13 posts
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.