Bouncer Feed Filter

by reason-machines2384a003145aNo license83 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 3 months ago

AI-powered browser extension that filters unwanted posts from Twitter/X feeds using natural language rules and multiple AI backends

AI-generated overview

Documents the Bouncer browser extension, which uses AI models to filter unwanted Twitter/X posts by natural-language rules.

What it does
This skill is reference documentation for Bouncer, a Chrome/Edge/iOS browser extension that classifies and hides Twitter/X feed posts matching user-defined plain-language filters. It covers building the extension from source, configuring AI backends (local WebGPU/WebLLM, OpenAI, Gemini, Anthropic, OpenRouter, Imbue), the content-script and adapter architecture, classification prompts, result caching, filter storage, and troubleshooting. It produces guidance and code examples rather than a runnable artifact of its own.
When to use it
Use it when you need to build, configure, extend, or debug the Bouncer extension, add a new AI provider, or understand how it observes feeds and classifies posts. It suits developers working on this specific extension rather than general content filtering.
Requirements
The documented extension requires Node.js and npm for building, a Chromium-based browser with developer mode, and API keys for cloud providers; local models need WebGPU support and a multi-gigabyte model download. The skill itself ships no scripts and is instructions only.

Bouncer Feed Filter

Skill by ara.so — Daily 2026 Skills collection.

Bouncer is a browser extension (Chrome/Edge/iOS) that uses AI to filter unwanted posts from Twitter/X feeds in real time. Users define filters in plain language ("crypto", "engagement bait", "rage politics"), and Bouncer classifies and hides matching posts using AI models — local (WebGPU via WebLLM) or cloud (OpenAI, Gemini, Anthropic, OpenRouter, Imbue).

Repository Structure

Bouncer/                  # Main extension source  src/    background/           # Service worker / background scripts    content/              # Content scripts (Twitter DOM interaction)    popup/                # Extension popup UI    adapters/             # Site adapters (Twitter/X)    models/               # AI backend integrations    utils/                # Shared utilities  icons/                  # Extension icons  manifest.json           # Chrome extension manifest  package.json  tsconfig.json

Installation & Build

From Source (Chrome/Edge)

bash
git clone https://github.com/imbue-ai/bouncer.gitcd bouncer/Bouncernpm installnpm run build

Load in Chrome:

  1. Go to chrome://extensions
  2. Enable Developer mode
  3. Click Load unpacked → select Bouncer/ folder
  4. Navigate to twitter.com or x.com

Development Build (watch mode)

bash
cd Bouncernpm run dev        # watch mode with hot rebuild

Production Build

bash
npm run build      # outputs to Bouncer/dist or inline

AI Backend Configuration

Bouncer supports multiple providers. Configure via the extension popup Settings panel.

Provider / Model Matrix

ProviderModel IDsAuth
Local WebGPUQwen3-4B, Qwen3.5-4B, Qwen3.5-4B VisionNone
OpenAIGPT-5 Nano, gpt-oss-20bAPI key
Google Gemini2.5 Flash Lite, 2.5 Flash, 3 Flash PreviewAPI key
AnthropicClaude Haiku 4.5API key
OpenRouterNemotron Nano 12B VL, Ministral 3BAccount token
ImbueDefaultNone (built-in)

API keys are stored in Chrome's chrome.storage.local — never hardcoded.

Core Architecture

1. MutationObserver — Content Script

The content script watches the Twitter feed for new posts:

typescript
// src/content/feedObserver.tsconst observer = new MutationObserver((mutations) => {  for (const mutation of mutations) {    for (const node of mutation.addedNodes) {      if (node instanceof HTMLElement) {        const post = extractPost(node);        if (post) classifyAndFilter(post);      }    }  }});
observer.observe(document.body, { childList: true, subtree: true });

