diff --git a/README.md b/README.md
index e42e586..e719733 100644
--- a/README.md
+++ b/README.md
@@ -1,85 +1,59 @@
-# OllaHub
+<div align="center">
+  <h1>🧠 OllaHub</h1>
+  <p><strong>Local-first LLM workspace with deep research capabilities and native OS integration.</strong></p>
 
-Local-first LLM workspace with deep research capabilities and native OS integration.
+  <p align="center">
+    <a href="https://www.rust-lang.org/"><img src="https://img.shields.io/badge/Rust-1.77.2-orange.svg?style=flat-square&logo=rust" alt="Rust"></a>
+    <a href="https://tauri.app/"><img src="https://img.shields.io/badge/Tauri-2.9.2-24C8DB.svg?style=flat-square&logo=tauri" alt="Tauri"></a>
+    <a href="https://nextjs.org/"><img src="https://img.shields.io/badge/Next.js-16.0.3-black.svg?style=flat-square&logo=next.js" alt="Next.js"></a>
+    <a href="https://www.typescriptlang.org/"><img src="https://img.shields.io/badge/TypeScript-5.0-blue.svg?style=flat-square&logo=typescript" alt="TypeScript"></a>
+    <a href="LICENSE"><img src="https://img.shields.io/badge/License-Custom-blue.svg?style=flat-square" alt="License"></a>
+  </p>
 
-[![Rust](https://img.shields.io/badge/Rust-1.77.2-orange.svg)](https://www.rust-lang.org/)
-[![Tauri](https://img.shields.io/badge/Tauri-2.9.2-24C8DB.svg)](https://tauri.app/)
-[![Next.js](https://img.shields.io/badge/Next.js-16.0.3-black.svg)](https://nextjs.org/)
-[![TypeScript](https://img.shields.io/badge/TypeScript-5.0-blue.svg)](https://www.typescriptlang.org/)
-[![License](https://img.shields.io/badge/License-Custom-blue.svg)](LICENSE)
-[![Version](https://img.shields.io/badge/Version-0.1.0-blue.svg)](https://github.com/evandrodevbr/OllaHub)
+  <br>
 
-## Overview
+  </div>
 
-OllaHub is a desktop application that orchestrates local Large Language Models (LLMs) through Ollama, providing a private, offline-capable workspace for research, analysis, and conversation. The application executes complex research workflows by decomposing queries, performing parallel web scraping, and synthesizing context from multiple sources before generating responses.
+---
 
-The architecture prioritizes data privacy by keeping all processing local, using SQLite with full-text search for efficient storage, and integrating ONNX Runtime for semantic relevance ranking without external API dependencies.
+## 🌟 Why OllaHub?
 
-## Key Features
+OllaHub is a powerful desktop application that orchestrates local Large Language Models (LLMs) through **Ollama**, providing a private, offline-capable workspace for research, analysis, and conversation. 
 
-### Deep Research Pipeline
+Unlike standard chat interfaces, OllaHub executes **complex research workflows** by decomposing queries, performing parallel web scraping, and synthesizing context from multiple sources before generating responses—all while keeping your data 100% local and private.
 
-The application implements a multi-stage research workflow:
+---
 
-1. **Query Decomposition**: Uses LLM to break complex queries into 3-5 atomic, search-optimized sub-queries
-2. **Parallel Web Scraping**: Executes multiple searches concurrently using headless Chrome and DuckDuckGo metadata API
-3. **Knowledge Base Aggregation**: Collects and deduplicates content from multiple sources
-4. **Context Condensation**: Applies relevance scoring and summarization to fit context within token limits
-5. **Validation**: Verifies knowledge base sufficiency before response generation
+## ✨ Key Features
 
-The pipeline includes safety checks to ensure decomposed queries remain relevant to the original intent, with automatic fallback to the original query if decomposition fails.
+### 🔍 Deep Research Pipeline
+OllaHub doesn't just answer; it researches. Our multi-stage pipeline includes:
+1. **Query Decomposition:** Uses the LLM to break complex queries into 3-5 atomic, search-optimized sub-queries.
+2. **Parallel Scraping:** Executes concurrent searches using headless Chrome and the DuckDuckGo metadata API.
+3. **Knowledge Aggregation:** Collects, deduplicates, and cleans content from multiple sources.
+4. **Context Condensation:** Applies relevance scoring (ONNX embeddings) and summarization to fit context within token limits.
+5. **Validation:** Verifies knowledge base sufficiency before response generation, with safety fallbacks.
 
