yibie maintains a curated awesome list of over 300 public projects built on Jev, TypeSafe AI's System One model that takes unstructured state plus a typed question and returns a typed decision (Choice, Score, or Noul) with a confidence value in a single forward pass. The list spans 14 categories from classification and routing to agent safety guardrails, database extensions, and open-source replicas, enforcing inclusion rules that require public, citable sources demonstrating a genuine typed-decision use of Jev.
- The repo explicitly warns that same-day bulk submissions from one author sharing a scaffold can satisfy every inclusion rule while remaining unproven, and treats volume as not evidence of quality
- Open replicas range from 547k parameters (minojev) to 395M (von); one 706K-parameter model beats Jev at form-filling (99.7% vs 83.6%)
- Jev performs no token generation; its speed comes from parallel constrained decoding, so an inference engine can expose a Jev-like API over any open-weight model
- A CAPTCHA arbitrage example illustrates per-decision pricing: solving at $0.0068 per hundred against a marketplace paying a cent each
- LangChain published both a Jev harness wiring guide and an independent evaluation concluding Jev is the cheaper and more consistent judge for online evals versus LLM judges
- The repo explicitly warns that same-day bulk submissions from one author sharing a scaffold can satisfy every inclusion rule while remaining unproven, and treats volume as not evidence of quality
- Open replicas range from 547k parameters (minojev) to 395M (von); one 706K-parameter model beats Jev at form-filling (99.7% vs 83.6%)
- Jev performs no token generation; its speed comes from parallel constrained decoding, so an inference engine can expose a Jev-like API over any open-weight model
- A CAPTCHA arbitrage example illustrates per-decision pricing: solving at $0.0068 per hundred against a marketplace paying a cent each
- LangChain published both a Jev harness wiring guide and an independent evaluation concluding Jev is the cheaper and more consistent judge for online evals versus LLM judges
- **Inference** – Platforms and engines for running models, plus user interfaces.
- **Models** – LLMs (general, coding, multimodal, image, audio), model providers, and specific model highlights.
- **RAG** – Retrieval-Augmented Generation tools.
- **Safeguards** – Safety and content filtering.
- **Agents & Tools** – Agent frameworks, Model Context Protocol, coding agents, computer/browser automation, memory management, and testing/evaluation.
- **Research, Training & Fine-tuning** – Security, sandboxing, and model development.
- **Hardware** – Local hardware options.
- **Tutorials** – Guides covering models, prompt/context engineering, inference, agents, and RAG.
- **Communities** – Places to connect and share knowledge.
- **Models** – LLMs (general, coding, multimodal, image, audio), model providers, and specific model highlights.
- **RAG** – Retrieval-Augmented Generation tools.
- **Safeguards** – Safety and content filtering.
- **Agents & Tools** – Agent frameworks, Model Context Protocol, coding agents, computer/browser automation, memory management, and testing/evaluation.
- **Research, Training & Fine-tuning** – Security, sandboxing, and model development.
- **Hardware** – Local hardware options.
- **Tutorials** – Guides covering models, prompt/context engineering, inference, agents, and RAG.
- **Communities** – Places to connect and share knowledge.
A collection of Model Context Protocol (MCP) servers, featuring various implementations, frameworks, and integrations for AI models to interact with local and remote resources.
