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jev-benchmarks

Probability-aware evaluation for typed decision models: calibration, selective risk, latency, and reproducible benchmarks.

Details

External ID
1374064344
Source
GITHUB
Company
—
Product
jev-rag-benchmark
Website domain
github.com
Launched
Sept. 17, 2026
Cohort
—
Upvotes
15
Upvotes percentile
0.4914168588265437
Tags
benchmarking, calibration, evaluation, machine-learning, reproducibility, selective-classification, zero-shot-classification
Fetched at
Sept. 21, 2026, 5:02 p.m.
Updated at
Sept. 21, 2026, 5:02 p.m.

Enrichment

Theme
decision model runtimes and tools
Vertical
Horizontal
Function
Observability & eval
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
probability-aware evaluation benchmarks for typed decision models
Manually corrected
False

Could you build this?

Yes Evaluation benchmarks for probability calibration, selective risk, and latency are standard statistical routines easily implemented in Python with NumPy and SciPy.

Competitors

Other products that read as similar to this one — 1235 launches clear the similarity bar, closest 8 shown.

Attention rank: #603 of 1236 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 323 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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