15 posts
An eight-minute vibe-coding challenge at a Mouse AI League mentor mixer, where I volunteer as a mentor and judge: an AI agent built a French flashcard app in eight minutes, and the speed of the machine is what makes product judgment the…
My kids collect Pokemon cards, and one of them asked what a Charizard is actually worth. Answering it turned into a weekend build: a chart that puts graded cards and the S&P 500 on one indexed axis, and admits out loud where its data is…
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 wrote a C++ options pricer to learn low-latency numerics. The first clean version priced fifteen million options a second; getting to 215 million was less about clever code and more about being wrong, in public with myself, about where…
A screensaver joke, every stock a fish, grew into a six-lens market board I leave running on a wall. It lives in one HTML file on purpose, its data is baked because the browser is not allowed to fetch it, and the feature that finally mad…
I built a self-healing RAG pipeline, a guardrails gateway, and an eval gate as one system, then threw a 44-question battery at it. Zero hallucinations, because the behavior that matters most is refusal. Here is how trust got built into t…
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…
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…
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.
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…
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.
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.