gg-friggin-ez

Notable signal

Fast & free profanity and toxicity screening via Jev & Laya

Launched 14h ago

Signal

Velocity

What this means

+111%Launching in a 111% WoW growing category.

Open Source had 139 launches this week vs 66 last.

13Founder Conviction Index: 13 — low signal.

Few of the conviction sub-signals (reputation, velocity, buyer-intent, tagline clarity) are firing yet.

Prediction

Top-5 finish probability

22%

today

Projected end-of-day votes

90range 68–122

Trajectory

stable

Vote pace holding steady.

Speed vs peers

1.3×

5 Developer Infrastructure launches

About

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)

AI Summary

gg-friggin-ez is an open-source multilingual profanity and toxicity screening tool for Node.js, utilizing advanced AI models to detect various forms of offensive language. It supports multiple Indic languages and offers configurable moderation actions with a low cost of approximately $0.000004 per message.

Attention & discussion velocity

Performance

Velocity0

Vote pace vs average

Momentum7

Sustained over 6h

Virality0

Spread × engagement

Engagement5

Discussion per unit of attention

Editorial read

Scored deterministically. Only candidates that fire a story trigger are sent to a model, so this one has no written angle.

InvestigateRanks high on little evidence. Worth a look, but verify before committing.

265/8

Surprising

Timeliness5%100

carried 7%

Novelty15%97

carried 20%

Story20%87

carried 27%

Trend20%50

carried 27%

Business15%3

carried 20%

Not measured · 3 of 8

Revenue10%—

Founder10%—

Cross-platform5%—

Gaps in our data, not findings about the product. Their weight is redistributed across the 5 we did measure.

measuredmeasured, zeronot measured

Full audit →

Signal sources

3 of 6 active

Launch tracking4 readings since we picked it up
AI analysisDeveloper Infrastructure
Comment intelligencethread not yet analysed
Outside discussionnot checked — domain is a storefront or shared
Revenue signalsno match on this domain
Editorial scoring13% evidence coverage

A source that found nothing is a measurement. A source that has not run is a gap. Neither means the launch lacks the thing.