I build and debug the infrastructure that agents run on. Most of my recent work is on agent evaluation harnesses — the layer between a model and a task where trials fail for reasons that have nothing to do with the model, and where a bad harness quietly turns an infrastructure timeout into an "agent failure" in your results table.
Before that: backend engineering on distributed systems, and a degree in financial mathematics. Both turn out to be the right training for this. Evals are a distributed systems problem wearing an ML costume — sandboxes, retries, streaming, idempotency, partial failure — and deciding what a noisy score actually tells you is a statistics problem.
- Recursive self improvement
- Agent safety and evaluation harnesses
- Training infrastructure
- Optimizer comparison for tool-using agents
- world-models — chess and Go distilled from Stockfish and KataGo on a single GPU: 2,301 Elo chess (95% CI [2,190, 2,601]).
- rl-playbook — rlplaybook.com, a visual timeline of deep RL's landmark papers from DQN forward
- rl / tabular-rl — RL agents with nothing abstracted away, from tabular methods up through PPO
- cassandra-playground — Cassandra-style anti-entropy repair in Go: gossip protocol with Merkle trees
- Project-Nash — Nash equilibria, Lemke–Howson, minimax, simplex; the game theory I keep reaching for in multi-agent settings
Distributed systems and infrastructure — gossip protocols with Merkle trees, Temporal.io workflows, Terraform on AWS. Quantitative finance — portfolio optimization, stochastic programming, derivative pricing. Python, Go, Rust, TypeScript, Java.