experiment and ended up a self-governing node: private retrieval, a resident agent under a written constitution, a code-enforced safety fence, and a nightly job where it audits itself and files…
Anthropic published a guide on building a session-level orchestration mode. I built it two ways, on the CLI and on the API, and then hit the part the guide does not cover: an orchestrator that fans out is useless without a backlog of rea…
How I replaced manual CSV exports with a live Garmin data feed for my AI marathon coach: a scheduled unofficial-API poller, resilient session handling, and the design calls that keep training and recovery data fresh and trustworthy.
Two posts ago I bet that keeping my portfolio reviewer's engine deterministic and auditable was worth it. This is where that bet paid off: because the engine is replayable, I could run a simulated market crash through the real production…
A personal portfolio reviewer where the scoring is deterministic and the AI only narrates. The architecture that held up after I had to rewrite the model it was built on, and why that boundary is the whole point.
Two weeks after I shipped a post about a scoring engine I'd built, I rewrote the spec it was based on. Here's what I learned, and why I had an AI agent do the literature review.
The previous two posts made claims. Here is what a week of the workflow looks like as a data trail, PRs, deploys, CI runs, specs merged, pulled from GitHub.
Spec-driven development reads like a methodology for controlling AI agents. It isn't. It's a methodology for managing context across stateless sessions. The spec is the persistent memory.
Two production sites, a blog, and two personal AI projects, shipped this week from a phone. The chain is voice dictation into Perplexity Computer, a spec, then Claude Code on the web. The interaction model is the story.
How I built a personal AI coaching system for marathon training, layering deterministic guardrails over an LLM narrative engine, ingesting Garmin FIT files, and designing for my own injury history.
Why spec-driven development and structured folder architecture are the missing infrastructure for AI-assisted engineering: methodology, common mistakes, and where to start.
A practitioner's review of Doug Kerwin's Enterprise Vibe Coding Playbook, why AI as a thinking partner, not a replacement, is the framework enterprise engineering teams need.