A curated, awesome-lint-clean list of projects built on TypeSafe's Jev, the first System One model, with a searchable site whose search is reranked by Jev and a "know before you build" section on limits.
A selective awesome list of Jev projects, integrations, and resources: 241 entries each checked to actually call Jev, admitted at a 10-star bar, and paired with hands-on reviews at mrjev.com.
An omp plugin that does verbatim Jev-scored context reduction: stale tool calls and results are truncated to a short head plus a recoverable note, while sticky rewrites keep the provider's prompt cache intact.
An open, local-first alternative to Jev that reproduces the Choice, Score, and Noul interface on commodity zero-shot NLI models with temperature scaling and conformal abstention, and runs offline with no API key.
DataCamp's explainer walks the eval tables behind System One models, from 67.8% workflow accuracy and 0% structured-output errors to the $0.042 per million token price and how to call the API.
A test report that puts Jev in front of each agent action before it runs: most attacks were caught, with almost no false positives on legitimate tool calls.
Turn natural language into typed function calls: closed-set arguments become Choice questions, optional ones become Noul questions, and 54 questions ship in one request for a trading assistant.
Rank 182 agent skills in one request and re-read the top three in a second: over 488 requests, wrong skill loads fell from 16.8% to 7.3% and needless loads from 9.8% to 4.0%.
A browser-use agent where Jev picks one action per step from a page's interactive elements. Runs as an MCP server, CLI, or library and returns a step trace, screenshot, and console errors.
An MCP server exposing ten Jev judgment tools, including verify, screen, find, rerank, classify, review, and gate, each returning typed probabilities in roughly 150 to 500 ms.
A single-binary Go MCP server exposing one evaluate tool for noul, choice, and score questions, with one-command registration for Claude Code, Claude Desktop, and Codex.
An agent skill for writing and improving programs that call Jev: question design, state structure, answer composition, confidence thresholds, and how to diagnose a question that keeps coming back wrong.
A small TypeScript client for asking typed questions about your data: one ask call mixes yes/no, choice, and score questions and returns probabilities, selected options, and confidences.
A Rust CLI that ranks agent skills for the next step with a two-stage Jev pass plus lexical prefiltering, with Claude Code hooks, a TUI, abstention, and --why-not explanations.
LangChain's langchain-typesafe integration exposes Jev as a TypeSafeClassifier plus experimental model-routing and auto-mode middleware that screens risky tool calls before execution.
Tamara's demo of fast-jev-compaction for Claude Code: instead of summarizing context, Jev scores every tool call and drops the irrelevant ones, with a video of it running.
Alex Volkov reports that a Jev-powered Claude plugin reviewing unnecessary tool calls cut a session from nearly 1M to 86K tokens in about one second, with the install prompt in the post.
A widely read counterpoint to Jev-based context compaction: compaction is reconstruction, not filtering, and scoring tool calls to drop history misunderstands context management.
An agent-first CLI for System One: ask, rank, extract, verify, screen, find, and decide from the shell, with an installable agent skill. Published on PyPI.
An open Python toolkit with question builders, confidence gates, tiered routing, a concurrent corpus evaluator, calibration tooling, a CLI, and an MCP server, with published benchmarks.
Jev-backed auto mode for the Pi coding agent: semantically approves bash, write, and edit tool calls, and fails closed whenever a decision cannot be made.
A Claude Code plugin and npm library that replaces transcript summarization with Jev keep-or-drop judgments, deleting stale tool calls and results while kept text stays verbatim.
psql-shaped CLI and Go, TypeScript, and Python SDKs that let you write jev(), jev_prob, jev_choice, and jev_score in queries against vanilla PostgreSQL, judging the surviving rows with Jev in batches. No CREATE EXTENSION or superuser required.
Jev bills input tokens and gives output away, and questions in one call run in parallel. Why the state is where the money is, and how to size a request before you ship it.
Retrieval is a sequence of bounded judgments. How a decision model slots into passage filtering, semantic find, reranking, and citation checks, and what stays in deterministic code.
Support is a long chain of small judgments over text. Triage category and urgency separately, route with a confidence gate, decompose hard tickets, and decide the action in code.