2. Post Extraction — Twitter Adapter

typescript
// src/adapters/twitter.tsexport interface ExtractedPost {  id: string;  text: string;  authorHandle: string;  imageUrls: string[];  element: HTMLElement;}
export function extractPost(element: HTMLElement): ExtractedPost | null {  const article = element.querySelector('article[data-testid="tweet"]');  if (!article) return null;
  const tweetText = article.querySelector('[data-testid="tweetText"]')?.textContent ?? '';  const handle = article.querySelector('[data-testid="User-Name"] a')?.getAttribute('href') ?? '';  const images = [...article.querySelectorAll('img[src*="pbs.twimg.com/media"]')]    .map(img => (img as HTMLImageElement).src);
  return {    id: article.closest('[data-testid]')?.getAttribute('data-testid') ?? crypto.randomUUID(),    text: tweetText,    authorHandle: handle.replace('/', ''),    imageUrls: images,    element: article as HTMLElement,  };}

3. Classification Request

typescript
// src/models/classify.tsexport interface ClassificationResult {  filtered: boolean;  matchedCategory: string | null;  reasoning: string;}
export async function classifyPost(  post: ExtractedPost,  filters: string[],  model: ModelConfig): Promise<ClassificationResult> {  const prompt = buildClassificationPrompt(post.text, filters, post.imageUrls);  const response = await model.provider.complete(prompt);  return parseClassificationResponse(response);}
function buildClassificationPrompt(  text: string,  filters: string[],  imageUrls: string[]): string {  return `You are a content filter. Given a social media post, determine if it matches any of the user's filter categories.
Filter categories: ${filters.map(f => `"${f}"`).join(', ')}
Post text:${text}
${imageUrls.length > 0 ? `The post contains ${imageUrls.length} image(s).` : ''}
Respond with JSON:{  "filtered": boolean,  "matchedCategory": "category name or null",  "reasoning": "brief explanation"}`;}

4. Hiding Filtered Posts

typescript
// src/content/filterUI.tsexport function hidePost(element: HTMLElement, reason: string): void {  element.style.transition = 'opacity 0.3s ease-out';  element.style.opacity = '0';  setTimeout(() => {    element.style.display = 'none';    element.dataset.bouncerFiltered = 'true';    element.dataset.bouncerReason = reason;  }, 300);}
export function showFilteredIndicator(count: number): void {  const indicator = document.getElementById('bouncer-filtered-count');  if (indicator) indicator.textContent = `${count} filtered`;}

Adding a New AI Provider

typescript
// src/models/providers/myProvider.tsimport type { ModelProvider, CompletionRequest, CompletionResponse } from '../types';
export class MyProvider implements ModelProvider {  private apiKey: string;  private endpoint = 'https://api.myprovider.com/v1/chat/completions';
  constructor(apiKey: string) {    this.apiKey = apiKey;  }
  async complete(request: CompletionRequest): Promise<CompletionResponse> {    const response = await fetch(this.endpoint, {      method: 'POST',      headers: {        'Authorization': `Bearer ${this.apiKey}`,        'Content-Type': 'application/json',      },      body: JSON.stringify({        model: request.model,        messages: [{ role: 'user', content: request.prompt }],        max_tokens: 256,      }),    });
    const data = await response.json();    return {      text: data.choices[0].message.content,      usage: data.usage,    };  }}

Register it in the provider registry:

typescript
// src/models/registry.tsimport { MyProvider } from './providers/myProvider';
export function createProvider(config: StoredConfig): ModelProvider {  switch (config.provider) {    case 'my-provider':      return new MyProvider(config.apiKey);    // ... other cases  }}

Result Caching

Bouncer caches classification results so repeated posts don't trigger new inference calls:

typescript
// src/utils/cache.tsconst CACHE_KEY = 'bouncer-post-cache';
export async function getCachedResult(postId: string): Promise<ClassificationResult | null> {  const stored = await chrome.storage.local.get(CACHE_KEY);  const cache = stored[CACHE_KEY] ?? {};  return cache[postId] ?? null;}
export async function cacheResult(postId: string, result: ClassificationResult): Promise<void> {  const stored = await chrome.storage.local.get(CACHE_KEY);  const cache = stored[CACHE_KEY] ?? {};  cache[postId] = result;  // Limit cache size  const keys = Object.keys(cache);  if (keys.length > 1000) delete cache[keys[0]];  await chrome.storage.local.set({ [CACHE_KEY]: cache });}

