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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.

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GITHUB · launched Sep 19, 2026 · 24 upvotes (top 34%) · 0 views

Niche: machine learning developer tools · 303 launches, 11 in the last 7 days ▼ -45% · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Hobby / open-source project

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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 : 2260 launches clear the similarity bar, closest 8 shown.

Attention rank: #742 of 2261 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 324 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.