gg-friggin-ez

gg-friggin-ez is a fast, free, drop-in multilingual profanity and toxicity screener for Node.js. It is powered by System 1 models such as TypeSafe AI's Jev and Laya, and it is built to catch leetspeak, ASCII drawings, character spacing, and romanized profanity across all languages, with native support explicitly called out for Kannada, Telugu, Tamil, Hindi, and Bengali. The project targets developers and engineering teams that need to screen user-generated text for profanity and toxicity in real time and at scale, and its stated goal is to be fast and cheap enough to run on every single message rather than only on a sampled fraction of traffic. The product grew out of a moderation problem the maker encountered while working in the real-money gaming industry, where chat moderation never had a good answer because existing options were too slow, too expensive, or too dumb to catch anything past a static keyword list. The maker frames the history simply. Pre-LLM approaches were fast but brittle, and traditional filters and ML/NLP models struggled with Romanized Indic text, slang, ASCII art, and creative evasion. Large language models were smart but too expensive to run at scale. System 1 models such as Jev and Laya changed the trade-off: they are single forward-pass decision engines built for real-time classification, offering sub-500ms end-to-end latency, deterministic output, and costs measured in pennies per million tokens. gg-friggin-ez was built around that shift, and it is designed for teams that need a real moderation answer rather than a keyword list. The headline capability is evasion-proof detection. According to the maker, the screener catches Romanized Indic profanity, leetspeak and ASCII-art evasion, and character-spacing tricks that are designed to slip past a keyword list. That is the genuinely hard version of the problem: not catching a plainly spelled swear word, but catching the many deliberately obfuscated ways people write the same word so that substring or keyword matching will not fire. The product also claims broad multilingual coverage across all languages, and it highlights native Indic support for Kannada, Telugu, Tamil, Hindi, and Bengali, which are languages where romanized and mixed-script input is common in chat and where static filters tend to be weakest. gg-friggin-ez converts its toxicity assessment into deterministic moderation actions: ALLOW, REVIEW, CENSOR, or BAN. Nothing happens silently. Every call returns the probability plus reasoning before a backend acts on anything, AUTO_BAN only fires at high confidence, and ambiguous content is routed to review instead of being instantly banned. Alongside the decision, the screener returns rich telemetry, including confidence scores, evasion detection flags, and a primary language classification, so a team can see not just what was flagged but why and in which language. The maker points to a Scunthorpe-style test case showing that a contextual model returns an ALLOW result for the sentence "I live in Scunthorpe", unlike substring matching, which can trip on innocent words. Performance and cost are central to the pitch. Moderation is described as sub-500ms, and the cost is stated as roughly $0.000042 per message when using Jev at $0.042 per million tokens, or $0 inference cost when self-hosting the open-source models. Those numbers matter at scale: a screener that costs a tiny fraction of a cent per message can be applied to every message in a chat stream rather than to a sample, which is precisely what makes real-time moderation practical in high-volume environments where a single stream may carry thousands of messages. gg-friggin-ez ships with TypeSafe AI's Jev as the default out-of-the-box engine, but the architecture is fully decoupled, so the pipeline can be pointed at your own System 1 models. Under the hood, the approach is to use System 1 models as single forward-pass decision engines, which is what produces the combination of real-time latency, deterministic output, and low inference cost. Instead of matching substrings, the model makes a contextual judgement and returns probability and reasoning that the surrounding application can act on, with the option to send uncertain cases to a human instead of taking an irreversible action. The stated benefits follow from those design choices. Because a false positive may still be routed to review rather than triggering an instant ban, the maker argues that recall matters more than precision for this problem: missing a toxic or profane message that is then viewed by potentially thousands of people on a livestream platform is worse than flagging something for a human to check. The maker also reports early benchmark evidence, 97.6% overall accuracy (41 of 42 cases) and 94.4% accuracy on Indic and romanized text across 14 languages, while noting that the 42-message sample (three per language) should be treated as early evidence rather than a rigorous study. In that set, no benign messages were auto-banned, and the closest thing to a false positive was one Bhojpuri line landing in review instead of an instant allow. Concrete use cases named in the content include chat moderation in real-money gaming, where wrongly muted or banned players carry their own support cost, and livestream platform chat, where a missed toxic message can be seen by thousands of viewers at once. More broadly, because the product is a Node.js package, it fits any backend that receives user-generated text and needs a moderation decision before it acts, with the option to route uncertain cases to human review rather than taking automatic action on ambiguous content. The primary audience is developers building chat or user-generated content systems in Node.js, especially teams operating in multilingual environments that include Indic languages. The package is installed with 'npm i gg-friggin-ez', the source lives on GitHub, and a demo is hosted on GitHub Pages. It is 100% free and open source, and the maker lists GitHub Copilot and Jev among the tools used by the launch team. Because the engine is pluggable, a team can start with the default Jev engine and later point the pipeline at self-hosted open-source models to reach $0 inference cost. In short, gg-friggin-ez packages an evasion-aware, multilingual, context-based profanity and toxicity screener into a free, drop-in Node.js package. Its value proposition is the combination of sub-500ms latency, ultra-low per-message cost, deterministic ALLOW/REVIEW/CENSOR/BAN actions, and telemetry that explains every decision, so that moderation can run on every message instead of a sample.