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\n\n\"gg-friggin-ez\n\n

gg-friggin-ez

\n\n

Fast, drop-in profanity and toxicity screener for Node.js, powered by TypeSafe AI Jev.\n\n

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\n\nFast. Cheap. Catches the friggin crap.\n\n

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\n Live Demo |\n Quick Start |\n Create Custom Screener |\n Install |\n The Problem & The Solution | \n Custom Schemas |\n Demos\n

\n\n[![npm version](https://img.shields.io/npm/v/gg-friggin-ez?style=flat-square)](https://www.npmjs.com/package/gg-friggin-ez)\n[![License](https://img.shields.io/badge/license-MIT-blue?style=flat-square)](LICENSE)\n[![Product Hunt](https://img.shields.io/badge/Product%20Hunt-View%20Launch-DA552F?style=flat-square&logo=producthunt&logoColor=white)](https://www.producthunt.com/posts/gg-friggin-ez/)\n\n\"gg-friggin-ez\n\n⭐ _Help us reach more developers and grow the community. Star this repo!_\n\n
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\n \"gg-friggin-ez\n

\n\nWorks across languages and scripts, with zero training required.\n\nReal-world chat isn't clean. Users switch scripts, write regional languages in Latin characters, add spaces between letters, and turn profanity into leetspeak. **gg-friggin-ez** is built for exactly these cases - detecting romanized profanity, code-mixed text, leetspeak, and character spacing across Indic languages like Bengali, Malayalam, Hindi, Tamil, Telugu, Kannada, etc.\n\nAll at ~50-500ms latency, making it suitable for real-time chat and game moderation without reaching for a general-purpose LLM.\n\n## The Problem\n\nStatic keyword denylists and generic moderation APIs fail on real-world user-generated content (social posts, comments, reviews, live chat, and in game text comms):\n\n- **Evasion & Leetspeak**: Common obfuscation tricks bypass static keyword denylists.\n\n| Technique | Examples |\n| -------------------------- | ------------------------------------------------------------------------------------------- |\n| **Symbol / number swaps** | `$hitt3r` · `p00da` · `b$dk` · `m1erda` · `c0nnard` · `傻x / c*m` · `死○ね / sh1ne` |\n| **Spaced characters** | `p a i t h i y a m` · `b s d k` · `m i e r d a` |\n| **Character stretching** | `looooosuuu` · `paaaagal` · `菜逼逼逼` · `死ねぇぇぇ` |\n| **Mixed-script** | `chuतिya` · Latin + native-script characters |\n| **ASCII / visual evasion** | Hostile gestures rendered with punctuation, e.g. ASCII middle fingers, that even LLMs miss. |\n\n> Examples span English, Tamil, Hindi, Spanish, French, Chinese, Japanese, and other languages.\n\n- **Romanized & Code-Mixed Languages**: Transliterated text has no standard spelling and freely blends multiple languages.\n\n| Language | Example | Variations |\n| ---------------------- | ---------------------------------------------------------- | --------------------------------------------- |\n| **English** | you are an absolute piece of sh1t, stop talking | `shit` · `$hit` · `sh*t` |\n| **Hindi / Hinglish** | abe chutiya chup kar na | `chootiya` · `chutya` · `chuतिya` · `c***iya` |\n| **Tamil / Tanglish** | nee oru pooda paithiyakaara da | `p00da` · `puda` · `puuda` |\n| **Kannada / Kanglish** | nin huccha naye tara adtiya | `huchcha` · `hu$ha` · `hucha` |\n| **Japanese / Romaji** | omae hontou ni shine yo gomi | `shiИe` · `sh1ne` · `死ね` · `死○ね` |\n\n- **Regional sparsity:** Non-Hindi regional languages suffer from limited training data and dialectal variation, challenging keyword lists and off-the-shelf classifiers ([DravidianCodeMix](https://doi.org/10.1007/s10579-022-09583-7)).