Think inside your AI world.
Should I fix this or ship that? Against my priority
A bug fix and a new feature are competing for the same week, and both have a case. Whether the fix wins isn’t a matter of taste — it’s a check against a rule the builder set for exactly this situation, one that most weeks never gets consulted.
Fix-or-ship isn’t a coin toss between two good options; it’s a rule applied to the state right now. Unl holds the reliability rule you ratified, so the question returns a verdict against today’s error rate, not against whichever task feels more finishable this week.
Why fix-or-ship keeps getting decided by feel
Both a fix and a new feature can be justified on their own terms — the fix reduces pain, the feature adds value — and comparing two justified things without a rule tends to default to whichever is more fun to build, or whichever was promised more recently.
That default is fine until it isn’t: a fix that keeps losing the coin toss against shinier work is a fix that quietly never gets scheduled, and the thing it was fixing keeps costing something every week it’s deferred.
Your actual rule
Say you run product for an infrastructure platform and have set the rule that decides this for you: reliability work outranks new surface whenever the error rate sits above your target, no exceptions for how appealing the new feature is. This week the error rate is over target, and a new dashboard page is also queued.
Measured against your own rule, the choice isn’t weighed fresh: “Fix first — error rate is over your target, and reliability outranks the new page by your rule.” The dashboard page wasn’t wrong to want. It simply doesn’t get to go first while the error rate sits where it is.
What the honest fix-or-ship question needs
A general-purpose model asked which to do first will weigh the two cases on their surface merits, because it has no access to your error-rate threshold or a live read of where the current rate actually sits against it.
Measured context checks the current error rate against your target and applies your reliability-first rule automatically, so the fix-or-ship question returns a verdict tied to today’s state rather than to whichever task happened to feel more urgent in the standup.
Fix-or-ship only has an honest answer against a rule tied to the current state, not a feeling about which task is more appealing; measured context checks today’s error rate against the PM’s reliability threshold and applies the rule automatically.
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 decide whether to fix a bug or ship a new feature first?
Check the current state against a rule you’ve set for exactly this trade-off, not against which task feels more appealing this week. Say you’re an infra PM; reliability work always outranks new surface once the error rate sits above your target — a rule tied to today’s numbers, not a mood.
Why would a small fix beat a bigger feature in priority?
Because the rule that decides fix-or-ship isn’t about size, it’s about a threshold — for one PM, reliability wins whenever the error rate is over target, regardless of how much smaller the fix looks next to the feature. Only the current number checked against that threshold tells you which one actually goes first.
Can AI tell me whether to fix something or ship a new feature?
It can compare the two cases on their surface merits, but it can’t apply your error-rate threshold, because that number is a decision you made about your own platform, not a fact it can read. Measured context checks the current rate against your threshold automatically. 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.
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