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opendecider

Open, calibrated System 1 decision models: typed choice / score / yes-no answers with a probability for every option. 400M to 80B; CPU, NVIDIA, Apple Silicon, LM Studio, Ollama and vLLM.

Details

External ID
1390390392
Source
GITHUB
Company
—
Product
opendecider
Website domain
github.io
Launched
Sept. 27, 2026
Cohort
—
Upvotes
13
Upvotes percentile
0.4260184473481937
Tags
apple-silicon, calibration, classification, decision-model, gguf, huggingface, llm, mlx, ollama, pytorch, system-one, typed-decisions, vllm
Fetched at
Oct. 1, 2026, 1:02 a.m.
Updated at
Oct. 1, 2026, 1:02 a.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 decision models for typed choice and scoring queries
Manually corrected
False

Could you build this?

No This is a custom-trained/fine-tuned 400M parameter ModernBERT-based decision model trained on calibrated decision benchmarks across multiple datasets, requiring ML research and model training pipelines.

What it would actually take: Building this requires curating and labeling a domain-specific dataset for typed decisions and calibrated probabilities. The architecture uses a ModernBERT backbone modified with task-specific classification/scoring heads, trained using PyTorch with calibration loss functions (e.g., temperature scaling or isotonic regression). Significant GPU compute and deep expertise in statistical model calibration and NLP are required.

Competitors

Other products that read as similar to this one — 76 launches clear the similarity bar, closest 8 shown.

Attention rank: #58 of 77 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 218 days after the earliest competitor.

Other launches for this product

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