\n\n- **Identity-Based Hate Speech:** Regional caste, gender, and religious harassment can be difficult for English-centric moderation systems to detect, especially in transliterated or code-mixed text - e.g. _\"kitchen me jaake khana bana ladki, games tere bas ka nahi\"_.\n\n- **False-Positive Traps**: Harmless friendly banter, cultural idioms, or casual slang get wrongly flagged as severe abuse by blunt keyword matchers.\n- **LLMs are slow & expensive:** General-purpose LLMs can take 1-3s just to produce the first token, making real-time moderation slow and costly at scale.\n\n## The Solution\n\n`gg-friggin-ez` is built around **System 1 decision models** - reflex-speed classifiers that return calibrated probabilities instead of generating conversational text. It ships with **TypeSafe AI Jev** configured as the default model, but Jev isn't hardcoded: swap in a different System 1 classifier, or bring your own via the [`system1` option](#bring-your-own-system-1-model).\n\nWith Jev as the default model:\n\n- **Reflex Speed**: ~50-500ms end-to-end response time.\n- **Low Cost**: $0.042 / 1M input tokens, and no charge for output tokens.\n- **Evasion-Aware**: Handles common obfuscation patterns including leetspeak, character spacing, romanization, and code-mixing.\n- **Multilingual**: Supports English and Indic languages, including romanized/transliterated input.\n- **Drop-In & Pluggable**: Simple Node.js API (`isProfane()`, `isToxic()`, `screen()`), with built-in screening rules, or bring your own custom schema - and your own System 1 model.\n- **Human Review Path**: Ambiguous/context-dependent cases can be routed for human review rather than forcing a binary decision.\n\n## Install\n\n```bash\nnpm install gg-friggin-ez\n```\n\n> Requires Node.js `18+` (or Bun). Ships as both ESM and CommonJS with bundled TypeScript types.\n\n
\nOther package managers\n\n```bash\nyarn add gg-friggin-ez\n```\n\n```bash\npnpm add gg-friggin-ez\n```\n\n```bash\nbun add gg-friggin-ez\n```\n\n
\n\n## Quick Start\n\n```ts\nimport { isProfane, isToxic } from \"gg-friggin-ez\";\n\nprocess.env.OPENROUTER_API_KEY = \"sk-or-...\"; // or pass { apiKey } to createScreener() instead\n\n// Fast boolean convenience checks\nconst profane = await isProfane(\"you are absolute dog sh1t\"); // true\nconst toxic = await isToxic(\"you are completely brainless and useless\"); // true\n\n// Works with transliterated, code-mixed, and obfuscated text\nconst indic = await isProfane(\"Nee oru p00da paithiyakaara da, 5colo nadatha\"); // true\n```\n\nOr `require()` it from plain CommonJS Node:\n\n```js\nconst { isProfane, isToxic } = require(\"gg-friggin-ez\");\n\nconst bad = await isProfane(\"you are absolute dog sh1t\");\n```\n\n> [!TIP]\n> **Performance tip**: `isProfane()` and `isToxic()` each trigger an inference request. If you need multiple moderation signals, call `screen()` once.\n\n### Full detail\n\n```ts\nimport { screen } from \"gg-friggin-ez\";\n\n// Multi-vector adversarial evasion: spacing, leetspeak, repeated chars, symbols, & mixed script\nconst result = await screen(\n \"Nee oru p 0 0 d a daaaa, b*dk chuतिya 5colo nadatha\",\n);\n\nconsole.log(result);\n// {\n// isProfane: true,\n// isToxic: true,\n// severity: \"SEVERE\",\n// severityScore: 1.95,\n// language: \"tamil\",\n// obfuscationType: \"mixed_script\",\n// obfuscationTypes: [\n// \"mixed_script\",\n// \"spaced_characters\",\n// \"leetspeak\",\n// \"repeated_characters\",\n// ],\n// action: \"AUTO_BAN\",\n// }\n```\n\n`screen()`, `isProfane()`, and `isToxic()` all accept the same optional second argument - see [Create custom screener](#create-custom-screener) below