Jev for slop and scam filters — a daily feed

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.

I built an on-brand ad machine with Jev and give away my whole setup Everybody is crazy about Jev because it solves a major friction in creative ops: fast and cheap verification of brand consistency This is how I used it. > Get your Jev API key - good news, there is no waitlist anymore > Create a Pletor brain -> one place to store all your brand guidelines, visual references, etc. is key to inform

生成AIなんでも展示会 定点観測してるプロジェクトや、早速Jev の活用事例が多くて参考になった！ （配信とかで目にしたくないコメントを自動非表示するリアルタイム拡張。1日回して10円との事😲 https://t.co/jgZuaAropA

I used Claude to find 21 leads ALREADY complaining about my competitors. For less than 10$. Here's how: > Identify your competitors > Use https://t.co/t6POX0yg0p + apify (or simply @MonidHQ) to scrape Trustpilor + G2 + ProductHung + Reddit + X + LinkedIn. > Jev (other models work just as well) to judge if a review is a complaint or not > Resolve the humans: Name → company → email via Apollo + Hunt

It’s interesting to see what it takes to jailbreak Jev. You can try it yourself here. I put it side by side with GPT-5.6 Luna: https://t.co/gnYVTRkNvg

This is how you Jevmaxx! @typesafeai I built a chrome extension that scans your x feed for larp and hides it for you! The results are INSANE! Larp be gone! Your feed will be free of larp at under 250ms per post and for under $0.01 for your daily doomscrolling needs! Here's how I built this: 1/ Scan the entire available x post library 2/ Find patterns for classic larp indicators 3/ Train jev on the

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. https://t.co/IwXY0YvcFe

The results? By having Jev act as the fast, intuitive gatekeeper before invoking heavy LLM generation, the hybrid pipeline significantly cuts token costs and latency while preserving high-quality memory extraction and knowledge graph #TypeSafeAI #Jev #SystemOne #BuildInPublic https://t.co/YiAkl2vz4N

How I Used Jev for an App Moderation #jev #ai #llm #developer https://t.co/fQXqvvo0ed via @YouTube https://t.co/5JP3g4ogMo

Tired of scam and promo spam in X replies? I built Veil, a Chrome extension that hides replies matching a rule you write in plain English. Powered by Jev from @typesafeai : it scores each reply against your rule, and you set the threshold. Open source 👇 https://t.co/wnpg3214yq https://t.co/9oSTgY1TDh

experimenting with jev handling tool injection at runtime . seems to be an interesting way to cut down on tool costs, with Opus it's cutting down 10 cents for a semi-complex request while identifying the correct tools. https://t.co/CuitRLCPAI

Built an AI slop finder for @X using Jev @typesafeai . • Tweet → Content script extracts text • Text → Backend API → Jev • Jev → AI-slop score • Score → Badge displayed on the tweet #jev #aislop #newmodels https://t.co/VMzJA1Tyde

you know how x feed is blasting you with promos, politics, rage baits, etc? so i made a (tamper)monkey script that uses jev to classify and hide types of posts you don't want https://t.co/PNS9BAhWpN

TypeSafe's Jev (@typesafeai ) can be used as safety filter and their cookbook mentions it as well, but there is no public benchmark. So, I benchmarked it on artificial analysis guardrail benchmark and it ties the #1 model on @ArtificialAnlys's guardrail benchmark. The whole run cost $0.12 (~$17 per 1M prompts).

My first JEV AI integration here) Hey there, i just shipped my first FREE PostMine tool today, and more are coming. Enter your X handle and Jev roasts your last 40 posts for AI slop with JEV (mine got 58% 😅). if you want help writing the next one, that's what PostMine is for. link in the first👇

Most alpha bots scream BUY. HypeMeter knows when to say NO. 👀 it scores hype vs real in seconds, filters NFT traps, and tracks every call publicly. Free in beta + NFT giveaway live. Awaken your Mind here: https://t.co/VVXtL8MXpP #HypeMeter #Jev #NFTSniper #CryptoAlpha #Web3

Jev使ってネタバレ防止のChromeアドオン作ってみた。 例） ガンダムシリーズの投稿は見たいけど、閃光のハサウェイの新作のネタバレは見たくないというシチュエーションでのデモ 結構いい感じに動いてる…！これなら不意打ちのネタバレくらわないのでは…！ @typesafeai #Jev https://t.co/GNRDuhTnfn

I wanted to give Jev a try so i built a chrome plugin for Gmail to try to detect spam, scams, and phishing attempts. Manually triggered scans only. No content is stored, just stats on how many messages you have checked. https://t.co/KKqEChfcPj

Neon EVM on the Hype Meter HYPE 100/100 (how loud) LEGIT 48/100 (how real) Verdict: LIKELY RUG Jev decides: https://t.co/WWjXCT3Vci

I built a Discord moderation bot with Jev powering its decisions — here's a demo https://t.co/SOxdHZESX0

