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snapjudge

Typed decisions (choice / score / yes-no) from local Qwen models on Apple Silicon. Probabilities come straight from the logits, no text generation. TypeSafe-compatible HTTP API, runs on MLX.

Details

External ID
1376283064
Source
GITHUB
Company
—
Product
snapjudge
Website domain
github.com
Launched
Sept. 18, 2026
Cohort
—
Upvotes
10
Upvotes percentile
0.28183448629259544
Tags
apple-silicon, classification, decision-making, fastapi, llm, llm-evaluation, local-llm, logits, mlx, qwen, structured-output, zero-shot-classification
Fetched at
Sept. 22, 2026, 1:02 a.m.
Updated at
Sept. 22, 2026, 1:02 a.m.

Enrichment

Theme
decision model runtimes and tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
logit-based typed decision server for local qwen models on apple silicon
Manually corrected
False

Could you build this?

Partial Exposing an HTTP API is trivial, but extracting and calibrating logit probabilities without token generation directly from local MLX transformer runtimes on Apple Silicon requires specialized ML systems knowledge.

What it would actually take: The system requires Python utilizing Apple's MLX framework and Hugging Face tokenizers/architectures to load Qwen models directly onto Unified Memory. The hard part is bypassing autoregressive sampling to perform exact single forward-pass forward passes over specific token IDs to evaluate softmax normalized logits into calibrated probability distributions. This requires machine learning systems engineering and deep familiarity with MLX computational graphs.

Competitors

Other products that read as similar to this one — 38 launches clear the similarity bar, closest 8 shown.

Attention rank: #35 of 39 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 2 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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