Open Source Projects

As part of our commitment to advancing local AI technology and democratizing artificial intelligence, we actively contribute to the open source community. Our projects reflect our core mission of creating sustainable, privacy-focused, and locally executable AI solutions. Below are our key open source contributions that demonstrate our expertise in advanced caching mechanisms, multi-agent reasoning systems, and local AI infrastructure.

🚀 MLX Omni Server with Advanced Cache Management

Repository: mlx-omni-server (cache-manager branch)

Our enhanced fork of the MLX Omni Server represents a significant advancement in local AI inference capabilities for Apple Silicon. This project exemplifies our vision of efficient, local AI computing with sophisticated caching mechanisms.

Key Features:

• Advanced KV Cache Pre-computation: Revolutionary approach to pre-computing and managing Key-Value caches for faster inference on long contexts

• Apple Silicon Optimized: Built on Apple's MLX framework, specifically designed for M1/M2/M3/M4 series chips

• OpenAI API Compatible: Drop-in replacement for OpenAI API endpoints, enabling seamless integration with existing applications

• Multi-Modal Capabilities: Supports chat completion, text-to-speech, speech-to-text, image generation, and embeddings

• Privacy-First Design: All processing happens locally on your machine, ensuring complete data sovereignty

• Intelligent Cache Management: Dedicated endpoints for cache validation, listing, and management

Technical Innovation:

Our cache management implementation allows for pre-computing KV caches during system idle time, enabling the vision of AI systems that "dream at night about tomorrow's tasks." This approach dramatically reduces inference time for domain-specific queries and enables efficient processing of massive document databases on local hardware.

The server supports our novel model@cache_path syntax, allowing users to leverage pre-computed contexts seamlessly:

response = client.chat.completions.create(

    model="mlx-community/Llama-3.2-3B-Instruct-4bit@/path/to/cache.safetensors",

    messages=[{"role": "user", "content": "Based on the context, what happened in 1974?"}]

)

🧮 Mathematical Calculator MCP Server

Repository: calculator-mcp-server

A sophisticated Model Context Protocol (MCP) server that extends Claude's capabilities with advanced mathematical computation tools. This project demonstrates our expertise in creating intelligent AI extensions and agent-based systems.

Capabilities:

• Symbolic Mathematics: Solve equations, calculate derivatives and integrals

• Statistical Analysis: Comprehensive statistical functions including regression analysis, confidence intervals, and correlation coefficients

• Matrix Operations: Full matrix mathematics support including multiplication, transposition, and complex operations

• Safe Expression Evaluation: Secure mathematical expression parsing and computation

• Seamless Integration: Easy integration with Claude Desktop through FastMCP

Use Cases:

This tool transforms Claude into a powerful mathematical assistant capable of handling complex calculations, statistical analysis, and symbolic mathematics. It represents our approach to augmenting AI capabilities through specialized, focused tools rather than monolithic solutions.

Example interactions:

• Complex mathematical expressions: 3.5^2 * sin(pi/4)

• Equation solving: x^2 - 5x + 6 = 0

• Calculus operations: derivatives and integrals of complex functions

• Statistical analysis of datasets with comprehensive reporting

🧠 Chain of Thought (CoT) Chat Application

Repository: cot_chat

An innovative implementation of Chain of Thought reasoning using a multi-agent system based on the Mixture of Specialized Agent Graphs (MosAG) approach. This project showcases our research into advanced reasoning methodologies and agent orchestration.

Architecture:

The application employs three specialized agents to implement sophisticated reasoning:

1. Initial Reasoning Agent: Analyzes user questions and proposes initial solutions using Chain of Thought methodology

2. Iterative Refinement Agent: Continuously improves and refines the initial solution through iterative analysis

3. Aggregation & Explanation Agent: Synthesizes the entire reasoning process and presents results in an accessible format

Technical Features:

• Flexible LLM Integration: Supports any LLM compatible with LiteLLM, including Claude, GPT-4, and other models

• YAML Configuration: Easy configuration of agents, models, and reasoning parameters

• Interactive Web Interface: Streamlit-based interface for real-time interaction and reasoning visualization

• Conversation Export: Save complete reasoning chains for analysis and documentation

• Transparent Process: Full visibility into the reasoning process of each agent

Research Application:

This tool enables exploration of complex reasoning patterns and demonstrates how multiple specialized agents can collaborate to solve intricate problems. It serves as a testbed for advanced reasoning methodologies and multi-agent coordination strategies.

Impact and Philosophy

These open source projects embody our core beliefs about the future of artificial intelligence:

Local-First Computing: All projects prioritize local execution, data sovereignty, and user control over their AI interactions.

Efficiency Through Innovation: Our caching mechanisms and specialized agents demonstrate how intelligent design can achieve powerful results with consumer hardware.

Transparency and Accessibility: By open-sourcing our work, we contribute to the democratization of AI technology and enable others to build upon our innovations.

Sustainable AI Development: Our projects showcase how advanced AI capabilities can be achieved without relying on energy-intensive cloud infrastructure.

Contributing

We welcome contributions, feedback, and collaboration on all our open source projects. Each repository includes comprehensive documentation, setup instructions, and contribution guidelines. Whether you're interested in local AI infrastructure, mathematical computing, or advanced reasoning systems, we invite you to explore, use, and contribute to our work.

Together, we can build the future of local, sustainable, and democratically accessible artificial intelligence.