Think inside your AI world.
Is my OKR on track, or just marked green?
Green is a label someone applied; on-track is a fact about pace. They usually agree and sometimes don’t, and the gap between them is where goals fail unnoticed. Through Unl the read checks the green against the pace you set, so you learn which kind of green you actually have.
A status colour is only as honest as the judgement behind it, and that judgement is often a hopeful glance. The reliable test is the number against your pace. Hold the pace in Unl and the read confirms or corrects the green, so a label never stands in for a verdict.
Green is a claim, not a measurement
When someone marks a key result green, they are asserting on-track — but the assertion rests on the same absent pace that makes check-ins theatre. A green applied by feel can sit on a goal that is quietly behind, because nobody compared the number to the rate it needs.
So green is a claim awaiting verification. Most of the time it is true; the danger is the minority where it is optimistic, because those are exactly the goals that fail without warning.
Checking the colour against the pace
Say your key result — trial-to-paid conversion at or above eight per cent — is marked green. The read checks it against your ratified pace and returns: green is optimistic — conversion is at six point four, below the eight you need, and the trend is flat, so this is behind, not on track. The label said one thing; the number says another.
Elsewhere the read confirms the green cleanly — a key result marked green and genuinely ahead of pace comes back verified. Either way, you know which of your greens are real and which were hope, instead of trusting the colour.
Labels that have to earn the colour
Because the pace lives in the read, a key result is green only when the numbers support it, and the read says so plainly when they don’t. The colour stops being a self-assigned comfort and becomes a claim the read either backs or corrects.
So “is it on track or just marked green?” gets a straight answer every time: the read tells you whether the green reflects the pace you set, so you spend your worry on the greens that are actually amber.
Green is a label someone applied; on-track is the number against the pace you set; through Unl the read checks the green against your pace and its reason, so it confirms the honest greens and corrects the optimistic ones — and a status colour never stands in for a verdict on a goal that is quietly behind.
Reads through Unl arrive with measured context — in the presence of the decisions you’ve already settled. The reach lane is live: 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.
Read further
Questions people ask
Is my OKR actually on track or just marked green?
Green is a claim someone applied; on-track is the number against the pace it needs. Through Unl the read checks the green against the pace you set, so it confirms the honest greens and corrects the optimistic ones — telling you which kind of green you actually have.
Why can an OKR be green but still behind?
Because the green was applied by feel, without comparing the number to the rate the goal needs — the same absent pace that makes check-ins theatre. A hopeful glance can mark a quietly-behind goal green, which is why those goals fail without warning.
How do I check if my green OKRs are real?
Read each against the pace you set. Through Unl a key result marked green is verified against its required run-rate, so the ones genuinely ahead come back confirmed and the ones that are optimistic come back corrected, with the gap and the reason.
What makes an OKR’s green label unreliable?
That someone applied it by hand and it is not re-checked against the pace. Through Unl the read tests the green against your required run-rate and its reason, so a green that has quietly fallen behind stops reading green.
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.