Pick one option
Select from a known set and return probabilities with confidence.
Discover 11 new open-source Jev projects added this week to bestjev, with verified GitHub repositories, languages, stars, and implementation details.
Open bilingual mmBERT-based 144.3M decision model returning Choice, Noul, and 2–10 Score answers with probabilities through a separate local PyTorch runtime.
Open 4B and 35B-A3B video decision models that share a video prefix across questions and directly score temporal-grounding intervals and multiple-choice answers without text generation.
Local SQLite BM25 retrieval pipeline where Jev answers batched Noul evidence-relevance questions to rerank passages before a separate model generates an answer; selected text is sent to hosted providers.
Open clients, evaluation tools, and runnable text/image/video decision demos for the hosted System One-compatible Trio-Spark service; model weights and training code are closed.
Pilot research monitor where Jev uses typed Noul and Score judgments to keep, review, or drop new papers and repositories against open research questions, while another model writes daily notes.
Batch and streaming log-analysis application where Jev or Laya answers configurable typed questions about events, with validated numeric results and optional Python actions.
Coding-agent plugins where Jev or a compatible System One model judges shell risks, task complexity, context retention, and tool exposure, with capabilities varying by host agent.
Python client for typed Choice, Score, and Noul decisions across Jev, OpenRouter, OpenAI Decisions, and local models, with confidence-based fallback and a System One-compatible server.
VisiData plugin that sends each text-column row to Jev or another OpenRouter decision model and adds typed labels, scores, probabilities, and confidence as columns.
Experimental pre-action gate that applies deterministic rules before asking Jev or a local compatible model whether an unresolved tool action should proceed or require confirmation.
Reproducible guardrail benchmark that compares Jev and other decision systems using typed Noul checks and a fixed 0.5 blocking threshold, with public scoring contracts and author-run results.
bestjev is an independent, community-maintained directory of open-source projects built around Jev, TypeSafe AI's System One model for typed decisions.
Each listing links to its original repository and records its category, primary language, GitHub star snapshot, and a localized description, helping developers find verifiable Jev SDKs, agents, integrations, applications, research, and tools.
bestjev is not affiliated with or endorsed by TypeSafe AI.
Jev is TypeSafe AI's System One model for decisions that software needs to consume. Instead of generating free-form prose, it accepts a state and one or more typed questions, then returns structured answers with calibrated probabilities.
It fits workflows where the option space or scoring rubric is known in advance: routing an agent, validating a tool call, ranking candidates, checking a policy, or deciding whether an action should proceed.
Applications can set deterministic thresholds, inspect uncertainty, and route low-confidence cases to a person or a larger model.
Select from a known set and return probabilities with confidence.
Place an input on an ordered scale with a full distribution.
Return the probability that a statement is true, from zero to one.
Each entry is checked against public evidence: the upstream repository, project-authored documentation, and visible Jev usage. Stars are a dated discovery snapshot, not a quality ranking.
“Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.”— TypeSafe AI, introducing Jev
Know a public project that uses Jev? Send us its repository and the evidence needed to verify it. Submissions are reviewed before they appear in the catalog.
Submit a projectOpens a pre-filled GitHub issue.Provide the public GitHub URL and a concise description of what the project does.
Link to code, documentation, a demo, or another public source that makes the Jev integration verifiable.
We verify the evidence, choose the right category, and add accepted projects in a future catalog update.