Blind security benchmarks for Jev, TypeSafe's System One model: prompt injection and vulnerable code detection, built on jev-go
Research and benchmarks — page 7
Benchmarks, papers and independent reproductions. Read these when you need to understand behaviour rather than adopt a library.
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Zero-shot named entity recognition on TypeSafe Jev. Define entity types in plain words — no training data, no GPU. 73.7 strict F1 across 12 Chinese/English NER benchmarks, with calibrated scores and auditable traces. 零样本命名实体识别。
Rubric-Based Zero-Shot Classification Benchmark: Jev vs Claude Haiku 4.5 vs Claude Sonnet 5 vs OpenJev on rubric-conditioned classification, chained decision execution, and exam grading -- with full price tracking.
Evaluate TypeSafe AI's Jev (System One Model) on TMMLU+ v1.1 — four-way choice via the API's own response schema, 100% parse rate by construction
Benchmarks of MCP tool selection accuracy and latency. V2 evaluates 143 Woku tools with GPT-6 Luna API calls and Jev; includes Go/Rust cores, one TypeScript SDK and reproducible reports.
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Small, fast, honest decision models. Open alternative to Jev: zero-shot, fit on your labels in seconds, calibrated with a coverage guarantee, Jev wire-compatible.
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Benchmark of TypeSafe's Jev against Sonnet 5, GPT-5 nano and local LLMs on 770 Reddit AITA verdicts: Brier scores, latency and cost
Typed System-1 decisions (Laya/Jev) as the judgment layer of a quant research stack, with Claude as System 2. Requirements → design → code → experiments.
Real-world Jev use cases, open-source projects, benchmarks and criticism — what people actually build with TypeSafe AI's Jev, and where it fails. Machine-readable, updated daily.
MCP server for Jev (TypeSafe System One): the three official question types — choice, score, noul — plus batch classify. Calibrated probabilities, ~0.5s, <$0.001/call.
A Quick Typesafe's Jev Evaluation on Persian
spaCy vs TypeSafe Jev on English spam, support routing and Swedish routing, evaluated in Braintrust
Local multimodal decisions on your Mac. Jev-compatible typed probabilities with Qwen, llama.cpp and Metal.
Compare TypeSafe Jev and LLMs on 60 synthetic candidate-experience reviews: four classification tasks, prompt variants, costs, tokens, and reproducible evidence.
Jev drives your Ego Lite browser: one typed-choice request per step. Single-file, zero-dependency port of browser-use/jev-ultrafast with multi-model benchmarks and extra guardrails. Unofficial.
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A TypeSafe-native (Jev) coding agent built on Recursive LLM Context Decomposition (RLCD), with a native macOS client
Pytest plugin for semantic assertions on LLM output using Jev's calibrated probabilities.
Sub-100ms security gate for AI agent tool calls, powered by TypeSafe's Jev (System-1) decision model. Single-request Choice/Noul/Score evaluation, dual-factor blocking matrix, calibrated-confidence routing, fail-closed parsing, zero-config local fallback. LangChain-ready.
An automated research paper radar that fetches the latest papers from arXiv, evaluates their relevance to a configurable research profile using TypeSafe AI, and ranks them by relevance score. Designed for personalized, daily literature discovery across different research domains.
Google ADK vs Microsoft Agent Framework for structured-output agents, with TypeSafe Jev as a vendor-neutral QC gate
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Semantic test matchers for Vitest and Jest, powered by TypeSafe's Jev model. Write expectations in plain English, get calibrated probabilities back.
AI life-and-civilization simulation: TypeSafe Jev makes every decision (typed, probabilistic, auditable); LLMs plan — OpenAI-compatible APIs, local models (Ollama, LM Studio), Claude Code, Codex.
No project description available.
Run jev-browser on a fully local JEV-style decision engine (no cloud API). Warm-browser fork, VRAM guard, measured benchmarks, run traces.