HOM Local gives AI an embedded persistent brain: decisions, evidence, and constraints that let real work continue across sessions, tools, and provider changes.
HOM is built to keep the signal behind the work, not shovel every scrap of text into storage and pretend retrieval alone will fix the mess later.
HOM returns evidence with confidence so you can tell the difference between grounded context, weak support, and a guess that should not drive the next move.
Useful memories rise, weak signals recede, and repeated work turns into a sharper brain over time. The result should be more leverage, not more clutter.
Projects, sessions, tools, skills, and agents work from one remembered context, which means the system can continue the job without rebuilding the world on every handoff.
Switch from Claude to GPT to Gemini to Ollama without losing the project brain. Model choice stays flexible because memory is not trapped inside one provider.
HOM can consolidate, detect drift, and improve recall paths without hiding the mechanism. Better memory should feel inspectable, not mystical.
HOM Local is the default starting point: private, personal, and fast to prove. Hosted or team memory can come later when coordination becomes the constraint.
The fastest path to proving whether persistent memory improves continuity, quality, and usefulness in your actual workflow.
Planned for teams that need shared memory, hosted access, and governed collaboration after the single-user workflow is already proven.
Same memory contract. Start local first. Add hosted coordination when the workflow truly needs it.
Bring local models, cloud models, coding tools, skills, and agent workflows into one remembered workspace.
The difference is practical, not cosmetic: less repeated setup, fewer broken handoffs, and a system that can keep useful context alive as work evolves.