I built a proxy that learns the small decisions my app asks an LLM for, so it stops paying for them
github.com · 1 day on the radar
Where is the demand coming from?
Mixed
Some genuine outside demand, some founder reach. This is the read that decides whether the traction would transfer to you.
Roughly what does it earn?
Not disclosedNo basis to estimateWe do not publish a figure until we have something to base it on. When we do, the method that produced it is shown alongside the range.
Could you build it?
Yes, in about 2 weeks
Nothing patented, no network effect, and no proprietary data set standing in the way.
What it is
Stuntd is a self-hosted proxy that intercepts LLM API calls, learns the patterns in small typed decisions (routing, triage, moderation), and serves them locally from a distilled model head instead of calling the provider repeatedly. Early-release tool targeting developers who want to reduce LLM token costs for routine categorization tasks.
The one thing to knowThis is a clever technical implementation for a real cost problem, but the market is narrow (typed decisions only, requires code instrumentation), monetization is absent, and distribution is purely GitHub stars with zero evidence of founder reach or organic pull.
Proof we found, with sources
The scores behind the verdict
Directional reads from public signals, 0 to 100▸ Show the four scores
TractionWeak8 / 100
How much demand shows up from people outside the founder's own circle.
CopyabilityEasy to match75 / 100
How feasible it is for a competent builder to ship something comparable.
Wedge potentialFair62 / 100
How much room is left for a new entrant: gaps, ignored segments, pricing openings.
MonetizationUnproven5 / 100
How much evidence there is that people actually pay: visible pricing, revenue claims, a paid tier in use.
Unlock the full brief
How it works under the hood, how it gets customers, where it is weak, and the opening we would take, with a bottom-line verdict.
Sign in to unlockOne-time payment. Delivered in about a minute. Yours to keep.
Revenue, user and tactic numbers are evidence we found and attributed, with a confidence rating.
They are directional reads, never verified guarantees.