Shrewd – what I learned distilling LLM labels into local classifiers
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Not disclosedNo basis to estimateWe do not publish a figure until we have something to base it on.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 8 days
Nothing patented, no network effect, and no proprietary data set standing in the way.
What it is
Shrewd is an open-source Python library that distills LLM judgments into lightweight local text classifiers for single fixed tasks. The project packages a research pipeline showing how to use prompt optimization and selective labeling to build fast, cost-efficient models that run without API calls, with four prebuilt demo classifiers (spam detection, email triage, guardrails, data gates) and published benchmarks.
The one thing to knowThis is early-stage research code with minimal adoption signals (3 HN points, no GitHub stars yet), and while the technical approach is sound and the buildable footprint is small, there is no monetization path, paying user base, or evidence of founder distribution reach to suggest immediate wedge potential.
Proof we found, with sources
The scores behind the verdict
Directional reads from public signals, 0 to 100▸ Show the four scores
TractionWeak12 / 100
How much demand shows up from people outside the founder's own circle.
CopyabilityEasy to match72 / 100
How feasible it is for a competent builder to ship something comparable.
Wedge potentialFair35 / 100
How much room is left for a new entrant: gaps, ignored segments, pricing openings.
MonetizationUnproven0 / 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.
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Revenue, user and tactic numbers are evidence we found and attributed, with a confidence rating.
They are directional reads, never verified guarantees.