Nicheloom

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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.

Details

External ID
1375466726
Source
GITHUB
Company
—
Product
minojev
Website domain
github.io
Launched
Sept. 18, 2026
Cohort
—
Upvotes
25
Upvotes percentile
0.6742890084550346
Tags
—
Fetched at
Sept. 22, 2026, 5:02 p.m.
Updated at
Sept. 22, 2026, 5:02 p.m.

Enrichment

Theme
scientific computing and deep tech tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
fast probability distribution inference from hidden states for developers
Manually corrected
False

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 — 73 launches clear the similarity bar, closest 8 shown.

Attention rank: #36 of 74 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 314 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a model & infra tool for Fintech yet.