Think inside your AI world.
Is this milestone real, or did it just move?
Milestones have a quiet failure mode: when the date is at risk, the definition slides to meet it. The milestone gets marked complete, the date holds, and something that wasn’t really done is recorded as done. Only your ratified definition can tell you whether the milestone was met or merely relabelled.
A milestone is a promise about a state: when we reach here, this will be true. Under deadline pressure the promise erodes — “done” gets redefined downward until it fits the calendar. Unl holds the definition you set, so a read can say whether the milestone met your bar or slid to meet the date — and which of your conditions it skipped.
How does a milestone quietly move?
Not by anyone deciding to cheat. The date approaches, the full definition looks unreachable, and the scope of “done” contracts one reasonable-sounding step at a time until it fits. Each step is defensible; the sum is a milestone marked complete that wouldn’t have qualified under the definition you actually set.
The record then lies gently. The plan shows the milestone hit on time, and the gap between “hit” and “hit as defined” is invisible — until a later milestone that depended on the real thing being done runs into the part that was quietly dropped.
What tells you which happened?
The original definition, re-applied. Say you’re a solo game developer, whose bar is settled: a milestone is done only if the build is playable end-to-end, not merely code-complete. The plan marks the vertical-slice milestone complete. Against your definition, the slice compiles but isn’t playable through — so it moved; it wasn’t met.
A general-purpose AI reads the label, not the bar. It will report the milestone as complete because the plan says so; it can’t check “playable end-to-end,” because that’s your ratified condition, not a status field.
What does a measured milestone read return?
The honest verdict against the bar: “Marked complete, but not met by your definition — the build compiles but isn’t playable end-to-end, which is your condition.” That protects the milestones downstream that assumed this one was really done.
This is measured context guarding integrity: the label says complete, the verdict says met-or-moved, and you find out from your own definition rather than from the dependency that breaks three weeks later.
A milestone marked complete and a milestone that met its definition are different things; measured context checks the state against the bar you ratified, so you learn whether it was met or merely relabelled.
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
How do I know if a milestone was really met?
Re-apply the definition you set for it — “playable end-to-end,” not just “code-complete.” A milestone marked complete and one that met its bar are different things, and under deadline pressure “done” tends to slide to fit the date. Measured context holds your definition and checks the state against it, so you learn which happened.
Why do milestones slip without anyone noticing?
Because the definition of done contracts one reasonable step at a time until it fits the calendar, and each step is defensible. The plan then shows the milestone hit on time, hiding the gap between “hit” and “hit as defined” — until a later milestone that depended on the real thing runs into the part quietly dropped.
Can AI track my milestones honestly?
It can read the label, but not audit it — checking “met” needs your definition of done, which is a decision rather than a status field. Measured context supplies the definition so the read reports met-or-moved with the skipped condition named. 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.
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.