Fast & free profanity and toxicity screening via Jev & Laya
Fast, free, drop-in multilingual profanity and toxicity screener for Node.js, powered by System 1 models like TypeSafe AI Jev and Laya. Catches leetspeak, ASCII drawings, character spacing, and romanized profanity across all languages including Kannada, Telugu, Tamil, Hindi, and Bengali. Ultra-low cost • Multilingual • Native Indic support • Evasion-aware • Sub-500ms • ~$0.000004/message • Configurable moderation actions • Open source (npm i gg-friggin-ez)
Hey Product Hunt! 👋 I'm Shikhar, creator of gg-friggin-ez
Why I built this Back when I worked in the real-money gaming industry, chat moderation was one of those problems that never had a good answer - too slow, expensive, or dumb to catch anything past a static keyword list. I've since moved into backend/AI engineering, and gg-friggin-ez is what happens when that old problem meets the current stack: System 1 models like Jev and Laya that enable real-time, multilingual toxicity screening cheap enough to run on every single message.
Pre-LLMs: Fast, but brittle - traditional filters and ML/NLP models struggled with Romanized Indic, slang, ASCII art, and creative evasion. LLMs: Smart, but too expensive to run at scale. System 1 Models (Jev, Laya): Single forward-pass decision engines built for real-time classification - sub-500ms end-to-end, deterministic output, and pennies per million tokens.
So I built gg-friggin-ez around it.
What it does • Evasion-proof: Catches Romanized Indic profanity, leetspeak & ASCII-art evasion • Deterministic actions: Converts toxicity into ALLOW, REVIEW, CENSOR, BAN • Rich telemetry: Returns confidence scores, evasion detection flags, and primary language classification. • Lightning-fast: sub-500ms moderation • Ultra-low cost: ~$0.000042/msg using Jev ($0.042 per million tokens), or $0 inference cost using self-hosted open-source models
Pluggable Architecture & Bring Your Own Model (BYOM)
While gg-friggin-ez ships with TypeSafe AI's Jev as the default out-of-the-box engine, it is completely decoupled, so you can point it to your own System 1 models.
Ever seen someone outsmart a chat filter? Tell me how. Let’s see if gg-friggin-ez catches it.
About gg-friggin-ez on Product Hunt
“Fast & free profanity and toxicity screening via Jev & Laya”
gg-friggin-ez launched on Product Hunt on September 22nd, 2026 and earned 90 upvotes and 4 comments, placing #16 on the daily leaderboard. Fast, free, drop-in multilingual profanity and toxicity screener for Node.js, powered by System 1 models like TypeSafe AI Jev and Laya. Catches leetspeak, ASCII drawings, character spacing, and romanized profanity across all languages including Kannada, Telugu, Tamil, Hindi, and Bengali. Ultra-low cost • Multilingual • Native Indic support • Evasion-aware • Sub-500ms • ~$0.000004/message • Configurable moderation actions • Open source (npm i gg-friggin-ez)
On the analytics side, gg-friggin-ez competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how gg-friggin-ez performed against the three products that launched closest to it on the same day.
Who hunted gg-friggin-ez?
gg-friggin-ez was hunted by Shikhar Srivastava. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of gg-friggin-ez including community comment highlights and product details, visit the product overview.
Shikhar Srivastava
Hey Product Hunt! 👋 I'm Shikhar, creator of gg-friggin-ez
Why I built this
Back when I worked in the real-money gaming industry, chat moderation was one of those problems that never had a good answer - too slow, expensive, or dumb to catch anything past a static keyword list. I've since moved into backend/AI engineering, and gg-friggin-ez is what happens when that old problem meets the current stack: System 1 models like Jev and Laya that enable real-time, multilingual toxicity screening cheap enough to run on every single message.
Pre-LLMs: Fast, but brittle - traditional filters and ML/NLP models struggled with Romanized Indic, slang, ASCII art, and creative evasion.
LLMs: Smart, but too expensive to run at scale.
System 1 Models (Jev, Laya): Single forward-pass decision engines built for real-time classification - sub-500ms end-to-end, deterministic output, and pennies per million tokens.
So I built gg-friggin-ez around it.
What it does
• Evasion-proof: Catches Romanized Indic profanity, leetspeak & ASCII-art evasion
• Deterministic actions: Converts toxicity into ALLOW, REVIEW, CENSOR, BAN
• Rich telemetry: Returns confidence scores, evasion detection flags, and primary language classification.
• Lightning-fast: sub-500ms moderation
• Ultra-low cost: ~$0.000042/msg using Jev ($0.042 per million tokens), or $0 inference cost using self-hosted open-source models
Pluggable Architecture & Bring Your Own Model (BYOM)
While gg-friggin-ez ships with TypeSafe AI's Jev as the default out-of-the-box engine, it is completely decoupled, so you can point it to your own System 1 models.
It's 100% free and open-source.
• npm i gg-friggin-ez
• GitHub: https://github.com/ItisShikhar/g...
• Demo: https://itisshikhar.github.io/gg...
Ever seen someone outsmart a chat filter? Tell me how. Let’s see if gg-friggin-ez catches it.