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 missing 90 percent of AI adoption
73% of surveyed enterprises use AI but only 10% run on it. The gap is not model quality. It is ownership, sign-off paths, and measured workflows
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The open-weight frontier arrived in June
In one June window, open weights reached the coding frontier. What I'd build on, what I'd admire from a distance, and which numbers to trust
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Local LLMs on a 64GB Mac Studio
The awkward middle tier of local AI in mid-2026, the capacity and bandwidth math that governs it, and the honest 75 percent verdict