🚀 Cursor10x is now DevContext 🚀

Cursor10x has evolved into DevContext - A more powerful, dedicated context system for developers

<b>🧠 Project-Centric</b>
<b>📊 Relationship Graphs</b>
<b>⚡ High Performance</b>


One database per project
Intelligent code connections
Minimal resource needs

🔥 DevContext takes AI development to the next level 🔥

🔄 Continuous Context Awareness - Sophisticated retrieval methods focusing on what matters 📊 Structured Metadata - From repository structure down to individual functions 🧠 Adaptive Learning - Continuously learns from and adapts to your development patterns 🤖 Completely Autonomous - Self-managing context system that works in the background 📚 External Documentation - Automatically retrieves and integrates relevant documentation 📋 Workflow Integration - Seamless task management workflow built-in

👀 Be on the lookout 👀

The DevContext Project Generator is launching in the next couple days and will create a COMPLETE set up for your project to literally 10x your development workflow.

Visit DevContext Repository

DevContext is a cutting-edge Model Context Protocol (MCP) server providing developers with continuous, project-centric context awareness that understands your codebase at a deeper level.

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📋 Table of Contents

• Overview
• Memory System
• Task Management
• Cursor Rules
• Installation
• Examples
• Troubleshooting
• License

Overview

Cursor10x bridges the gap between stateless AI interactions and continuous development workflows by providing:

• Persistent Memory: Retains context across multiple sessions
• Task Management: Standardizes implementation of project tasks
• Best Practices: Enforces coding standards and workflows

The system uses the Model Context Protocol (MCP) to register tools that Claude can execute directly, creating a fully autonomous development assistant with memory and structured workflows.

Note: This project is now deprecated in favor of DevContext, which provides enhanced capabilities with more advanced context management.

Memory System

The Cursor10x Memory System creates a persistent memory layer for AI assistants (specifically Claude), enabling them to retain and recall:

• Recent messages and conversation history
• Active files currently being worked on
• Important project milestones and decisions
• Technical requirements and specifications
• Chronological sequences of actions and events (episodes)
• Code snippets and structures from your codebase
• Semantically similar content based on vector embeddings
• Related code fragments through semantic similarity
• File structures with function and variable relationships

This memory system bridges the gap between stateless AI interactions and continuous development workflows, allowing for more productive and contextually aware assistance.

System Architecture

The memory system is built on four core components:

1. MCP Server: Implements the Model Context Protocol to register tools and process requests
2. Memory Database: Uses Turso database for persistent storage across sessions
3. Memory Subsystems: Organizes memory into specialized systems with distinct purposes
4. Vector Embeddings: Transforms text and code into numerical representations for semantic search

Memory Types

The system implements four complementary memory types:

1. Short-Term Memory (STM)

   • Stores recent messages and active files
   • Provides immediate context for current interactions
   • Automatically prioritizes by recency and importance

2. Long-Term Memory (LTM)

   • Stores permanent project information like milestones and decisions
   • Maintains architectural and design context
   • Preserves high-importance information indefinitely

3. Episodic Memory

   • Records chronological sequences of events
   • Maintains causal relationships between actions
   • Provides temporal context for project history

4. Semantic Memory

   • Stores vector embeddings of messages, files, and code snippets
   • Enables retrieval of content based on semantic similarity
   • Automatically indexes code structures for contextual retrieval
   • Tracks relationships between code components
   • Provides similarity-based search across the codebase

Features

• Persistent Context: Maintains conversation and project context across multiple sessions
• Importance-Based Storage: Prioritizes information based on configurable importance levels
• Multi-Dimensional Memory: Combines short-term, long-term, episodic, and semantic memory systems
• Comprehensive Retrieval: Provides unified context from all memory subsystems
• Health Monitoring: Includes built-in diagnostics and status reporting
• Banner Generation: Creates informative context banners for conversation starts
• Database Persistence: Stores all memory data in Turso database with automatic schema creation
• Vector Embeddings: Creates numerical representations of text and code for similarity search
• Code Indexing: Automatically detects and indexes code structures (functions, classes, variables)
• Semantic Search: Finds related content based on meaning rather than exact text matches
• Relevance Scoring: Ranks context items by relevance to the current query
• Code Structure Detection: Identifies and extracts code components across multiple languages
• Auto-Embedding Generation: Automatically creates vector embeddings for indexed content
• Cross-Reference Retrieval: Finds related code across different files and components

Cursor Rules

Cursor rules are enforced through two complementary systems:

1. MDC (Markdown Configuration) Rules - Stored in .cursor/rules/ di