Agent Chaperone
An MCP proxy and hooks adapter screen agent tool calls and returned content with probability-based Jev judgments.
Tools that help agents and command-line users choose their next command, tool, memory, route or safeguard.
An MCP proxy and hooks adapter screen agent tool calls and returned content with probability-based Jev judgments.
A small Grok Bot router uses Jev to decide whether to reuse work, stop retries, limit research, or ask for approval.
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NewA prompt composer classifies task difficulty when the user finishes typing and offers a faster mode for simple requests.
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NewA natural-language API finder uses Jev to choose among more than a thousand endpoints across dozens of providers.
A MoonBit playground puts Jev's typed decisions into games, command-line experiments, browser actions, and agent safety checks.
A Pi coding-agent extension uses Jev to discover relevant tools and skills, make typed judgments, and optionally guard tool calls and compaction.
A Unix CLI that turns Jev judgments into predicates, routes, scores, filtered streams and stable exit codes.
A fail-closed permission gate for Pi that asks Jev to judge tool calls left undecided by hard rules.
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NewA visual builder that wires Jev Choice, Score and Boolean decisions into prompt workflows.
Daily rankings of agent skills across registries, with Jev judging social relevance, listing quality and category.
An opt-in Jev review of ambiguous dead-code findings in the Skylos scanner, with uncertain results left visible.
A phone conversation copilot that reads visible chat and offers suggested replies while the user stays in control.
Jev DSH 决策引擎|面向 Agent Harness 的结构化决策插件。原生支持 DeepSeek Harness,通过 iPolloWork 支持 OpenCode、Codex Harness。
Jev-powered semantic code search for coding agents — find behavior across repositories via CLI or MCP, with exact source excerpts and line numbers.
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NewJev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open @typesafeai , create API key (keep it off chat paste) step 2 → tell…
Semantic grep CLI where Jev judges which files and exact line ranges match a natural-language code query.
Retrieve by relevance, not resemblance: filter an AI assistant's memories with TypeSafe's Jev.
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NewJev killed 7 more SEO/GEO workflows 👇 1/ Assess which competitor pages to copy -> It scores every competitor page on answer, depth, proof and freshness, then checks its rank in Google and ChatGPT to show which ones are worth copying 2/ Identify which page elements to change to get cited -> It reads the title, meta…
用 TypeSafe Jev 推荐已安装 Skill / Bounded installed-skill recommendations with TypeSafe Jev. Python CLI, Codex skill, bilingual docs and live examples.
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NewJev brought us closer to JARVIS it instantly does things like launch agents on a canvas - without awkawardly waiting for slowGPT LLM loop @clonkapp is now the fastest agent orchestrator on the planet
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NewJev dropped the price of SEO/GEO fixes by 90% Agents that audit and fix a client's SEO/GEO used to cost us ~$250 Here's where the savings come from: 1/ 30x faster reads of Search Console and PostHog/Mixpanel data 2/ 30x faster checks of what ChatGPT searches on Bing 3/ 30x faster modeling of what users ask Gemini and…
A Jev toolkit for agent model routing, memory filtering, compaction, skill selection, triage, and bounded computer/browser decisions.
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Newbest coding agent use case for Jev: enforcing rules a linter can't coding agents tend to break rules and some rules can't be codified Jev can now score every turn against such rules and tell the agent what to fix asap open-source:
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NewToday I’m excited to show you our new harness called AgentRun, built with @pidotdev and @typesafeai's Jev. It’s built for an agent to learn how to do a job, code itself a general solution, and then get out of the way.
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Newit's f*cking insane Jev sat between GPT and me, killing every draft that broke my rules before i saw it. good setup. then Jev did not answer. the agent decided silence was safer and stopped sending me anything at all. took a second agent to unstick it. your checker needs a branch for the moment it goes quiet: > let it…
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NewA Box demonstration uses Jev decisions to organize incident reports into escalation, monitoring and review folders.
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NewAlright playing with Jev through Vercel. 1. quick coaching support during a session rather than running through my gateway. 2. Anti-slop detector and fixer.
