Deepseek AI Assistant - ALWAYS ON Python AI Agent for Engineers that SHIP
AI Summary
Video Summary
Personal AI Assistant Overview
- The video introduces an always-on personal AI assistant for engineering tasks.
- The assistant is built on open-source technology and the DeepSpeed V3 language model.
- It’s designed to be cost-effective and powerful, adding a new dimension to computing work.
Key Features and Configuration
- The assistant has three essential elements: speech-to-text, language model (brain), and text-to-speech (voice).
- Configuration includes setting the assistant’s name, the human companion name, and the components (ears, brain, voice).
- The assistant can run locally on hardware, with the option to use local LLM providers for privacy.
- The assistant’s active memory (scratch pad) allows it to remember and update information, crucial for engineering tasks.
Commands and Execution
- Commands are given in natural language and executed by the assistant.
- The scratch pad pattern is introduced, allowing the assistant to have an active memory.
- The assistant can create, update, and delete users based on commands and scratch pad updates.
- Errors in commands can be corrected by giving the assistant new instructions.
Real-Time Speech to Text
- The video highlights a real-time speech-to-text library called “real-time St” that is foundational for personal AI assistants.
- The library allows for always-on transcription with adjustable accuracy and speed settings.
- The author demonstrates the transcription speed and accuracy with different model settings.
Typer AI Agent and Command Prompt
- The typer AI agent is the underlying mechanism that the assistant uses to execute commands.
- The command prompt structure includes purpose, instructions, typer commands, context files, and natural language requests.
- The assistant is designed to work with typer-based commands, which are useful for Python CLI command builders.
Future of Personal AI Assistants
- Personal AI assistants are seen as an untapped dimension of engineering that can work in parallel with engineers.
- Future improvements could include giving the assistant vision to see the screen, writing code, and integrating different AI agents.
- The goal is to have compute systems that are always on and solving problems.
Conclusion
- The video encourages viewers to fork the code base and experiment with their own personal AI assistant.
- The author plans to use the assistant to scale up their engineering work and make it more reusable and easier to use.
Detailed Instructions and Tips (No URLs or CLI Commands Provided)
- No specific URLs, CLI commands, or detailed instructions were provided in the transcript.