Writing on security, systems, open-source projects, and working with generative AI.
An LLM is a stochastic sequence completion engine that is very good at sounding human. Working well with a coding harness means holding both of those truths at once, and using them deliberately.
Generative AI is intellectual property laundering. Where that is theft and where it is a contribution to the commons depends on the domain, so here is where I land on art, open source, contract work, and local models.
Scoped engagements across the stack — from low-level infrastructure to system architecture. If it touches production, it's in scope.
Build pipeline audits, dependency risk, provenance and signing, CI/CD hardening.
Architecture review, threat modeling, segmentation, and hardening for production networks.
Failure-mode and consensus analysis for large-scale, high-availability systems.
Novel or ambiguous problems — research-to-production work when there's no existing playbook.
Don't see your exact problem listed — ask anyway.
Ten years building secure, large-scale infrastructure at Google, Square, and RedSeal — with depth in distributed systems, software supply chain security, and network security.
Comfortable owning a problem end to end: from research question to something running in production. Demilich Digital is that same work, available directly.