Samba Copilot

Samba Copilot the true orchestrator of agents and super agents which enable high productivity to its wielder by combining multiple modalities , models, and tools , on personal or web data.

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Samba Copilot

Inspiration

After exploring the AI starter kit provided by the team, I realized that combining multiple commonly used AI applications could create a more powerful copilot tool. this hackathon allowed me to integrate everything under one unified solution.

What it does

  • Press 1 to record voice instructions or use it as a speech-to-text note maker
  • Press 2 to capture screenshots, saved as base64 in memory
  • Press 3 to send to AI model (uses largest LLM for text-only queries, best vision model for images)
  • Press 4 to type the response in any textbox system-wide (streams at 100 characters per second)
  • Toggle between using clipboard text as context
  • Toggle between using personal RAG data from workspace/memory
  • Can analyze and explain memes using the Llama model
  • Focuses on hotkey functionality for minimal tool switching and maximum efficiency

How we built it

  • Leveraged open-source technologies while focusing on usability
  • Built with PyQT for cross-platform compatibility, ~100 MB Distribution size.
  • Standalone application without browser or separate server requirements
  • Modular, object-oriented design for easy customization and extension, Mods system
  • Multi modal Normalization

Challenges

The main challenge lies in testing and continuous improvement. While building AI apps has become more accessible, optimizing for both user and developer experience remains crucial.

Key Accomplishments

  • White-label ready with customizable app name, color theme, and variables for enterprise use
  • Adaptable system prompts / plugins various use cases: Report Generation, Receipt scanning, Content writing, Project planning, Realtime Meeting Copilot, Document analysis, OCR Engine.

Learnings

Sambanova's performance proved exceptionally fast, demonstrating the potential of "Lightning Fast AI" for significant productivity gains in copilot applications.

Future Development

  • Implement fine-tuning endpoints from Sambanova studio cloud = Reduce context requirements
  • Evaluate performance against RAG and daily fine-tune jobs
  • Explore speeds with quantized models
  • Note: Current coding copilot capabilities using Llama model need improvement; hoping for Sambanova to implement Nemotron/Qwen-coder

Try now!

https://github.com/nikhil-swamix/sambanova-copilot https://youtu.be/ZITFUoB8b-k?si=2Rb0WZvwQud4tfxV

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