01 About
I patented an athletic recovery product at seventeen and shipped 600 units before I finished high school. That taught me the only lesson that transfers: the thing is not real until someone who owes you nothing uses it.
Right now I’m building Telos — AI candidate sourcing for IT staffing agencies. Most matching tools rank résumés by keyword overlap with a job description, which is a search problem pretending to be a hiring problem. Telos scores candidates against placement history: who actually got hired, for what, and what the recruiter said about them. My dad’s firm Flexton is the first design partner, which means my feedback loop is a recruiter telling me the ranking is wrong and why.
Before that: Vibecheck, which scans a repo with real static analysis and opens a PR that fixes what it finds; Strata, field guidance for plumbing contractors that has to keep working in a crawl space with no signal; and pipecat-firewall on PyPI, a drop-in guard that blocks prompt injection before a voice agent’s model ever sees the turn.
The through-line is systems that have to be right about people — matching them to work, remembering their lives, refusing to be talked into betraying them. In all three the interesting engineering is the failure mode, not the demo.
That is also why every project here ships with what is wrong with it. A portfolio of things that only worked is not evidence of judgement. Most of these pages end with the part I could not solve, and seven of them are dead.
Outside the terminal: competitive breaker, qualified for nationals. Guitar on Monday nights at Pomona. Currently training for a half marathon, badly.
02 Experience
03 Selected work
04 Stack
Languages
Python, TypeScript, SQL. Python for anything with data or a model in it; TypeScript when a person has to look at it.
Backend
FastAPI for services, Next.js when the front end and API belong together, Supabase/Postgres for state.
AI
Claude and GPT-4o, Pinecone for retrieval, Pipecat + LiveKit for voice. Deterministic code wherever a model is not actually required.
Quant
pandas, statsmodels, scikit-learn. Enough econometrics to know when a p-value is load-bearing and when it is decoration.
Infra
Vercel, Railway, Docker, GitHub Actions. Boring on purpose — deployment should not be the interesting part.
Reach for first
The smallest thing that can be put in front of a real user this week. Everything I have learned came from that, not from the architecture diagram.
05 Interests
- AI agents under adversarial pressure
- labor markets
- calibration & forecasting
- prediction markets
- breaking
- guitar
- running
- climbing
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