A situation we handle
When your AI memory surfaces the wrong things
It surfaces something — confidently. Related to your topic, adjacent to your point, and wrong for the turn. That's not bad luck: similarity finds what sounds like your topic, not what governs it.
How the wrong things get found
Resemblance is not relevance.
Most AI memory retrieves by similarity: your words go in, the nearest-sounding stored text comes back. It's a fine mechanism — for an archive. But sounding like your topic and bearing on your decision are different relations. The four brainstorms that resemble today's question outnumber the one call that settles it, so resemblance wins and the wrong thing arrives.
To be fair to the field: for an agent trawling its own history, similarity is often exactly right. The mismatch is the environment — a person mid-decision doesn't want the nearest-sounding text, they want the thing they concluded.
How human recall actually works
You recall by what you concluded, not by what resembles.
Ask yourself where a project stands and you don't get a cloud of similar conversations — you get the settled points: we chose this, we ruled that out, this is still open. Conclusions, with their reasons. That's the index your own mind keeps.
Unl keeps the same index: decisions with their workings, entered on your word, superseded when your thinking moves — and served as unprompted relevant context when one actually bears on the turn. When what's stored is only what was settled, what surfaces can only be settled things. Tuned for Claude, Claude Code, ChatGPT & Cursor at launch, extending across the AI ecosystem. Connects anywhere MCP does.
Serve the model your conclusions with their workings and relevance sorts itself — for you and for it. A reasoning model given settled points builds forward from them; given nearest-sounding text, it wanders where the resemblance leads.
Questions people ask
Why does my AI keep bringing up related-but-wrong context?
Because it retrieves by similarity: the nearest-sounding stored text wins. Resemblance and relevance are different relations — the brainstorm that sounds like today's question isn't the decision that governs it. A unit made of settled conclusions can't make that class of error.
Isn't better search the fix?
Sharper similarity is still similarity — it ranks resemblance more finely. The fix is what's stored: hold conclusions with their reasoning instead of everything that was said, and the retrieval problem mostly dissolves.
What exactly does Unl store?
The decisions you settle, each with its reasoning, plus what you deliberately ruled out — entered on your word, superseded when your thinking moves. Not transcripts, not scraped facts.
What does the model do differently with a conclusion?
It builds from it. Reasoning models work best from reasoning — a settled point with its why gives the model a floor to stand on, where nearest-sounding text gives it a scent to follow.
What this is
Think inside your AI world — you stay in command
Unlimitless (Unl to friends) holds what you've settled, reads what your tools are showing, and catches what's changed out in the world — and hands your AI whatever bears on the work, the moment it's needed, without you asking. The right thing, in front of the model, unprompted, with you in command of the call. So you keep moving toward what you set out to build, on top of everything you've already decided.
It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector. Quick to connect, in a couple of steps.
Unlimitless is open now to invited Alpha. Apply for the Beta waitlist to come in ahead of the full launch:
Alpha is invite-only · free at launch.