// Advisory · work with me
Find the signal in the AI noise.
Most AI advice comes from people who have never had to make it run. I have. By day I architect enterprise platforms; by night I run a local-first AI operating system on hardware I own: a real cluster, serving real models to real applications. I have made the expensive mistakes already, so you do not have to.
01
Cut through the hype
What AI can actually do for your work this quarter, and what is still a demo. Honest answers, mapped to your situation, not a vendor pitch.
Strategy
02
Own vs. rent
When local-first beats the cloud and when it does not. The privacy, cost, and control calculus, worked against what you actually need.
Build vs. buy
03
Make it real
Architecture for models, memory, and agentic tooling that does real work. Built lean, designed to run on the hardware you already have.
Architecture
04
Keep your data yours
For anything sensitive or regulated, local-first is not a feature, it is the whole point. Context and inference that never leave hardware you control.
Privacy-first
// The rule
The same one the whole lab runs on: lean, efficient, no bloat, runs on anything. If owning your models, your memory, and your tools sounds like the right bet for what you are building, start a conversation.