for the full list of what you can configure and pass in. `screen()` returns the complete moderation result:\n\n- `isProfane` - `true` / `false` - explicit profanity or slurs\n- `isToxic` - `true` / `false` - hostility, harassment, or personal attacks\n- `severity` - `\"NONE\"` / `\"MILD\"` / `\"SEVERE\"`\n- `severityScore` - continuous `0.0-2.0` score, e.g. `1.95`\n- `language` - detected language, e.g. `\"tamil\"`\n- `obfuscationType` - primary evasion technique, e.g. `\"mixed_script\"`\n- `obfuscationTypes` - all detected evasion techniques, e.g. `[\"mixed_script\", \"spaced_characters\", \"leetspeak\", \"repeated_characters\", \"symbol_substitutions\"]`\n- `action` - `\"ALLOW\"` / `\"SUSPICIOUS_REVIEW\"` / `\"AUTO_CENSOR\"` / `\"AUTO_BAN\"`\n- `latencyMs` - actual measured end-to-end request latency (no artificial adjustment)\n- `costUsd` - inference cost in USD, derived from the provider's reported cost or actual token usage; `undefined` if neither is available (never a fabricated estimate)\n\n> [!NOTE]\n> **Where does `action` actually come from?** The configured System 1 model (Jev by default) only returns calibrated probabilities/scores for toxicity, profanity, severity, language, and obfuscation type - it never decides an outcome. `gg-friggin-ez` then applies deterministic policy thresholds on the client side to turn those probabilities into `ALLOW` / `SUSPICIOUS_REVIEW` / `AUTO_CENSOR` / `AUTO_BAN`. This keeps the AI model narrowly scoped to classification while your application (or this library's default thresholds) owns the moderation policy.\n\n### Visual Evasion & ASCII Art Screening\n\nStandard keyword denylists and traditional NLP models process text as 1D token streams, and may miss 2D ASCII drawings. `gg-friggin-ez` detects ASCII-art evasion as a distinct moderation signal.\n\n```ts\nconst asciiMiddleFinger = `\n....................../´¯/) \n....................,/¯../ \n.................../..../ \n............./´¯/'...'/´¯¯\\`·¸ \n........../'/.../..../......./¨¯\\\\ \n........('(...´...´.... ¯~/'...') \n.........\\\\.................'...../ \n..........''...\\\\.......... _.·´ \n............\\\\..............( \n..............\\\\.............\\\\...\n`;\n\nconst res = await screen(asciiMiddleFinger);\n\nconsole.log(res.obfuscationType); // \"ascii_art\"\nconsole.log(res.obfuscationTypes); // [\"ascii_art\"]\nconsole.log(res.severityScore); // 1.30\nconsole.log(res.action); // \"SUSPICIOUS_REVIEW\"\n```\n\n### Create custom screener\n\n```ts\nimport { createScreener, DEFAULT_TOXICITY_QUESTIONS } from \"gg-friggin-ez\";\n\nconst screener = createScreener({\n apiKey: \"sk-or-...\", // default: OPENROUTER_API_KEY, TYPESAFE_API_KEY, or JEV_KEY env var\n system1: undefined, // swap the whole System 1 model (endpoint + model id + pricing); see \"Bring your own System 1 model\"\n baseUrl: undefined, // override just the endpoint (rarely needed - use `system1` instead)\n model: undefined, // override just the model id (rarely needed - use `system1` instead)\n timeoutMs: 15000, // per-request timeout in ms\n retries: 2, // retries after the initial attempt, for transient errors only (clamped to >= 0)\n thresholds: { review: 0.35, censor: 0.6, ban: 0.75 }, // action decision thresholds\n questions: DEFAULT_TOXICITY_QUESTIONS, // Jev question schema; see \"Custom schemas\"\n});\n\n// thresholds and questions can also be passed per-call, overriding the screener's\n// defaults for that call only - the rest of the options above are constructor-only.