AI looks impressive until the work spans more than one session, one model, or one operator. Then the hidden costs show up: repeated briefings, broken continuity, and context trapped in transcripts.
That failure mode is not a minor UX bug. It is the reason many AI workflows stay shallow, fragile, or too brittle to trust in real operating work.
We built HOM Local to solve that layer directly: keep the project brain intact, bring recall back with evidence, and let the user own it even when models change.
We care less about making AI look magical and more about making it operational: remembered context, inspectable recall, and continuity that survive actual work.
HOM should preserve the decisions, constraints, and evidence that change outcomes, not hoard noise for its own sake.
HOM Local starts on your machine. The memory stays portable, model-agnostic, and outside someone else's moat by default.
If the brain improves over time, the operator should still be able to inspect confidence, evidence, drift, and recall paths.