-### Local-First Architecture
+### 🛡️ Local-First & Privacy-Focused
+* **Ollama Integration:** Direct subprocess management for seamless local model execution.
+* **Local Semantic Search:** Powered by ONNX Runtime (`all-MiniLM-L6-v2` 384-dimensional embeddings) with no external API calls.
+* **Hybrid Storage:** Blazing fast SQLite (FTS5 for full-text search) with JSON fallbacks for backward compatibility and data safety.
 
-- **Ollama Integration**: Direct subprocess management of Ollama for model execution
-- **ONNX Embeddings**: Local semantic similarity using all-MiniLM-L6-v2 (384-dimensional embeddings)
-- **SQLite with FTS5**: Full-text search with virtual tables for session titles and message content
-- **Hybrid Storage**: Dual persistence (SQLite + JSON) with automatic migration and fallback
+### 🔌 MCP (Model Context Protocol) Integration
+Extensible by design. Connect external tools via MCP servers:
+* **Tool Discovery:** Automatic enumeration of available tools from running servers.
+* **Process Management:** Full lifecycle control (start, stop, restart) with health monitoring.
+* **Tool Execution:** Runtime invocation of tools with direct result injection into the LLM context.
 
-### Hybrid Storage System
+### ⚡ Reactive & Customizable UI
+Built with **React 19, Next.js 16, and Tailwind CSS**. Features smooth animations (Framer Motion), accessible primitives (Radix UI/Shadcn), resizable panels, and lightweight state management (Zustand).
 
-The application uses SQLite as the primary storage backend with JSON as a fallback:
+---
 
-- **SQLite (Primary)**: WAL mode for concurrent reads/writes, optimized cache settings, FTS5 virtual tables for search
-- **JSON (Legacy)**: Maintained for backward compatibility and export functionality
-- **Automatic Migration**: Seamless transition from JSON to SQLite with data preservation
+## 🏗️ System Architecture
 
-### Reactive UI
-
-Built with React 19 and Next.js 16, the interface uses:
-
-- **Tailwind CSS**: Utility-first styling with design tokens
-- **Radix UI / Shadcn**: Accessible, headless component primitives
-- **Zustand**: Lightweight state management for settings and UI state
-- **Framer Motion**: Smooth animations for state transitions
-- **Resizable Panels**: Customizable layout with persistent preferences
-
-### MCP (Model Context Protocol) Integration
-
-Supports external tool integration through MCP servers:
-
-- **JSON-RPC Communication**: Bidirectional communication with MCP servers via stdin/stdout
-- **Tool Discovery**: Automatic enumeration of available tools from running servers
-- **Process Management**: Lifecycle management (start, stop, restart) with health monitoring
-- **Tool Execution**: Runtime invocation of MCP tools with result injection into LLM context
-
-### System Monitoring
-
-Real-time hardware monitoring and resource management:
-
-- **GPU Detection**: NVIDIA and AMD GPU identification with utilization tracking
-- **CPU/Memory Stats**: System-wide resource usage via `sysinfo` crate
-- **Model Recommendations**: Hardware-aware model suggestions based on available VRAM/RAM
-
-### Task Scheduler
-
-Cron-based task execution system:
-
-- **Recurring Tasks**: Schedule periodic operations (e.g., knowledge base updates)
-- **Task Queue**: Priority-based execution with retry logic
-- **State Persistence**: Task state survives application restarts
-
-## Architecture
+<details>
+<summary><b>Click to view Architecture Diagram</b></summary>
 
 ```mermaid
 graph TB
@@ -127,232 +101,106 @@ graph TB
     style Bridge fill:#7c3aed,color:#fff
     style Backend fill:#dc2626,color:#fff
     style External fill:#059669,color:#fff
+
 ```
 
-### Data Flow
+</details>
 
-1. **User Query** → Frontend captures input and triggers preprocessing
-2. **Query Preprocessing** → Intent classification, entity extraction, query enrichment
-3. **Deep Research** → Query decomposition → Parallel search → Scraping → Knowledge base aggregation
-4. **Context Condensation** → Relevance scoring (ONNX embeddings) → Summarization → Token optimization
-5. **LLM Generation** → Ollama subprocess with streaming response
-6. **Storage** → SQLite persistence with FTS5 indexing for future retrieval
+---
 
