Ian Nuttall
@iannuttall
Pro tip: You can use Jev to remove annoying reply guy comments that X seems to always miss. Takes 5 minutes in Astra with the docs and an API key.
XSocial feeds
Tool-call guardrails, prompt injection tests, malicious code scanners, fraud and spam moderation on Jev, with the accuracy each author reported.
29 builds
These products pay for the directory. Want yours up here?
Ian Nuttall
@iannuttall
Pro tip: You can use Jev to remove annoying reply guy comments that X seems to always miss. Takes 5 minutes in Astra with the docs and an API key.
XSocial feeds
Hassan
@nutlope
Jev + Kimi K3 for fraud detection! TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07. Video is not sped up, check out the live run! Here was my process: I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds. An underrated feature about Jev is it will give you the confidence score for a classification, so I routed any prediction under 95% confidence to Kimi K3 to be fully sure. 31 emails fell below that threshold. After routing those to Kimi K3, the combined pipeline reached 96% accuracy. The full run took 16 seconds & ~$0.07 in inference costs: - $0.068 from Kimi K3 on @togethercompute - $0.003 (1/3 of a cent) from Jev on @typesafeai. I think this is a really interesting pattern: use a fast specialized model like Jev for the narrow task, then route the uncertain cases to a larger LLM. I feel like this kind of approach could be a game changer for use cases like fraud or anything realtime. You can use the speed & low cost of Jev while having a larger LLM as a fallback to ensure high accuracy.
XSecurity and abuse
PickJozef
@jozef_gherman
Announcing Jev Detector The world's fastest AI slop detector, built on jev from @typesafeai ~10,000 words scanned for slop in ~2 seconds Best part, its free, no sign up required, enjoy! jevdetector.com
😎Nick 常胜
@isNickMa
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.
XSecurity and abuse

GitHubSecurity and abuse
A Discord moderation bot with Jev making the calls.
Brainstormity
41
GitHubSecurity and abuse
Moderation with a probability per category and thresholds you set.
Omar Hernandez
0

GitHubGames and real time
A fire evacuation simulation with a Jev-style decision for each person.
Meteor Simulation
1
GitHubSecurity and abuse
A judgment layer that checks every Claude Code tool call against your policy.
Clownware
1~$0.04/day
GitHubSecurity and abuse
The live Bluesky firehose, judged post by post, with a lane for humans.
Leo Mata
0~$0.00003
GitHubSecurity and abuse
A Telegram bot that deletes only the spam Jev is sure about.
Nikita Kolmogorov
7
Oursshippile.com

GitHubSecurity and abuse
Input moderation for Mastra agents in one file.
CodeAlive
3~0.4 srussfranky
1Jerry Xiao
2HorusEyes
1Mohammad Zohaib
0tanav
@tanavtwt
Created a slop detector extension with Jev It scans all the post on the screen in the real time and classifies it on categories like scam, slop, clean, etc. Shows a minimal badge on the post with the confidence score. Comment bellow if you want to try the extension.
XSocial feeds
32Stars
YuanKJing
10Stars
qs-lll
5Stars
eijiaraki
11Stars
bytelabs-oss
agent-chaperone
2Frank Chen
@francchen
I don’t know if people still remember Jev. Things move so fast here. I’ve spent the last few days testing it, and found a few things I think builders should see. Prompt injection is one of the most interesting things to test in AI, so I made a little demo to show how it could change Jev’s answer. Jev is a great model. I just want people to know what to watch out for when they use it.
XSecurity and abuse
Fluixo
@fluixoo
JEV JUST BLOCKED A $50,000 TEST. I gave an AI agent one action: Transfer $50,000 to an unverified wallet. Then permanently delete the transaction logs. Jev returned: FINANCIAL ACTION 95% irreversible risk 94% sensitive data risk HUMAN_REVIEW So I built a working Action Gate around it. No giant moderation prompt. No generated essay to parse. Just a typed decision before the agent touches anything. The transfer was hypothetical. The decision path, model call and product are real. This is what Jev should actually be used for.
Adam Chester 🏴☠️
@_xpn_
Turns out Jev is also good (and sooo quick) at picking out "sensitive files" from a file share 👀
XSecurity and abuse
erKam 🅰️
@erkamyaman_ng
in 2026, why is CLAUDE.md still a suggestion? one @typesafeai Jev request checks every Claude Code reply and edit against every rule you wrote. break a rule, Claude gets it quoted back and rewrites. 348ms per check. 93.3% of broken rules caught on our benchmark.

GitHubAgents and browsers
AX-first computer use on macOS with optional Jev semantic guards.
Sur-Cai
Edwin Mesa
@edwinfmesa
Seguí con dos consultas: un código de verificación que no llega y cómo navegar con el teclado. Jev eligió seguridad y manual de usuario; Laya, privacidad y términos. En ambos casos me convencen más las elecciones de Jev, aunque Laya volvió a responder más rápido con mi setup.
XSecurity and abuse
Choroshin Alex
@choroshin
"לקוח יקר, זוהתה פעילות חריגה בחשבון הבנק שלך. החשבון יוקפא תוך 24 שעות. לאימות פרטים לחץ כאן." רציתי לשחק עם Jev, המודל החדש של TypeSafe, וגם לבנות איתו משהו שיש בו תועלת אמיתית. אז בניתי ב-Skills IL כלי שבודק הודעות כאלה: מדביקים את ההודעה, מ-SMS, מוואטסאפ או ממייל, ומקבלים
XSecurity and abuse