{"slug": "user-friendly-forces-your-privacy-matters", "title": "User-Friendly Forces — Your Privacy Matters", "summary": "A developer built User-Friendly Forces, a Chrome extension that analyzes website privacy policies and returns a 0–100 privacy score, a plain-English three-sentence summary, and color-coded red flags. The tool runs inference locally through Ollama with open-weight models such as llama3.2, llama2 and mistral, falling back to the Gemini API only when Ollama is unavailable, so policy text stays on the user's machine. A local Node.js Express server on localhost:5000 brokers requests, and a health endpoint reports which provider is active.", "body_md": "*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady](https://dev.to/challenges/hacktoberfest-week3-2026-10-19)*\n\nUser-Friendly Forces is a Chrome extension that reads, analyzes, and summarizes website privacy policies — so you never have to sit through walls of legalese again.\n\nYou know the drill: you click \"I agree\" without reading anything because nobody has time for 8,000 words of legal boilerplate at 2 AM. That agreement you just clicked through might allow the company to sell your browsing habits, share your data with 200+ \"partners,\" retain your information indefinitely, or quietly opt you into targeted advertising.\n\nUser-Friendly Forces fixes that by doing the lazy thing smartly. Click the extension on any privacy policy page, and within minutes you get:\n\n📊 A Privacy Score (0–100) — higher means more privacy-friendly\n\n📝 A plain-English 3-sentence summary of what the policy actually says\n\n🚩 Color-coded red flags (🔴 High / 🟡 Medium / 🟢 Low) with specific details like \"Your data is sold to third parties\" or \"Retention period is indefinite\"\n\nThe entire analysis runs on your machine using Ollama — no data sent to any server you don't control. If Ollama isn't available, it gracefully falls back to the Gemini API (with a key you provide).\n\nWho gets to be lazier? Anyone who ever clicked \"Accept\" without reading — which is everyone. Now you can actually know what you agreed to, in 30 seconds instead of 30 minutes.\n\nVideo Demo:\n\nCode Base:\n\nA Chrome extension that analyzes website privacy policies and highlights suspicious patterns — using **local AI (Ollama)** with **Gemini as a cloud fallback**.\n\n```\n[Chrome Extension popup]\n        │ POST /  (prompt + optional Gemini key)\n        ▼\n[Node.js server — index.mjs]\n        │\n        ├─► Ollama  (localhost:11434)   ← primary (free, private)\n        │\n        └─► Gemini API                 ← fallback (if Ollama is offline)\nnpm install\n```\n\nThe only runtime dependency is Node.js ≥18 (uses native `fetch`).\n\n`express` and `cors` are installed via package.json.\n\n`ollama pull llama3`\n`http://localhost:11434`\nYou can change the model by setting the environment variable:\n\n```\nOLLAMA_MODEL=mistral node index.mjs\nGEMINI_API_KEY=AIza… node index.mjs\n```\n\nStack:\n\nChrome Extension (Manifest v3) — the user-facing popup, written in vanilla JS/HTML/CSS with a glassmorphism aesthetic\n\nNode.js backend (index.mjs) — a tiny Express server running locally on localhost:5000\n\nOllama — primary AI inference, running completely locally on your machine\n\nGemini API — cloud fallback, only used if Ollama is unavailable\n\nThe open-source AI layer:\n\nThe extension sends the privacy policy URL to the local Node server. The server uses Ollama's chat API (/api/chat) to run the analysis with whatever open-weight model you have installed — tested with llama3.2, llama2, and mistral. It also handles older Ollama versions by falling back to /api/generate.\n\nThe model is given a strict structured prompt that forces JSON output with a privacy score, plain-English summary, and a list of flags with severity levels. A robust JSON extractor strips any markdown fences or preamble text the model might add before parsing.\n\nThe Gemini fallback tries 5 model names in sequence, skipping any that return 404 (some models aren't available in all regions):\n\n``` js\nconst GEMINI_MODELS = [\n  'gemini-2.0-flash',\n  'gemini-1.5-flash',\n  'gemini-1.5-flash-latest',\n  'gemini-pro',\n  'gemini-1.0-pro',\n];\n```\n\nThe health endpoint (GET /health) lets the popup check provider availability at startup and show a live status badge — green for Ollama ready, yellow for Gemini fallback, red if nothing is available.\n\nPrivacy tools need to be private themselves. That's the core tension with a closed-API approach.\n\nIf I had built this with a hosted OpenAI or Anthropic API, every privacy policy you analyze would pass through their servers. You'd be trusting a third party with the URLs of every privacy page you visit, potentially leaking which services you use, when you use them, and what concerns you enough to check. A tool meant to protect your privacy shouldn't compromise it in the process of doing so.\n\nWith Ollama:\n\n✅ Runs entirely offline — once the model is downloaded, no internet connection required\n\n✅ Zero data leaves your machine — the policy URL, the AI prompt, and the analysis result never touch an external server\n\n✅ Costs exactly $0 to run — indefinitely, for any number of policies\n\n✅ Model-agnostic — swap llama3.2 for mistral, phi3, gemma2, or any future open-weight model that fits your hardware, with no code changes\n\n✅ No API key, no rate limits, no usage quotas — analyze 500 privacy policies in a day if you want\n\nA closed API would have introduced a monthly bill, rate limits, and the irony of sending your privacy-checking queries to a company whose own privacy policy you haven't read. Open inference removes all three problems at once.\n\nThe open approach also lets the community improve it: you could fine-tune a small model specifically on privacy policy datasets (there are several on Hugging Face) to get better, more consistent analysis than a general-purpose model provides. That's simply not possible with a closed API.\n\nThe average privacy policy is 2,500 words and takes ~10 minutes to read properly.\n\nMost people spend 0 minutes reading it and just click \"Accept.\"\n\nUser-Friendly Forces gets you from \"0 minutes of knowledge\" to \"actually informed\" in under 2 minutes, without reading a word.\n\nIn one week of testing across a dozen popular websites:\n\n🔴 3 out of 4 had at least one high-severity flag\n\nThe most common flag: \"Data shared with unspecified third parties\"\n\nWorst score seen: 18/100 (a popular fitness tracking app)\n\nBest score seen: 71/100 (a small open-source developer tool)\n\nThe sloth doesn't read the privacy policy. The sloth makes the AI read it, hangs upside down, and waits for the summary. That's the energy.\n\nBest Use of Gemma — The extension works with Google's open-weight gemma2 model running locally through Ollama, giving users a Google-quality model without any API calls\n\nBest Use of GitHub Copilot — Development workflow used Copilot for iterating on the structured prompt engineering and the JSON extraction logic", "url": "https://wpnews.pro/news/user-friendly-forces-your-privacy-matters", "canonical_source": "https://dev.to/mritunjai/user-friendly-forces-your-privacy-matters-1ig0", "published_at": "2026-10-24 13:07:43+00:00", "updated_at": "2026-10-24 13:12:29.711499+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-tools", "ai-products", "developer-tools"], "entities": ["User-Friendly Forces", "Ollama", "Gemini", "Google", "Chrome", "Node.js", "llama3.2", "mistral"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/user-friendly-forces-your-privacy-matters", "markdown": "https://wpnews.pro/news/user-friendly-forces-your-privacy-matters.md", "text": "https://wpnews.pro/news/user-friendly-forces-your-privacy-matters.txt", "jsonld": "https://wpnews.pro/news/user-friendly-forces-your-privacy-matters.jsonld"}}