Full Stack Dev & Open Source Builder | Building LexGuard AI | Next.js, FastAPI & RAG pipelines

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Sujal Meena  • Peerlist

<h3>Key Features</h3><ul><li>Automatic hibernation: Inactive tabs are suspended after a timeout you choose (5 minutes to 4 hours).</li><li>Smart exclusions: Tabs playing audio, pinned tabs, tabs with unsaved form input, and your active tab are never touched.</li><li>Whitelist: Keep specific sites awake forever (exact domain and sub-domain matching).</li><li>Smart Memory mode: Hibernates faster automatically when your system RAM runs low.</li><li>Battery Saver mode: Only hibernate while on battery power.</li><li>One-click controls: Hibernate All / Wake All right from the toolbar popup.</li><li>Quick-Wake search (Ctrl+Shift+K): Instantly find and restore any tab.</li><li>Restore on focus: Optionally wake a tab the moment you click it.</li></ul><h3>Deep Sleep Stash</h3>Go beyond hibernation. Stash tabs to close them completely and save them for later:<ul><li>Stash Tab: Tuck away a single page.</li><li>Stash Group: Save an entire Chrome tab group (name, color and all tabs) and restore it intact.</li><li>Stash All: Clear every suspended tab at once for a fresh start. Everything reopens from the Stash Board in your dashboard whenever you want.</li></ul><h3>Analytics Dashboard</h3>A clean dashboard shows estimated memory saved, tabs suspended over time, your top hibernated sites, and a 7-day activity chart — plus a built-in guide to using stashing effectively.<h3>Privacy First</h3><ul><li>No accounts, no tracking, no analytics sent anywhere — everything stays on your device.</li><li>Optional tab previews are OFF by default; no screenshots are ever captured unless you turn them on.</li></ul>Note: memory figures are estimates based on typical tab usage and may vary by site.

Tab Hibernator Pro

I built LexGuard AI because most AI legal tools are just standard API wrappers that hallucinate or struggle with specific legislation. With India's DPDP Act 2023 introducing massive penalty risks, companies need deterministic compliance auditing, not guesswork.Here is how the system actually works under the hood:Grounding LLMs in the DPDP Act (RAG Setup)Instead of relying on the LLM's pre-trained knowledge, I built a hybrid RAG pipeline using FAISS and SentenceTransformers (all-MiniLM-L6-v2) in the backend. - Uploaded policies are split into paragraph-sized chunks.- A custom scoring algorithm combines vector similarity, lexical overlap, and rule-based topic boosters (boosting terms like 'consent withdrawal' or 'grievance officer' to retrieve exact sections of the DPDP Act).- This guarantees that the context passed to the LLM is anchored directly to the actual legal provisions. Two-Tier Pipeline &amp; Fail-safes- For quick landing page widget runs, the app uses gemini-2.0-flash for high-speed triage.- For deep dashboard audits, it flags complex clauses and processes them using higher-parameter models.- If Gemini hits quota limits, the backend automatically fails over to Llama-3.3-70b via Groq using a Tenacity-based exponential retry wrapper. Core Auditing Features- Differential Policy Auditing: You can upload old vs. new versions of a privacy policy to track exactly which compliance issues were fixed, which were introduced, and which remain unchanged.- Privacy UX Scorecard: Calculates a readability grade, translates dense legal jargon into plain language, and audits "multilingual readiness" since the DPDP Act mandates regional language notices.- Data Retention &amp; Privacy: The database uses MongoDB TTL indexes to auto-purge uploaded policy files and audit reports once a session expires, aligning with data minimization rules.Tech Stack- Frontend: Next.js 14, Tailwind, shadcn/ui.- Backend: FastAPI, Docker, Docker Compose, FAISS, SentenceTransformers.- Database &amp; Auth: MongoDB Atlas + Supabase.- Payments: Razorpay (with webhook verification).- Deployment: Vercel Edge Serverless Proxies (to handle CORS/routing) and AWS EC2.Feel free to play around with the app or browse the codebase!GitHub: <a target="_blank" rel="noopener noreferrer" class="break-all link-break text-gray-500 hover:underline" href="https://github.com/sujalmeena7/lexguard-ai">https://github.com/sujalmeena7/lexguard-ai</a>

LexGuard AI — Enterprise DPDP Compliance SaaS

Built a distributed incident root-cause analysis platform for DevOps/SRE teams ingesting live Prometheus alerts. → Feedback-aware RAG pipeline (LlamaIndex + ChromaDB) auto-generates root-cause hypotheses — cutting mean triage time by ~70% → Non-blocking FastAPI endpoints sustaining sub-200ms P99 latency under high-throughput alert bursts → Multi-tenancy, JWT auth, rate-limiting; deployed on Render + Vercel with CI/CD via GitHub Actions → Built chaos simulator to stress-test incident detection across microservices Stack: Python · FastAPI · LlamaIndex · ChromaDB · PostgreSQL · Prometheus · Docker · Next.js Live Url - https://sentinel-sre-zeta.vercel.app