Writing
24 posts
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…
The Knicks won their first title since 1973, decades before I was born. A lifelong fan on the grandfather who handed down the wait, the lean years that taught patience, and the roster I half-built in my head a decade before it came true.
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.
A backtest's job is not to find an edge. It is to stop you from believing in one that is not there. The toolkit I used to test my own trading engine, and the part where it killed my single best signal.
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…
Why good validation reports every problem at once instead of failing on the first one, and how to build the accumulator, phasing, and structured errors that make it work.
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 high-level tour of the technologies running this site: Next.js on CloudFront, Python Lambdas behind API Gateway, DynamoDB plus S3, Anthropic's API with a Bedrock fallback, and AWS CDK wiring it together.
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.
Patterns for pre-execution safety checks, parallel validation, opt-out design, and extensible guardrail architecture on workflow platforms.
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.
An introduction to the blog, reflections on infrastructure monitoring, platform leadership, and building systems that empower organizations to innovate safely at scale.