Think inside your AI world.
A study, against a recommendation you give
The literature — the journals and databases you already subscribe to — publishes far faster than anyone can read: thousands of papers a week, across fields you will never touch. What you actually need is the one study that bears on a recommendation you give — the advice you stand behind — and that is a line the feed cannot hold, because the recommendation is yours.
Read the literature through Unl — the paid journals you license included, since the sources you already trust are the better ones — and it arrives measured against the recommendations you settled, so instead of scanning the whole stream you get the single study that bears on advice you give, with why it crosses. The one, not the thousand.
What the published literature publishes
The literature is a genuine public good, and there is a lot of it:
- Thousands of papers a week across every field, method and journal
- Every result at once, the practice-changing one beside the thousand that are not
- Disciplines and topics, almost none of which touch what you advise on
- No idea which study bears on a recommendation you give — that context is not in the abstract
The line the feed can't hold
Say you give a recommendation and named what would change it: I advise this approach because the evidence favours it; if a well-powered study finds against it on the outcome that matters, the recommendation I stand behind has to move. That threshold is a decision you made; it is not a field the database models.
The reason the line is yours is the whole point. A literature alert can match keywords; it cannot know the recommendation you actually give, or the finding you decided would change it, or that a small underpowered paper is noise to you while a well-designed study landing against the outcome you advise on is not.
The frame judges the data it is given; it does not verify the source’s accuracy.
The one that crosses
So when a study lands against the exact recommendation you give, on the outcome you said would change it, it crosses — and the other thousand papers that week do not. Through Unl the answer arrives as a verdict: a well-powered study now finds against the approach you recommend, on the outcome you said would change your advice, so the recommendation you give needs revisiting — here is the paper, read against your guidance. Same public literature; a decision instead of a digest.
You didn't ask — it was already there
You did not ask for it. The recommendation was already recorded, so the next time you open Claude the crossing is already in the window — read against what you said would change your advice, with the reason it cleared it — rather than waiting for you to keep up with the journals. You author the recommendation once; the read applies it every time the evidence moves.
The answer comes back measured against what you already decided, and why.
The lane is live and open to this tool today: one box, paste anything. If it speaks MCP, Unl can reach it. Readings arrive unprompted, the data beside the criterion; Unl is a courier, not a warehouse, and keeps only your keys and the frame.
Questions people ask
How is this different from a journal alert or a literature feed?
An alert fires on the source’s rule — a keyword, a journal, a topic. This fires on YOUR rule: the specific recommendation you give, the finding you decided would change it, with the reasoning attached. The feed shows you every paper; the measured read shows you the one that bears on advice you actually give, and why.
Do I get pinged the moment a paper publishes?
The capability is that the crossing arrives measured against your recommendation the next time you are working — it is already in the window, read against what would change your advice, rather than a stream you monitor. A scheduled push is a separate delivery; what the read guarantees is that when a study bears on a recommendation you give, it surfaces as a verdict against your guidance, not as one more paper.
Does Unl change anything in the journals or in my practice?
Unl reads through the published literature, the journals you license included, and can write back on your explicit gesture — it never acts as a side effect of a read. The study is a published fact; the recommendation you give lives in Unl; the read joins them and returns a verdict, altering neither.
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.