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.
multilingual leetspeak and character-spacing detection is the hard version of this problem, and it's usually also where the false positives live, place names, usernames, and ordinary words in one language that happen to look like a workaround in another get caught by the same pattern matching that catches real evasion attempts. the classic case is a word like "Scunthorpe" tripping a filter for containing a substring that reads as profanity out of context. for a game chat with real-money stakes or bans attached, a wrongly muted or banned player is its own support cost. is there a documented false-positive rate across languages, or a way for a game to see what got flagged and why before it's acted on, rather than the screening being a silent pass/fail?
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)
gg-friggin-ez was featured in Open Source (68.9k followers), Developer Tools (519.9k followers), Artificial Intelligence (479.2k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 253.1k products, making this a competitive space to launch in.
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.
Want to see how gg-friggin-ez stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
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.