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67
Fair
The public score: the same for everyone. Members also see how well it fits them.
What you could build
The offerA consulting service that recommends the right local LLM model and hardware, and provides a step‑by‑step fine‑tuning guide for the startup’s specific tasks.
Who paysStartup founders and engineers who want to run AI locally to control costs and data privacy.
First versionA one‑page questionnaire to capture the startup’s workload, followed by a personalized recommendation email and a sample fine‑tuning script delivered manually.
One way to serve the need, not the only one.
Why it scores 67
Demand47Five asked in thirty days; reading on the subject down 16% on the month.
Timing76Three this month against two in the two before.
Market gap65Not measured yet: we have seen nothing written on it, and we read a narrow slice of what is sold.
Evidence84Two independently run sources.
We show real demand. We never estimate what you'll earn.
PotentialModestPotential describes the market we observed. It is not a prediction of what you will earn.
How we know
Five people asked for this in the last thirty days.
They came from two independently run sources, not one list.
Every fact above comes from something we observed and kept, never from an estimate. How Colaunched finds ideas
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What we don't know
Whether anyone will pay for it. No signal can tell us that. The first gate in a launch is there to find out.