-## Tech Stack
-
-### Core
-
-| Layer | Technology | Version | Purpose |
-|-------|-----------|---------|---------|
-| **Desktop Framework** | Tauri | 2.9.2 | Native desktop app with Rust backend |
-| **Backend Language** | Rust | 1.77.2 | System-level operations, performance-critical paths |
-| **Frontend Framework** | Next.js | 16.0.3 | React-based UI with SSR capabilities |
-| **UI Library** | React | 19.2.0 | Component-based interface |
-| **Language** | TypeScript | 5.0 | Type-safe frontend development |
-
-### Frontend
-
-| Category | Technology | Purpose |
-|----------|-----------|---------|
-| **State Management** | Zustand | 5.0.8 | Settings and UI state |
-| **Styling** | Tailwind CSS | 4.0 | Utility-first CSS framework |
-| **Components** | Radix UI | Latest | Accessible primitives (dialogs, dropdowns, tooltips) |
-| **Components** | Shadcn/ui | Custom | Pre-built component library |
-| **Animations** | Framer Motion | 12.23.24 | Smooth transitions |
-| **Markdown** | react-markdown | 10.1.0 | Message rendering with GFM support |
-| **Syntax Highlighting** | react-syntax-highlighter | 16.1.0 | Code block rendering |
-
-### Backend
-
-| Category | Technology | Purpose |
-|----------|-----------|---------|
-| **Database** | SQLite (rusqlite) | 0.31 | Local data persistence with FTS5 |
-| **Web Scraping** | headless_chrome | 1.0 | Browser automation for content extraction |
-| **HTML Parsing** | scraper | 0.18 | DOM parsing and content extraction |
-| **Text Processing** | readability | 0.3 | Main content extraction from HTML |
-| **HTTP Client** | reqwest | 0.12 | API requests with streaming support |
-| **Async Runtime** | tokio | 1.0 | Async I/O and concurrency |
-| **Task Scheduling** | tokio-cron-scheduler | 0.9 | Cron-based task execution |
-| **System Info** | sysinfo | 0.36.1 | Hardware monitoring |
-| **Embeddings** | ONNX Runtime (ort) | 2.0.0-rc.10 | Local semantic similarity |
-| **Tokenization** | tokenizers | 0.21 | Text tokenization for embeddings |
-| **Array Operations** | ndarray | 0.16 | Numerical computations |
-
-### AI/ML
-
-| Component | Technology | Purpose |
-|-----------|-----------|---------|
-| **LLM Runtime** | Ollama | Local model execution |
-| **Embeddings Model** | all-MiniLM-L6-v2 | 384-dim semantic embeddings |
-| **Embeddings Runtime** | ONNX Runtime | Model inference |
-
-## Getting Started
+## 🚀 Getting Started
 
 ### Prerequisites
 
-- **Node.js**: 20.x or later
-- **Rust**: 1.77.2 or later
-- **pnpm**: Latest version (`npm install -g pnpm`)
-- **Ollama**: [Install Ollama](https://ollama.ai) and ensure it's running on `http://localhost:11434`
+Before you begin, ensure you have the following installed:
 
-### Installation
+* **Node.js**: `v20.x` or later
+* **Rust**: `v1.77.2` or later
+* **pnpm**: Latest version (`npm install -g pnpm`)
+* **[Ollama](https://ollama.ai)**: Must be running locally on `http://localhost:11434`
 
-1. Clone the repository:
+### Installation & Development
+
+1. **Clone the repository:**
 ```bash
-git clone https://github.com/evandrodevbr/OllaHub.git
+git clone [https://github.com/evandrodevbr/OllaHub.git](https://github.com/evandrodevbr/OllaHub.git)
 cd OllaHub
+
 ```
 
-2. Install dependencies:
+
+2. **Install dependencies:**
 ```bash
 pnpm install
+
 ```
 
-3. Run in development mode:
+
+3. **Run in development mode:**
 ```bash
 pnpm tauri dev
+
 ```
 
-This command will:
-- Start the Next.js development server on `http://localhost:3000`
-- Build the Rust backend
-- Launch the Tauri application window
+
+*This starts the Next.js server (`localhost:3000`), builds the Rust backend, and launches the Tauri native window.*
 
