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Bud-Decision-Engine

BudEcosystem/Bud-Decision-Engine · Homepage

Run open decision models locally, get calibrated probabilities.

Automated README summary

Automatically summarized from the README on 2 Oct 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

Bud Decision Studio is a cross-platform desktop app and serving system for Jev-like "System One" decision models. It lets anyone run open decision models on their own computer, giving a situation and typed questions to get calibrated probabilities for every answer in milliseconds.

How it uses Jev

Jev-like models take a situation and typed questions (Pick one, Rate, Yes/no, Pick any, Put in order, Estimate) and return a probability per answer. The studio uses these probabilities to decide if an answer is sure enough to act on, based on a template's threshold.

Primitives:choicescorenoul

Technique worth stealing

Use typed questions with a state to get calibrated probabilities for decisions, enabling automatic actions when certainty exceeds a threshold.

Try it

Install via one-line command or download from releases, then open the app and choose models to download.

View on GitHub

Start using Jev: API key and your first request →

Model judgments from README excerpts

Project signals use repository metadata, an existing summary and the first 700 README characters, not the full project. “Not found” means not detected in that material; it does not establish absence. These are the original judgments with recorded models and dates, not source audits or run results.

Models & evaluation dates

Inclusion gate
jev-1.13.0
Project signals
jev-1.13.0
Category
jev-1.13.0
Pattern tags
jev-1.13.0

Evidence

These signals use repository metadata, an existing summary and at most 700 cleaned README characters. ≥ 0.60 means reported in that excerpt; ≤ 0.40 means not found there. The middle band is inconclusive. None is a source-code audit or a test result. The runnable-tool question excludes research experiments; the recommendation question asks for a learning shortlist, not a quality verdict.

  • Jev is core to the projectFound in material0.85
  • Shows a System One patternunclear0.40
  • Describes handling uncertaintynot found in the excerpt0.04
  • Reports measured numbersnot found in the excerpt0.06
  • Is a runnable toolFound in material0.98
  • Top-10 learning shortlist signalnot found in the excerpt0.20
  • Is a replica of the modelFound in material0.73
  • Solves a common problem (0–2)score on a 0–2 scale1.00 / 2
  • About JevFound in material0.88
Signals
Jev is core to the project0.85Shows a System One pattern0.40Describes handling uncertainty0.04Reports measured numbers0.06Is a runnable tool0.98Top-10 learning shortlist signal0.20Is a replica of the model0.7300.51Solves a common problem (0–2)1.00 / 2012

no-judgment band (0.40–0.60)

Design patterns
Questionnaire0.94Check-up0.83Flight recorder0.63Funnel0.2000.51

no-judgment band (0.40–0.60)

The Jev model and date for each judgment stage are listed above. Card written by DeepSeek V4.1 Flash from the README on 2 Oct 2026.