LLM stacks \ 4.3b Coding agents (deep dive)
25.0718 Lab notes (Gdrive), Git
Docs 25.0718
- #420 JUNK JUNK xxxxxxxxxxxxxxxxxxxxxxxxxxxx
- #419_gemini_cli_CONCEPTS_v02_25.0703
- #418_(BINGO)_gemini_cli_
- #417_(FAIL)_gemini_cli_GPT_ xxxxxxxxxxxxxxxxxxx
- #416_(CHAOS)_copilot_coding_agent_v05_25.0705
ME: "forgot the py script.... i am only interested in what you (Copilot) are doing."
Ah, focusing just on me (GitHub Copilot)! Here's what's happening:
What I Am:
- GitHub Copilot Chat in VS Code
- Agent: I orchestrate tools and provide responses
- Model: I'm powered by Claude 3.5 Sonnet
What I'm Using Right Now:
Just 1 Model: Claude 3.5 Sonnet
- That's me! - The model generating these responses
- Provider: Anthropic (via VS Code integration)
- Role: Understanding your questions, providing explanations about RAG/MCP
My Tools/Capabilities:
- File reading: I can read your simple_rag.py
- