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jev-forge

An open training and inference stack for Jev-style decision models. Train models to score dynamic candidate branches from a shared prefix, with support for high-cardinality choice, calibration, and fast batched inference.

Details

External ID
1376779168
Source
GITHUB
Company
—
Product
jev-forge
Website domain
vercel.app
Launched
Sept. 19, 2026
Cohort
—
Upvotes
24
Upvotes percentile
0.6626313092492954
Tags
—
Fetched at
Sept. 23, 2026, 5:02 p.m.
Updated at
Sept. 23, 2026, 5:02 p.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
open training and inference framework for branch-scoring decision models
Manually corrected
False

Could you build this?

No Building an end-to-end training and inference engine for prefix-shared decision models requires novel machine learning research, custom kernel optimization, and high-performance systems engineering.

What it would actually take: The stack requires a custom training pipeline using PyTorch/Triton alongside a C++ inference engine that implements custom CUDA kernels for dynamic prefix branch sharing. The core challenges involve designing calibrated loss functions for high-cardinality action candidates and writing specialized attention kernels to share KV-cache prefixes across dynamic branches during batched evaluation. This demands senior ML research scientists and CUDA systems performance engineers.

Competitors

Other products that read as similar to this one — 983 launches clear the similarity bar, closest 8 shown.

Attention rank: #348 of 984 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 324 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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