What I'm focused on

Shipping

• HIPAA-compliant AI systemsat a stealth medtech startup in SF. Can't say more publicly — happy to talk in interviews.
• EEAAO — a VSCode dev companion with a personality engine, Obsidian vault memory, and open-source agent routing. Phase 0 skeleton done. Phase 1 (code module + live vault + mood states) is the current sprint.
• This portfolio — 30-day build-in-public arc. Every design decision documented and posted as a daily blog thread.

Reading

• Natural Language Autoencoders— Anthropic's paper on steering LLM representations. Wrote a deep-dive post on it.
• The Mom Test by Rob Fitzpatrick — re-reading it with the lens of "how do I validate EEAAO without building the wrong thing first."
• Anthropic's Constitutional AI papers and the broader alignment literature — keeping current with the field.

Thinking about

• Whether on-device AI (Gemma 4, Phi-4) is underrated for personal productivity apps — privacy by default, zero latency, no API costs.
• The evaluation gap in healthcare AI: most teams ship models without rigorous eval pipelines. HealthAdminBench is one attempt to close this. There are a dozen more benchmarks that need to exist.
• What "founding engineer" actually means at an applied AI startup — and which companies are building real infrastructure vs. GPT wrappers with good PR.

Looking for

Founding-engineer or ML engineering roles at applied AI startups, Series A–C, in SF or remote. I care most about the problem domain and the quality of the team. Healthcare AI is where I have depth, but I'm not limited to it — anywhere that rigorous evaluation and real-world deployment are first-class concerns.

Not doing

• Taking on freelance work — fully committed to the above
• Speaking at conferences (not this year)
• Social media that isn't directly build-in-public related