14 posts
My market tracker now listens to ten different crowds: message boards, raw forum archives, search trends, encyclopedia traffic, insider filings, video chatter, news tone. The interesting design decision is not the collection. It is the w…
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 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…
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.