 ### Building for Production
 
 ```bash
 pnpm tauri build
-```
-
-The built application will be in `src-tauri/target/release/bundle/`:
-- **Windows**: `.msi` installer
-- **macOS**: `.dmg` or `.app`
-- **Linux**: `.deb`, `.AppImage`, or `.rpm`
-
-### Environment Configuration
-
-The application uses Tauri's built-in configuration. No environment variables are required for basic operation. MCP server configurations are stored in the application data directory and managed through the UI.
-
-## Project Structure
 
 ```
-OllaHub/
-├── app/                    # Next.js app directory (pages)
-│   ├── chat/              # Chat interface
-│   ├── settings/           # Application settings
-│   ├── setup/              # Initial setup wizard
-│   └── tasks/              # Task management
-├── components/             # React components
-│   ├── chat/               # Chat-specific components
-│   ├── settings/           # Settings UI components
-│   ├── setup/              # Setup wizard components
-│   └── ui/                 # Reusable UI primitives (Shadcn)
-├── hooks/                  # React custom hooks
-│   ├── use-chat.ts         # Chat state management
-│   ├── use-deep-research.ts # Research pipeline orchestration
-│   ├── use-mcp-servers.ts  # MCP server management
-│   └── ...
-├── lib/                    # Utility functions
-│   ├── knowledge-base-processor.ts  # Context condensation
-│   ├── contextual-analyzer.ts       # Query analysis
-│   ├── query-enricher.ts            # Query expansion
-│   └── ...
-├── services/               # Service layer
-│   └── webSearch.ts        # Web search abstraction
-├── store/                  # Zustand stores
-│   └── settings-store.ts   # Application settings
-├── src-tauri/              # Rust backend
-│   ├── src/
-│   │   ├── lib.rs          # Main Tauri commands
-│   │   ├── db.rs           # SQLite database layer
-│   │   ├── embeddings.rs   # ONNX embeddings module
-│   │   ├── web_scraper.rs  # Web scraping logic
-│   │   ├── scheduler.rs    # Task scheduling
-│   │   └── ...
-│   └── Cargo.toml          # Rust dependencies
-└── data/                   # Static data
-    ├── models/             # Model metadata JSON files
-    └── prompts/            # LLM prompt templates
+
+*Compiled binaries will be available in `src-tauri/target/release/bundle/` (.msi, .dmg, .app, .deb, etc.).*
+
+---
+
+## 💻 Tech Stack Deep Dive
+
+<div align="center">
+<img src="https://www.google.com/search?q=https://skillicons.dev/icons%3Fi%3Drust,tauri,nextjs,react,ts,tailwind,sqlite" />
+</div>
+
+
+
+
+| Layer | Technologies Used | Purpose |
+| --- | --- | --- |
+| **Core** | Tauri 2.9, Rust 1.77 | Native desktop environment, high-performance system I/O, concurrency (Tokio). |
+| **Frontend** | Next.js 16, React 19, TS | SSR-capable UI, Zustand (State), Framer Motion (Animations), Tailwind/Radix. |
+| **Database** | SQLite (rusqlite 0.31) | FTS5 virtual tables for lightning-fast full-text search and session storage. |
+| **AI / ML** | Ollama, ONNX Runtime | Local LLM execution and 384-dim semantic embeddings (`all-MiniLM-L6-v2`). |
+| **Scraping** | headless_chrome, scraper | Parallel web automation, DOM parsing, and readability extraction. |
+
+---
+
+## 🗺️ Roadmap
+
+* [ ] **Vector Store Integration**: Transition from SQLite BLOB embeddings to a dedicated vector DB (LanceDB or Qdrant).
+* [ ] **Advanced RAG**: Multi-modal retrieval supporting images, PDFs, and local documents.
+* [ ] **Cloud Sync**: Optional E2E encrypted synchronization of chat sessions across devices.
+* [ ] **Custom Plugin System**: Extensible architecture for user-defined research workflows.
+* [ ] **Model Fine-Tuning UI**: Native interface for training custom models on personal knowledge bases.
+
+---
+
+## ⚖️ License & Terms
+
+OllaHub is released under a custom license combining freeware and creditware models. See the [LICENSE](https://www.google.com/search?q=LICENSE) file for full legal terms.
+
+**TL;DR:**
+
+* ✅ **You may:** Use freely for personal/educational/internal use, modify, and fork the project.
