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openJev-verdict-2.0

Calibrated 151M Non-Autoregressive Decision Engine beating TypeSafe Jev & Laya on LocalLLaMA/typed-decisions (77.10% acc, 0.0636 Brier, 0.0144 ECE)

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GITHUB · launched Sep 19, 2026 · 276 upvotes (top 3%) · 3 views

Niche: large language models · 191 launches, 5 in the last 7 days ▼ -29% · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Hobby / open-source project
  • agentic-ai
  • brier-score
  • calibration
  • decision-engine
  • deep-learning
  • encoder
  • +14 more

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Could you build this?

No This project is a custom-trained, non-autoregressive neural network decision model requiring specialized ML research, architecture design, and specific calibrated benchmark training.

What it would actually take: Building this requires designing a custom 151M-parameter non-autoregressive transformer or BERT-style architecture, curating a specialized dataset of typed decision trees, and training it using PyTorch with calibration loss functions (temperature scaling, Brier score optimization). This requires deep machine learning research expertise, GPU compute clusters, and non-autoregressive decoding theory.

Competitors

Other products that read as similar to this one : 896 launches clear the similarity bar, closest 8 shown.

Attention rank: #44 of 897 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 320 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.