Contributing to local AI
Small changes.
Public evidence.
Colibri is part of my continuing work on making local AI more useful. My contributions include focused fixes and tests submitted for upstream review.
Selected examples
Work you can inspect.
Each example links to its public proposal. Expand it for the dated verification context.
Release dashboard hit tracking
A focused fix concerning GLM dashboard hit tracking.
Inspect the public pull request ↗What was checked on September 12, 2026?
The saved official GitHub API check recorded this proposal open and ready for review, with 25/25 checks successful. That is a dated snapshot, not a claim about its status today or a measured performance improvement.
Exact public title: “fix(glm53): release dashboard hit tracking”.
Preserve explicit Windows expert budgets
A fix intended to preserve explicit expert-memory budgets on Windows.
Inspect the public pull request ↗What was checked on September 12, 2026?
The saved official GitHub API check recorded this proposal open and ready for review, with 26/26 checks successful. That is a dated snapshot, not a claim about its status today or a measured performance improvement.
Exact public title: “fix(coli): preserve explicit Windows expert budgets”.
Make planner fixtures sparse
A test change that avoids materializing zero-filled shard payloads in resource-planner fixtures.
Inspect the public pull request ↗What was checked on September 12, 2026?
The saved official GitHub API check recorded this proposal open and ready for review, with 27/27 checks successful. That is a dated snapshot, not a claim about its status today or a measured performance improvement.
Exact public title: “test(planner): avoid materializing zero-filled shard payloads”.
Evidence date: September 12, 2026, 16:36–16:38 UTC. These examples come from an official GitHub API snapshot. Current status may differ. Passing checks do not establish merge approval, production readiness or performance on another machine.
Why I contribute
Work on the details.
Share what can be checked.
Making a system practical involves more than selecting a model. Resource settings, platform differences, tests and understandable evidence all matter.
I want the public work to show both useful changes and honest limits. Maintainer review and further testing remain part of that process.
Explore the other public work →