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CommunityTools & Integrations1 starsVerified 2026-09-22

TypeSafe Code Guard

An MCP server for Antigravity IDE, Cursor, and Claude Code that uses Jev (jev-latest) to locate code semantically and audit git diffs, replacing broad grep sweeps of entire files.

Category
Tools & Integrations
Published by
Community
Author
greenyamao
Added
2026-09-22
Tagscommunitypythonmcpcoding-agentsearchguardrails

Highlights

  • typesafe_find_code selects a file through Choice and Noul judgments, then pinpoints a 15-line preview with line numbers and confidence scores.
  • typesafe_audit_diff questions a git diff against task intent and flags global state mutations, removed defensive checks, and debug leftovers.
  • Shifts candidate search and ranking outside the LLM context, ingesting roughly 150 tokens instead of 25,000 to 40,000.
  • Evaluates code semantics through cloud decisions at about $0.00004 per call.

Quickstart

json
{
  "mcpServers": {
    "typesafe-code-guard": {
      "command": "python",
      "args": ["/path/to/repository/mcp_server.py"]
    }
  }
}

Watch out

No license file, so reuse terms are unclear. Needs Python 3.10+, a TypeSafe API key, and manual MCP registration in the assistant's config.

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From the community

Posts from builders shipping with Jev right now.

Follow @typesafeai

Screening agent actions with Jev

Tested TypeSafe’s Jev (no-text, probability-only model) as an AI agent safety monitor. Checking each action first worked well caught most attacks with almost no false blocks, and much faster than Gemini.

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Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

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Cua's small System One models

A 706K-parameter form filler

cua open sourced a 706k param model that fills a whole form in one 50ms pass the llm agent doing the same form took 23 turns and 39.6 seconds the specialists are going to eat the generalists from the bottom

Cua
Cua
@trycua

1/ Introducing CUA-S1: a family of System One Models, small, specialized, and built for computer use. Today we're open-sourcing CUA-S1-FORMS, the first in the family: github.com/trycua/cua

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Navigating Neo4j with Jev

Jev 这个 waitlist 还是很给力的,昨天申请,今天就能用上。 给已经拿到 API、但还不知道怎么玩的人整理了一份 Awesome Jev,目前我能确认到的 Jev 项目基本都在这里: 1. jev-ultrafast Browser Use 做的高速浏览器 Agent。Jev Show more

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思维怪怪
思维怪怪
@0xLogicrw

前 OpenAI 研究员 Diogo Almeida 创办的 TypeSafe AI 推出新模型 Jev。它有点像一个能读懂自然语言的超级分类器,不生成文本,只返回选项、分数和概率,专门给软件做判断。 普通大模型需要一个 token 一个 token 往外生成,Jev 则可以并行给出多个结果。TypeSafe 还用新的 RLCD

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Reranking 33,047 catalog entries

拿 Jev 做搜索重排,我先泼一盆冷水:单独用,它没打赢向量检索 TypeSafe 的 Jev 这阵子很火,一堆项目拿它做重排。我们在 Agent Skills Hub 的 33,047 条目录上认真测了一次,164 条中英文真实查询,9,831 对分级标注,整套只花了 2.6 美元 三个结论 01|单独重排,约等于没赢 Jev 重排 bge-m3 Show more

Jason Zhu
Jason Zhu
@GoSailGlobal

有美团、阿里的老哥嘛? 试试加一路召回、重排(离线、近实时实现),我觉得有奇效 他在文本理解上 跟之前机器学习、llm很不一样 还能自动打标签做特征

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Six uses that stuck after 60 days