Think inside your AI world.
The feature-cut decision, through Unl
Deciding to remove a shipped feature is harder than deciding not to build one, because something already exists and someone will notice it’s gone. That asymmetry keeps features alive long after the number that would justify cutting them has already made the case.
A feature earns its keep by usage, not by having once been worth shipping. Unl holds the keep line you ratified — under 5% usage at 90 days, cut — so a read can name which shipped features have quietly fallen under it, rather than leaving the question to whoever notices first.
Why shipped features are harder to remove than unshipped ones
A feature that was never built has no one advocating for it once it’s cut from a list. A feature that’s already shipped has users, however few, and removing it means an actual, visible change someone might complain about — which makes cutting it feel riskier than leaving it quietly unused.
That asymmetry means low-usage features tend to survive by default, not because anyone decided they were worth keeping, but because nobody wanted to be the one who removed something and had to answer for it.
Your actual line
Say you run product for an analytics dashboard tool and have fixed your keep line precisely: a feature gets cut if usage sits under 5% at the 90-day mark, no sentimentality for how good the original idea was. A custom-alert feature shipped three months ago is sitting at 3% usage.
Measured against your own line, the cut decision doesn’t need debating: “Cut it — 3% usage after 90 days, under your 5% keep line.” The feature wasn’t badly built. It simply never found the audience the keep line requires to justify its upkeep.
What the cut decision becomes
A usage dashboard can show the 3% figure clearly enough, but it can’t independently apply your specific 5%-at-90-days rule and flag the feature as a cut candidate, because that threshold and the timeframe attached to it are your own decision, not a default the dashboard ships with.
Measured context checks every shipped feature against your line automatically, so a feature drifting under 5% gets named the moment it crosses, and the cut decision stops depending on someone happening to notice the usage chart and remembering to raise it.
A shipped feature survives by default once nobody wants to be the person who removes it, unless usage is checked against a fixed keep line; through Unl the PM’s own threshold is applied automatically, so a feature under the line is named as a cut candidate without anyone having to notice first.
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 cut a feature that’s already shipped?
Check its usage against a keep line you’ve fixed in advance, not against how good the original idea was. For one analytics-tool PM that line is explicit: under 5% usage at 90 days, cut it. A feature can be well-built and still fail to find the audience the line requires.
Why do low-usage features stay shipped for so long?
Because removing something that already exists feels riskier than never building it — there are users, however few, and someone might notice it’s gone. Features survive by default, not because anyone decided they were worth keeping, unless a fixed usage line forces the question to actually get asked.
Can AI tell me which of my features to cut?
It can show the usage numbers, but it can’t apply your specific keep line and timeframe automatically, because that threshold is your own decision about your own product, not a default the dashboard carries. Measured context checks every feature against your line and flags the ones that cross it. 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.
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