+* ✅ **You must:** Give visible credit to **Evandro Fonseca Junior** and link to [evandro.dev.br](https://evandro.dev.br). Keep copyright notices intact.
+* ❌ **You may not:** Sell the software, bundle it in paid products, or monetize it without explicit written permission.
+
+For commercial licensing inquiries, please contact me via [evandro.dev.br](https://evandro.dev.br).
+
+---
+
+<div align="center">
+<p>Built with ❤️ by <a href="https://github.com/evandrodevbr">Evandro Fonseca Junior</a></p>
+</div>
+
 ```
-
-## Design Patterns
-
-### Backend (Rust)
-
-- **Command Pattern**: Tauri commands encapsulate backend operations
-- **State Management**: Arc<Mutex<>> for shared state (browser instances, MCP processes)
-- **Error Handling**: Result<T, E> with custom error types and fallback strategies
-- **Async/Await**: Tokio runtime for concurrent operations (scraping, MCP communication)
-
-### Frontend (TypeScript)
-
-- **Custom Hooks**: Encapsulate complex logic (deep research, chat management)
-- **State Machines**: Step-based workflows (idle → planning → searching → aggregating)
-- **Optimistic Updates**: UI updates before backend confirmation
-- **Streaming**: Real-time token rendering via Tauri events
-
-### Data Layer
-
-- **Repository Pattern**: Database abstraction in `db.rs`
-- **Migration Strategy**: Dual storage (SQLite + JSON) with automatic fallback
-- **Full-Text Search**: FTS5 virtual tables with trigger-based synchronization
-
-## Roadmap
-
-### Planned Features
-
-- **Vector Store Integration**: Replace SQLite BLOB embeddings with dedicated vector database (LanceDB or Qdrant)
-- **Cloud Sync**: Optional encrypted synchronization of chat sessions across devices
-- **Plugin System**: Extensible architecture for custom research workflows
-- **Advanced RAG**: Multi-modal retrieval with image and document support
-- **Model Fine-tuning UI**: Interface for training custom models on user data
-- **Collaborative Features**: Shared workspaces and team collaboration
-
-### Technical Improvements
-
-- **Performance**: Optimize SQLite queries with prepared statements caching
-- **Memory Management**: Implement LRU cache for embeddings model
-- **Error Recovery**: Enhanced retry logic with exponential backoff
-- **Testing**: Comprehensive unit and integration test coverage
-- **Documentation**: API documentation with code examples
-
-## Contributing
-
-Contributions are welcome. Please follow these guidelines:
-
-1. **Code Style**: Follow existing patterns (Rust: `rustfmt`, TypeScript: ESLint)
-2. **Commits**: Use conventional commits (feat:, fix:, docs:, etc.)
-3. **Pull Requests**: Include description of changes and test results
-4. **Issues**: Use GitHub Issues for bug reports and feature requests
-
-## License
-
-OllaHub is released under a custom license that combines freeware, creditware, and non-commercial restrictions. See the [LICENSE](LICENSE) file for full terms.
-
-### Quick Summary
-
-**You may:**
-- Use OllaHub freely for personal, educational, and internal professional use
-- Modify and distribute the Software (with proper attribution)
-- Fork the project and create derivative works (with attribution)
-
-**You must:**
-- Give credit to **Evandro Fonseca Junior** and link to [evandro.dev.br](https://evandro.dev.br)
-- Keep copyright notices and attribution intact
-- Not sell the Software or charge for access without written permission
-
-**You may not:**
-- Sell the Software or bundle it in paid products (without permission)
-- Use it as a monetization mechanism
-- Remove attribution or copyright notices
-- Collect donations on behalf of OllaHub
-
-For commercial licensing inquiries, visit [evandro.dev.br](https://evandro.dev.br).
-
-## Acknowledgments
-
-- [Ollama](https://ollama.ai) for local LLM execution
-- [Tauri](https://tauri.app) for desktop framework
-- [Radix UI](https://www.radix-ui.com) for accessible components
-- [Shadcn](https://ui.shadcn.com) for component library
-- [ONNX Runtime](https://onnxruntime.ai) for embeddings inference