\nawait screener.screen(\"some borderline text\", {\n thresholds: { censor: 0.5 },\n questions: DEFAULT_TOXICITY_QUESTIONS,\n});\n```\n\n| Option | Type | Default | Per-call override |\n| ------------ | ----------------------------- | ------------------------------------------------------------- | ----------------- |\n| `apiKey` | `string` | `OPENROUTER_API_KEY` / `TYPESAFE_API_KEY` / `JEV_KEY` env var | No |\n| `system1` | `System1ModelConfig` | Jev (auto-picks TypeSafe or OpenRouter based on the key) | No |\n| `baseUrl` | `string` | from the active `system1` model | No |\n| `model` | `string` | from the active `system1` model | No |\n| `timeoutMs` | `number` | `15000` | No |\n| `retries` | `number` | `2` | No |\n| `thresholds` | `{ review?, censor?, ban? }` | `{ review: 0.35, censor: 0.6, ban: 0.75 }` | Yes |\n| `questions` | `Record` | `DEFAULT_TOXICITY_QUESTIONS` | Yes |\n\n> [!NOTE]\n> Every option lives on `createScreener()`. `screen()`, `isProfane()`, and `isToxic()` accept the same `thresholds` and `questions` as a second argument, scoped to that one call only.\n\n### Configurable policy thresholds\n\nYou can customize the decision action thresholds per-call or across the shared screener.\n\n```ts\nconst result = await screen(\"some borderline text\", {\n thresholds: {\n review: 0.3, // route to review at >= 30% confidence\n censor: 0.55, // auto-censor at >= 55%\n ban: 0.7, // auto-ban at >= 70%\n },\n});\n```\n\n### Retries & timeouts\n\n```ts\nconst screener = createScreener({\n timeoutMs: 10000, // per-request timeout in ms (default: 15000)\n retries: 2, // retries *after* the initial attempt (default: 2 → up to 3 total attempts)\n});\n```\n\nOnly transient failures are retried - HTTP `408`/`429`/`5xx`, network errors, and timeouts - with backoff between attempts. Non-transient errors (e.g. `401 Unauthorized`, `403 Forbidden`, `404 Not Found`) fail immediately instead of being retried. `retries` is clamped to a minimum of `0` (one attempt, no retries).\n\n### API key configuration\n\n`gg-friggin-ez` reads the API key from environment variables (in order): `OPENROUTER_API_KEY`, `TYPESAFE_API_KEY`, `JEV_KEY`. You can also pass it explicitly to `createScreener()`.\n\n```ts\nimport { createScreener } from \"gg-friggin-ez\";\n\nconst screener = createScreener({ apiKey: \"sk-or-...\" });\n```\n\nIf no key is configured, `screen()` throws an error - a real API key is required to reach Jev. (The browser demo in [`demo/`](./demo) has its own offline heuristic fallback for exploring the UI without a key, but the `gg-friggin-ez` package itself does not.)\n\n### Custom schemas\n\nBring your own Jev question schema - useful for a different language family, domain, or moderation policy. Set it once on a screener instance, or override it for a single call.\n\n```ts\nimport { createScreener, isProfane } from \"gg-friggin-ez\";\n\n// Per-instance: every screen() call on this screener uses myCustomQuestions\nconst screener = createScreener({\n apiKey: \"sk-or-...\",\n questions: myCustomQuestions, // same shape as DEFAULT_TOXICITY_QUESTIONS\n});\n\nconst result = await screener.screen(\"some text\");\n\n// Per-call: override the schema for a single check without a dedicated instance\nawait isProfane(\"some text\", { questions: myCustomQuestions });\n```\n\n### Bring your own System 1 model\n\n`gg-friggin-ez` ships with Jev (TypeSafe AI) configured as the default System 1 decision model, but the client isn't hardwired to it. Every provider detail - endpoint, model id, and published pricing - lives in one place ([`src/system1/models.ts`](./src/system1/models.ts)), so you can point the screener at a different System 1 classifier, or even a plain LLM that returns the same `{ noul, choice, score, usage }` shape, without touching any request/parsing logic:\n\n```ts\nimport { createScreener, BUNDLED_SYSTEM1_MODELS } from \"gg-friggin-ez\";\n\n// Example 1: Use pre-bundled local Laya (ConvAI Innovations)\n// Run Laya via Python (`pip install laya`) behind an HTTP endpoint\nconst screener = createScreener({\n system1: BUNDLED_SYSTEM1_MODELS.layaLocal, // http://localhost:8000/v1/decisions\n});\n\n// Example 2: Use custom System 1 endpoint\nconst customScreener = createScreener({\n apiKey: \"your-provider-api-key\",\n system1: {\n id: \"my-custom-model\",\n baseUrl: \"https://my-provider.example.com/v1/decisions\",\n model: \"my-model-id\",\n pricing: { inputPerMillionUsd: 0.1 }, // optional - omit if the provider reports usage.cost\n },\n});\n```\n\n> [!NOTE]\n> Hugging Face model repository URLs (such as `https://huggingface.co/convaiinnovations/laya`) host raw model weights and documentation, not HTTP JSON decision endpoints. To use open-source models like Laya, serve the model locally or in your VPC (for example with FastAPI calling `laya.predict(state, questions)`), then pass that server's decision URL in `baseUrl`.\n\n
\nSelf-hosting Laya with FastAPI (Python code & steps)\n\n#### 1. Install dependencies\n\n```bash\npip install fastapi uvicorn laya\n```\n\n#### 2. Create the decision server (`server.py`)\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom typing import Dict, Any, Optional\nimport laya\n\napp = FastAPI()\n\n# Load English (laya) or multilingual (laya-multilingual for Indic languages)\nagent = laya.load(\"convaiinnovations/laya-multilingual\")\n\nclass DecisionRequest(BaseModel):\n model: Optional[str] = \"convaiinnovations/laya\"\n state: Dict[str, Any]\n questions: Dict[str, Any]\n\n@app.post(\"/v1/decisions\")\nasync def handle_decisions(req: DecisionRequest):\n answers = agent.predict(req.state, req.questions)\n return {\n \"answers\": answers,\n \"usage\": {\n \"input_tokens\": len(req.state.get(\"text\", \"\")),\n \"cost\": 0.0,\n }\n }\n\nif __name__ == \"__main__\":\n import uvicorn\n uvicorn.run(app, host=\"0.0.0.0\", port=8000)\n```\n\n#### 3. Start the server\n\n```bash\npython server.py\n```\n\n#### 4. Connect `gg-friggin-ez`\n\n```typescript\nimport { createScreener, BUNDLED_SYSTEM1_MODELS } from \"gg-friggin-ez\";\n\nconst screener = createScreener({\n system1: BUNDLED_SYSTEM1_MODELS.layaLocal, // http://localhost:8000/v1/decisions\n});\n\nconst result = await screener.screen(\"Teri maa ki c**t\");\nconsole.log(result.action); // AUTO_BAN or AUTO_CENSOR\nconsole.log(result.costUsd); // 0 (self-hosted)\n```\n\n
\n\nA custom `system1` config is pinned for the lifetime of the screener instance - calling `setApiKey()` to rotate credentials never resets it back to Jev's defaults. If you only need to point at a different Jev-compatible deployment (e.g. a self-hosted proxy), the simpler `baseUrl`/`model` string options are still supported and behave the same as before.\n\n## Interactive Browser Demos\n\n> **Live Demo:** Try the interactive simulations live in your browser at **[https://itisshikhar.github.io/gg-friggin-ez/](https://itisshikhar.github.io/gg-friggin-ez/)**.\n\nThe repository includes two interactive simulations - **Twitch live chat** and **Valorant text comms** - showcasing real-time code-mixed toxicity detection. Test it directly online on **[GitHub Pages](https://itisshikhar.github.io/gg-friggin-ez/)**, or run it locally with Bun. Add an OpenRouter or TypeSafe AI API key via the **API Keys** modal, or use the built-in offline heuristic evaluator without a key.\n\n### Twitch Live Stream Chat UI\n\n

