Enterprise AI adoption & deployment
I get AI out of the demo and into the work.
I lead enterprise AI rollouts: turning demos into approved, owned, measured workflows. 40 GenAI deployments, every one cleared through security.
How I built a local AI router, in 90 seconds.
My job is making AI systems make sense to the people who have to trust them. Here is one I built, explained simply.
Silent preview loop. Use Play with sound for the narrated Cerebellum explainer with captions.
Things I built and shipped.
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https://github.com/jbelnick/cerebellum-local-ai-router
Local AI Router
Routes lower-risk AI work to local models with policy controls and a reviewable decision trail.
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https://github.com/jbelnick/planbridge
PlanBridge
Local, read-only MCP connector that lets ChatGPT plan over an allowlisted workspace, then hands the frozen plan to Codex.
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https://github.com/jbelnick/meeting-intelligence-pipelines
Meeting Intelligence Pipelines
Turns sanitized call notes into reviewed follow-up, risk flags, and named owners.
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https://github.com/jbelnick/llm-judge-evals
Evaluation Harness
A golden-dataset judge that fails CI when output quality drifts.
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https://github.com/jbelnick/meeting-intelligence-mcp
Meeting Intelligence MCP Server
Read-only meeting tools exposed behind a stable, packaged boundary.
Recent writing.
Practical notes on AI adoption and local models. All writing ->
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The Mac mini that never swaps
Why the unattended jobs in my local-AI setup run on a dedicated always-on Mac mini instead of my 64GB Mac Studio, serving one small model that never gets evicted.
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I built a loop that learns from my editor
Part 2 of the local writer story: a self-improvement loop whose first act was rejecting its own idea, and why that rejection is the feature
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I trained a local model to write like Claude
I trained a 9B to write like Claude, benched it blind, and the ending flipped: the bigger model with the distilled skill took the seat