Evaluation & benchmarks — page 4

A living-world RPG whose NPCs are decided by TypeSafe's Jev judge model; code owns rules, numbers and state.

Multi-axis writing quality checker powered by TypeSafe AI's Jev model. Separate named checks, each with its own verdict and confidence.

A reproducible, self-hosted benchmark for comparing JEV and LLM decision-making on your own datasets, models, and evaluation policies.

Claude Code plugin + zero-dependency CLI for TypeSafe's Jev: judge items in bulk with calibrated yes/no, pick-one and rubric answers; handle the confident ends in code, review only the uncertain band.

A decision layer for agent control flow — yes/no, choice and rubric questions answered from one token's probabilities, measured against labelled sets — with a Claude Code permission hook and a trainable local judge

Benchmark of TypeSafe's Jev as a malicious agent-skill detector on MalSkillBench, with a verify-and-escalate cascade

Pi coding agent extension that shifts to the right LLM for each prompt, using a local (Laya) or remote (Jev) decision model to judge task difficulty.

A fast System One decision model (Jev or local Laya) picks which LLM answers each prompt: OpenAI-compatible Go gateway + decision benchmark

Ne yazarsanız yazın, beş kelimeden biriyle cevap veren sohbet botu. Kelimeyi TypeSafe AI'ın Jev evaluation modeli seçer.

TypeSafe's Jev and four fast LLMs added to Stanford MedHELM's MedHallu results: harness, preregistered run plans and every run file.

Unofficial community MCP plugin for Codex and Claude Code using TypeSafe Jev for bounded routing, ranking, extraction, verification, and escalation

Find out which qualities of your writing actually predict engagement. Rates every post you have published against a pre-registered rubric using Jev's calibrated judgments, then tests those ratings against your real engagement numbers. Refuses to report findings your sample cannot support.

A streaming event bus whose routing, subscription and consumption are decided by a probabilistic judge. The reference judge is TypeSafe AI's Jev (System One) model: send it a payload and a set of typed questions, get back calibrated probabilities instead of prose.

Behavioral contracts for TypeSafe Jev — pin production expectations, eval model upgrades, catch flips and confidence regressions.

Antivirus scanner and real-time defense sentinel: extracts structural features from executables, scripts, and documents (entropy, hashes, strings, PE imports) and has Jev judge whether each is malicious.

Claude Code Stop hook that checks an AI assistant's claims against what it actually read this session, using TypeSafe's Jev as the judge

What your last session knew, scored against what this one is doing. MCP server: a per-project ledger written as things happen, recalled per task with TypeSafe's Jev evaluation model via Vercel AI Gateway.

Independent benchmark data for TypeSafe's Jev (System One model) vs LLMs: accuracy, calibration, cost. Boards + per-decision logs, CC-BY-4.0

Jev test bench — Texas Hold'em edition: live-fire testing of TypeSafe's Jev evaluation model through a React poker game (Vercel AI Gateway). MIT.

Feedback on your paper in seconds.

Typed verdicts on pull requests with TypeSafe System One (Jev): one parallel call, policy in code, usable as a GitHub Action

Typed-decision benchmark from PadFlow (land development SaaS): schemas, anonymized labeled rows, and a runner for confidence-calibrated models like TypeSafe Jev.

Crypto trading bot on Binance testnet using TypeSafe (Jev) to judge news

Utilizing Jev, the RLCD-type model provided by TypeSafe AI, to independently and cheaply judge agentic coding sessions.