I scanned base:0x9126236476efba9ad8ab77855c60eb5bf37586eb on the Hype Meter @jesusislord HYPE 65/100 (how loud) LEGIT 37/100 (how real) Verdict: HYPE WAGON Jev decides: https://t.co/Me3tJxwdQN

This the the monster I created 1000s of real human reactions filtered by jev on structure and emotion ready to be cloned with AI UGC on https://t.co/i4vF01t4m9 comment "reaction" and I will send you the entire library https://t.co/WOPG1zHmB6

I built an AI slop detector that mostly shuts up. Every detector I tried had an answer for every paragraph, and I think that is the bug. A tool that calls 15% of human writing "AI" is not detecting much of anything. It's accusing people. Slop Meter says can't tell on 87% of paragraphs and is right 97.4% of the time when it does speak. On 24,000 human-written web pages it flagged 0.31%. Same 600 te

Jevによる迷惑メールフィルタ、送信後にフィルタをかけずに、送信前にフィルタをかけたらどうだろうと思い実装。 送信ボタンをクリックすると、先にJevに送られて迷惑メールと判定されると、送信しても届かないよと警告をするようにしました。そのまま再度クリックすれば、送信はできますが、どこまでこれが心理的抵抗に働くか。しばらく、運用してみよう。 1つのリスクとして、Jevが迷惑メールじゃないメールを迷惑メールと判定すると、普通の問い合わせの方に不信感を与えてしまうかもという部分はあるが、Jevの判定基準をしばらく信じてみる！

Jevハッカソンで開発したChrome拡張機能『炎上ガード for X』がchromeウェブストアに登録されました！ 意図せず自分や他人を傷つける結果になりうる投稿を事前に検知してリスクを減らすことができます。外部APIを利用しますが激安なので、気にせず使えます。ぜひに。 https://t.co/zev6hu2DON

jev helped me build my own ad blocker - apple safari-inspired thanos snap … INSANE! w/o any hardcoded filter lists. looks at each element and decides: ad or not in one article: 16 ads gone, ~20k tokens, $0.0008 cached for next visit open source, byok, repo below @typesafeai https://t.co/qkuzE6Wx1N

我用本地 Jev （SemIf + Qwen3.5-4B）修改了我的 Telegram 群組刪廣告 Bot。 它推理只要 0.7s。 替代了原來 Gemma 4 e4b 的中間層，而且準確率更高。 https://t.co/1iaAIzeDmE

I JUST UNCOVERED A $1,000,000 CRYPTO SCAM NETWORK ON X FOR 2 CENTS. 1,010 connected posts. 22 hidden scam syndicates mapped on one detective board in under 60 seconds. This investigation terminal was built as a hybrid system pairing JEV with GROK BOT to create the ultimate automated X forensic engine. While standard moderation misses thousands of fake accounts daily, JEV + GROK BOT exposed the ent

酔ってるかどうかをJevが判定してくれるやつ #aimeetup https://t.co/f4sNEAHycR

Jev Pump Screener just became the fastest pre-ape filter I've ever run it scores whatever PUMPFUN coin you're already looking at BEFORE you decide to send it - and it's free, runs on your own machine, no API key needed to start the loop: you open a coin page → extension reads what's on screen → local score → verdict, right in your terminal step 1 → clone the repo below, npm install in server/, tha

Built https://t.co/ctvcq68VZ1 to keep Telegram groups clean by automatically scanning user profiles and banning spam/NSFW accounts before they cause chaos. Powered it with Jev (@typesafeai) for the backend logic and workflow handling Github: https://t.co/S02ZbhZx7X

the prompt injections that actually hurt agents don't say: "ignore all previous instructions." they say: "please." so i built a prompt-injection hook for claude code and tested it on 239 examples. claude constantly reads: web pages mcp results emails files repo descriptions calendar events product reviews all of that enters the model's context. and if whoever created that content hid an instructio

a bundling/farm detection model using JEV for all new launches on Robinhood costs $0.7/day, ~200 millisecond per token it works like magic https://t.co/KpY36cSlUf

@denisyarats jev-compatible api is what I'd test first. I ran kev-4b vs jev-1.13 on 59 versions of one privacy policy (236 labeled cells): at 0.85 both made 0 errors, kev just abstained a lot more. happy to run AutoJev on the same cells once inference is up. harness: https://t.co/16Tj3jkJdb

I was exploring Jev by @typesafeai AI for safety use cases and it is an impressive and useful model, however don't expect it to magically auto classify any data without proper parameters and thresholds (see finding below). I open-sourced a scam-email classification where you can use your own API key to try it. What I built - a proof of concept for suspicious email classification aimed at scam emai

got tired of reply guys.. so i built a chrome extension that stamps them jev by @typesafeai reads every reply and hides the low effort ones want it? thinking of open sourcing it https://t.co/bwc9wU167S