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Newthanks to @typesafeai jev I no longer have fill out all of those fields on prompt boxes. It picks the agent / model / computer / folder for me. - For a major rewrite it uses Fable + Claude Code. - Changes to an ios app run on one of my macs
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New🆕@typesafeai dropped Jev this week, a new frontier model optimized for decisions. We put it to work on an incident triage workflow in Box. Jev doesn't generate text. You send it state plus typed questions and it returns booleans with probabilities, enums, and scores in a single pass, so there's no output to parse and…
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NewJev made our Slack agent 2x faster ⚡️ Our agent can be quite slow because it needs to read skills and figure out which tools to call. We used @typesafeai's new model to speed this up by first passing it the prompt and classifying the best skill, tool and params to use before handing it to the agent
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NewPlayed around with @typesafeai Jev today, mostly to understand what it does for tool calling. Instead of an LLM deciding what to do, I substituted that part with Jev. My learning is that we will be able to make the agent act before the user finishes the sentence. So far, we've used different LLM combinations…
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NewDoc-OCR router using Jev @typesafeai A Jev-powered router that looks at a PDF page by page, decides which pages actually need OCR, extracts the rest locally. Result: save cost on # OCR pages + speed
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NewCorent’s brain uses @typesafeai Jev now. Not to generate the output, to make the decision before the output gets generated. When a request comes into Corent, you don’t need to tell us which model to use. You send the prompt, the workload and the quality level. Then Jev helps us decide what that request actually needs…
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NewStarchild just became the first professional-grade app to integrate Jev from @typesafeai We use Jev to help route to LLMs which gave a 20x decrease in cost and 6x increase in speed for prompt classification Here it is classifying prompts in real-time (avg time was 140ms)
A Rust CLI and MCP server uses Jev to route and score zero-cost SEO and generative-search research tasks.
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NewExperimentation with Jev from @typesafeai and our harness @goose_oss Use Jev to take a prompt and JIT select a model just before the turn runs. Jev and models like it will be useful in many areas and this is just early prototype code on a first idea
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NewGot early access to @typesafeai Jev and spent a few hours building on it. The problem I keep hitting: agent traces tell you which tool ran, how long it took, what it returned but never whether the agent is actually getting anywhere. An agent editing, testing and reverting the same file six times looks perfectly…
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NewShipper presents a Jev-assisted workflow for turning website URLs into mobile applications; the full build path is not independently reproduced.
Scans a codebase for covert, deceptive, or data-stealing behavior with Jev, then reports suspicious files and line ranges before the user runs it.
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NewJev also constantly monitors my macOS clipboard. Is the content of the clipboard a terminal command? The app offers a quick action to run it in my terminal.
Jev picks which of your rules apply to each prompt, so Claude only sees the ones that matter.
A command-line toolkit exposes Jev verification, classification, extraction, ranking, routing, compaction, and custom typed decisions for scripts, CI, and agent workflows.
Fish-style zsh history autosuggestions ranked by Jev (TypeSafe).
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NewAs promised, Jev is now integrated into Orus agents. What is Jev, and how can it help our agents? Jev is a model from TypeSafe AI, built to answer precise questions with structured answers. In Orus, it reviews the trades your strategy proposes, before they happen. It approves the entry, refuses it, or waits for one…
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NewEveryone picks Jev confidence thresholds by vibes. 0.95? 0.5? I built jevcal. Give it your data and say "I need 99% accuracy". You get the exact threshold, how much Jev can handle, and how much still needs an LLM. Open source, one command. @typesafeai
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NewJust hacked a fun little project - Skill Router (Open source) using Claude and Jev @typesafeai Why? I have ~ 90 Claude Code skills installed and use maybe five. The rest are fine, I just forgot they existed, and Claude won't reach for a skill on its own unless you call it. So I built myself a little personal…
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Newi added Jev compaction to a small agent with searchable memory. this is a fork of nanocode with some extra tools to grep a memory.md file + use Jev compaction to remove unnecessary memories.
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Newsmall experiment where I used jev to power the global search in replicas. the possibilities of improving UX with models like jev are endless. we can now have things like: - search that understands what users mean - actions that change based on what you're trying to do - routing tasks to the right agent/model…
Claude Code plugin: trim long Bash output with TypeSafe Jev before the model sees it.
