Tooling46d ago
Auto-Guard open-sources Jev-backed allow/escalate/block gates for coding-agent tool calls
@roshanchandna introduced rchandnaWUSTL/auto-guard: before a coding agent runs a tool call, Auto-Guard sends the proposal to TypeSafe Jev and returns allow, escalate (ask you with a one-line LLM reason), or block. Install with pip install agent-autoguard then autoguard install for Claude Code by default, with adapters for Cursor, Codex, Copilot, Gemini, and Windsurf. The author cites roughly 0.4s and about $0.000025 per check.
Tooling38d ago
DeepEval 4.2: Jev for LLM-as-judge decisions at ~$0.042/M input tokens and ~100ms
Confident AI’s deepeval 4.2.x (notes for 4.2.2/4.2.4; PyPI at 4.2.6) wires TypeSafe Jev into LLM-as-a-judge: evaluation LLMs still handle language tasks, while closed verdicts go to System One for typed decisions with calibrated probabilities. Claimed decision-stage pricing is about $0.042 per million input tokens with no output charge, ~100ms typical latency, and batched independent questions. Faithfulness, GEval, DAGMetric, and Classifiers are integrated; enable via deepeval set-mode experimental plus typesafe-sdk—missing keys fail loudly instead of silent fallback.
Release38d ago
GPT Researcher 0.16/v3.7.0: Jev usefulness context filter beats embeddings by 59% relevance
GPT Researcher shipped v3.7.0 on 2026-09-26 (PyPI gpt-researcher 0.16.x). The context filter now defaults to TypeSafe Jev, scoring passage usefulness for each sub-query instead of embedding similarity. On a 28-task replay with identical sources, kept-passage relevance is 73% for Jev vs 46% for embeddings (~59% relative lift). Without TYPESAFE_API_KEY it falls back to local BM25—no embedding provider required. Also requires Python 3.12+ and adds a retriever plugin entry point.
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