Filter Management

Filters are stored and retrieved via chrome.storage.sync:

typescript
// src/utils/filters.tsexport async function getFilters(): Promise<string[]> {  const result = await chrome.storage.sync.get('bouncerFilters');  return result.bouncerFilters ?? [];}
export async function addFilter(topic: string): Promise<void> {  const filters = await getFilters();  if (!filters.includes(topic)) {    await chrome.storage.sync.set({ bouncerFilters: [...filters, topic] });  }}
export async function removeFilter(topic: string): Promise<void> {  const filters = await getFilters();  await chrome.storage.sync.set({    bouncerFilters: filters.filter(f => f !== topic),  });}

Local WebGPU Models (WebLLM)

Local models run entirely in-browser via WebGPU — zero data sent externally:

typescript
// src/models/providers/webllm.tsimport { CreateMLCEngine, type MLCEngine } from '@mlc-ai/web-llm';
let engine: MLCEngine | null = null;
export async function loadLocalModel(modelId: string, onProgress?: (p: number) => void): Promise<void> {  engine = await CreateMLCEngine(modelId, {    initProgressCallback: (report) => onProgress?.(report.progress),  });}
export async function localComplete(prompt: string): Promise<string> {  if (!engine) throw new Error('Local model not loaded');  const response = await engine.chat.completions.create({    messages: [{ role: 'user', content: prompt }],    max_tokens: 256,  });  return response.choices[0].message.content ?? '';}

Chrome Extension Manifest Key Points

json
{  "manifest_version": 3,  "permissions": ["storage", "activeTab", "scripting"],  "host_permissions": ["https://twitter.com/*", "https://x.com/*"],  "background": { "service_worker": "background.js" },  "content_scripts": [{    "matches": ["https://twitter.com/*", "https://x.com/*"],    "js": ["content.js"],    "run_at": "document_idle"  }]}

Troubleshooting

ProblemFix
Extension not loadingEnsure npm run build completed without errors; reload unpacked extension
Posts not being filteredCheck that filters are saved in popup; open DevTools on x.com and check console for errors
API key errorsVerify key is stored via Settings panel, not hardcoded; check provider dashboard for quota
Local model not loadingBrowser must support WebGPU (chrome://flags/#enable-unsafe-webgpu); first load downloads model (~2-4GB)
Filtered count not updatingMutationObserver may have detached; reload the page
TypeScript errors on buildRun npm install to ensure all types are present; check tsconfig.json target is ES2020+

Common Patterns

Check if a post should be processed (before API call):

typescript
async function classifyAndFilter(post: ExtractedPost): Promise<void> {  // Skip if already processed  if (post.element.dataset.bouncerProcessed) return;  post.element.dataset.bouncerProcessed = 'true';
  // Check cache first  const cached = await getCachedResult(post.id);  if (cached) {    if (cached.filtered) hidePost(post.element, cached.reasoning);    return;  }
  const filters = await getFilters();  if (filters.length === 0) return;
  const config = await getModelConfig();  const result = await classifyPost(post, filters, config);
  await cacheResult(post.id, result);  if (result.filtered) hidePost(post.element, result.reasoning);}

Storing API key securely (popup UI):

typescript
// Never log or expose the key — store only via chrome.storage.localasync function saveApiKey(provider: string, key: string): Promise<void> {  await chrome.storage.local.set({ [`${provider}_api_key`]: key });}
async function getApiKey(provider: string): Promise<string> {  const result = await chrome.storage.local.get(`${provider}_api_key`);  return result[`${provider}_api_key`] ?? '';}

Source and attribution

Source:reason-machines/trending-skillsinskills/bouncer-feed-filterat commit2384a00

License: No license

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