ACP and MCP adapter that bridges TypeSafe Jev with any LLM — computer use and typed decisions alongside Codex, Claude, Grok, and OpenCode.
Classify first. Read selectively. A portable agent plugin and MCP tool for batch text classification.
Context-pruning proxy for Claude Code and Codex: Jev judges which history is still needed, measured not claimed. POC here now, heading soon into https://github.com/compozy/compozy.
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NewAn OpenCode plugin demonstration uses Jev to assess the intent of commands against an allowed-domain policy.
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NewA Claude Code plugin experiments with pruning context instead of summarizing it.
"Work appears completed or recorded. Run /compact to save tokens."
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NewA demo assistant connects search, reference, weather and personal tools.
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Newi made a @typesafeai jev+chatgpt instance race against a purely chatgpt instance on two super simple tasks to see the difference, 2-3x faster! :o on first-time tasks chatgpt will write a question set for jev on the fly, and then subsequent tasks get super fast https://t.co/gMBtV3ou3c
Self-hosted, versioned skills library for AI agents. MCP, scoped clients, and optional Jev recommendations.
Interactive TypeScript coding CLI with typed routing, decision programs, and validated formal trees driven by Jev.
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NewA CLI recommends an agent or model from a task and the user's available AI subscriptions.
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NewBuilding an agent with model router powered by Jev.
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NewA PR-review demonstration turns Jev probabilities into review routes, with uncertain critical checks handed to a human or larger model.
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NewA creator tests Jev in a memory workflow and reports lower token use and faster retrieval; the measurements remain creator-reported.
Skillbox uses Jev to return relevant agent skills directly from a query.
An experimental supervisor uses Jev to assess and steer a coding agent.
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Newgot @typesafeai's new model Jev as a chief of staff for bots Jev reads the task, wakes the right teammates off the bench and gives each one the right model It is possible on OpenMausBot as it supports all the LLMs from your existing subscriptions Jev as a decision engine is great
A command-line tool classifies Git commits by change type, bug fixes and security relevance.
Now using @typesafeai Jev in etc AI ends up vibe coding so much AI regex slop if you don't read the code, so I can finally move all this hard-coding to Jev and it's insanely fast! Also for regular LLM calls, it is around 10x faster, 50% cheaper
A Rust CLI ranks installed agent skills against live session context with Jev, then applies abstention and local feedback around the typed result.
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NewAn MCP tool supplies repeated software-quality measurements to coding agents.
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NewA router uses Jev to choose the model that best fits each incoming request.
A personal-assistant agent uses Jev to select one tool from a 100-tool catalog before each model step.
Ultra-fast, criteria-based model routing using Jev from @typesafeai.
A staged code-review workflow follows Jev signals through focused checks and a local dashboard.
A local router chooses a model tier for each Claude Code or Codex turn.
A Pi extension gates tool calls, judges command output and exposes typed Jev questions.
A Pi extension watches tool calls and agent progress for safety and completion problems.
A Claude Code hook that filters large tool results before they enter the agent's context.
The Loki agent offers Jev as an optional typed-decision companion and session model router.
Code quality and test coverage for coding agents: Jev scores each source file so the agent knows what to fix first.
An agent memory plugin that can ask Jev whether a new request needs a memory search.
duet-agent uses a Jev-backed routing table to select models for agent work.
A local memory system for AI agents with an optional Jev reranker for recalled notes.
A repository governance kernel can attach optional Jev trajectory observations to its assurance loop while retaining deterministic policy, interlocks and proof rules.
OpenWork uses Jev as a typed verification judge for agent-produced work in its evaluation testkit.
Smithers uses Jev checks and scores across repository and workflow automation.
A terminal tool exposes typed Boolean, Choice and Score evaluations with Jev as the default evaluation model.
NeuroLink exposes typed Jev decisions for routing, compaction, tool use, and retrieval planning.
A terminal coding agent uses Jev for typed turn, risk and progress judgments before its generative planning and tool loop.
Formanator can use Jev to select benefit and reimbursement categories for claims.
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