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minojev

Decisions, not tokens: minojev reads calibrated, typed probability distributions straight from hidden states in one forward pass — zero output tokens, fully reproducible on a laptop CPU.

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GITHUB · launched Sep 18, 2026 · 25 upvotes (top 32%) · 0 views

Niche: machine learning inference engines · 108 launches, 8 in the last 7 days ▼ -27% · Explore this niche →

Category and tags

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

Visit website ↗ Source listing ↗

Could you build this?

No It involves novel mechanistic interpretability research and specialized model probing to extract calibrated probability distributions directly from internal LLM hidden states in a single forward pass without token generation.

What it would actually take: Building minojev requires deep expertise in machine learning interpretability, linear representation probing, and calibration theory. The stack would involve PyTorch or ONNX runtime with custom linear probe heads or probing vectors trained on internal activations of open-weight models (e.g., Llama/Mistral) along with Platt scaling or isotonic regression for calibration. The core challenge is discovering or learning the geometric directions in residual stream layers that reliably represent truthfulness and calibrated probabilities across diverse inputs.

Competitors

Other products that read as similar to this one : 221 launches clear the similarity bar, closest 8 shown.

Attention rank: #79 of 222 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 314 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.