\n \"Twitch\n
\n Live Twitch chat moderation with AutoMod timeout, language detection, and moderator reveal.\n

\n\n### Valorant Realtime Comms\n\n

\n \"Valorant\n
\n In-game tactical comms HUD with real-time censorship, 3-strike penalty system, and inline telemetry cards.\n

\n\n### Running the demo locally\n\nRequires [Bun](https://bun.sh) (v1.1+).\n\n```bash\ngit clone https://github.com/ItisShikhar/gg-friggin-ez.git\ncd gg-friggin-ez\nbun install\n\nbun run demo\n```\n\nOpen [http://localhost:3000](http://localhost:3000) in your browser.\n\n### Running Tests\n\n```bash\nbun test\n```\n\n### Building the npm package\n\n```bash\nnpm run build\n```\n\nEmits `dist/index.js` (ESM), `dist/index.cjs` (CJS), and `dist/index.d.ts` (types).\n\n## Use cases\n\n`gg-friggin-ez` fits real-time (and passive) text moderation pipelines. Below are illustrative use cases and examples:\n\n- **Games & platforms** - lobby chat, team comms, live game chat (Valorant, Counter-Strike: Global Offensive (CS:GO), Counter-Strike 2 (CS2), League of Legends (LoL)), Discord/Telegram community bots, gaming platforms, and similar online multiplayer or live platforms\n- **Chat & messaging surfaces** - live chat (Twitch chat, TikTok Live comments), group chat, live customer support chat widgets, marketplace buyer-seller chat (Alibaba chat), dating app messaging (Tinder or Hinge chat), and other user-generated messages (Uber user/driver chat), including multilingual and obfuscated abuse\n- **Moderation use cases** - game profanity filter, toxic chat filter, multiplayer chat moderation API, real-time toxicity detection, AI content moderation, automated harassment detection, AI agent/chatbot output screening, and player trust-and-safety tooling for esports and gaming communities\n- **Multilingual & Indic-language support** - works across languages broadly, with especially strong coverage for Indic and code-mixed text - a gap most moderation packages don't handle well: Bengali text censoring, Malayalam chat moderation, Hindi/Hinglish, Tamil/Tanglish, Telugu/Tenglish, Kannada/Kanglish, Marathi/Marlish, Punjabi/Punglish, Gujarati/Gujlish, Bhojpuri, and other code-mixed Indian languages\n\n## How It Compares\n\nHow `gg-friggin-ez` compares on latency, multilingual coverage, and real-world evasion handling.\n\n| Moderation Approach | Latency | Strengths | Trade-offs |\n| :-------------------------------------------------------- | :------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------ |\n| **`gg-friggin-ez`** (default model: TypeSafe AI Jev) | **~50-500ms** | Multilingual, including romanized/transliterated Indic languages (Tamil, Telugu, Kannada, Bengali, Hindi, etc.), evasion tricks (leetspeak, spacing) | Limited conversational context (routes to human review) |\n| **Perspective API** (Google Jigsaw) | ~80-120ms | Monolingual English, Spanish, standard Hindi (`hi`) | Unsupported on Dravidian & regional Indic languages. Scheduled to sunset end of 2026. |\n| **OpenAI Moderation Endpoint** (`omni-moderation-latest`) | ~150-350ms | Multilingual standard text (40 languages supported with major gains in Telugu, Bengali, Marathi) | Fixed 13 harm categories rather than custom policy schemas; higher false-positive rate on casual colloquial banter. |\n| **Custom BERT / FastText** | ~15-30ms | Extremely fast; performance depends on training data | Requires training data + maintenance. |\n| **General LLMs (GPT-4o)** | 1,000-3,500ms | Has multilingual world knowledge | 10x to 30x latency, plus 100x inference cost. |\n\n## Benchmark Results\n\nResults from 42 curated test cases across 14 languages, covering multilingual, romanized, and obfuscated text.\n\n| Metric | `gg-friggin-ez` (default model: Jev) | OpenAI (`omni-moderation`) | Perspective API | Legacy Keyword Filter |\n| :-------------------------------- | :----------------------------------- | :------------------------- | :--------------------------------- | :-------------------- |\n| **Overall Accuracy** | **97.6%** (41 / 42) | 88.1% (37 / 42) | 38.1% (16 / 42) _(18 unsupported)_ | 61.9% (26 / 42) |\n| **Romanized Indic Accuracy** | **94.4%** | 77.8% | 16.7% | 66.7% |\n| **Global Languages Accuracy** | **100%** | 100% | 72.2% | 55.6% |\n| **Obfuscated Evasion Catch Rate** | **100%** | 71.4% | 28.6% | 35.7% |\n| **Inference Cost** | **~$0.000052 / msg** | Free | Free _(sunset 2026)_ | $0.00 |\n\n> _Note on API language coverage:_ According to Google's official [Perspective API documentation](https://github.com/conversationai/perspectiveapi), its `TOXICITY` model officially supports only 18 languages - with standard Hindi (`hi`) and experimental Hinglish (`hi-Latn`) being its only Indic coverage. Tamil, Telugu, Kannada, Bengali, Marathi, and Bhojpuri are unsupported.\n\n> For complete itemized data across all 42 tests, individual scores, and latency logs, see [raw-benchmarks.md](./raw-benchmarks.md).\n\n## Contributing\n\nContributions are welcome, whether they improve performance, fix bugs, add\npresets, components or effects, expand examples and tests, or improve documentation.\nSee [Contributing](CONTRIBUTING.md) before opening a pull request.\n\n## Author\n\nBuilt by [Shikhar Srivastava](https://www.linkedin.com/in/itisshikhar/).\n\n[GitHub](https://github.com/ItisShikhar) ·\n[LinkedIn](https://www.linkedin.com/in/itisshikhar/)\n\n## License\n\ngg-friggin-ez is released under the [MIT License](LICENSE).\n","readmeFilename":"README.md"}