InvinOS
  • 🛡️ Invinos Documentation
  • 📌 Introduction to Invinos
  • 🌐 Why Invinos Exists
  • 🧱 System Architecture Overview
    • Core Design Layers
      • Network Layer
      • Browser Layer
      • Wallet & Asset Layer
      • Computation Layer (AI)
      • Identity Layer
      • Interface & Dashboard Layer
    • Interface & Dashboard Layer
    • Mobile-First Optimization
  • 🧠 Core App Modules
    • Stealth Browser
    • Privacy VPN
    • zkMixer
    • Ghost Wallets
    • Anonymous DEX
    • Local LLM
    • Context Lock
    • Privacy Dashboard
    • zk-Identity
    • Governance
  • 🔄 Workflow: Real User Actions
    • Private Browse Flow
    • Crosschain zkMixer Flow
    • Encrypted AI Interaction Flow
    • Full Privacy Session Example
    • Token Utility and Access Flow
  • 🔐 Who use Invinos?
    • DeFi Traders Avoiding Wallet Tracking
    • Journalists Operating Under Surveillance
    • Mobile Users Seeking Default Privacy
    • Builders and Devs Testing Encrypted Flows
    • Community Contributors Managing DAO Work Privately
  • 💸 VINOS Tokenomics
    • Supply and Distribution
    • Platform Fee Structure and Holder Tiers
    • Governance and Protocol Evolution
    • Sustainability Through Utility
  • 🧭 Invinos Roadmap 2025.
    • Foundation Layer (Q2 2025)
    • Utility Expansion (Q3 2025)
    • Ecosystem Launch (Q4 2025)
  • 🤝 Get Involved
Powered by GitBook
On this page
  1. 🧠 Core App Modules

Local LLM

Artificial intelligence is one of the most powerful tools available to users — but it comes with major privacy trade-offs when used via cloud APIs. Every prompt submitted, question asked, or reply received is often stored, analyzed, or reused by the service provider.

Invinos eliminates this surveillance by embedding local language models that run fully offline. These models do not connect to external servers, do not require internet access, and do not log user activity. Users can generate text, translate content, summarize documents, or ask questions in complete privacy.

Each model runs inside a sandbox, and no prompt is saved after inference. When the app closes, all AI session data is wiped. This allows for powerful natural language reasoning — without putting user thoughts, ideas, or patterns into external hands.

PreviousAnonymous DEXNextContext Lock

Last updated 3 days ago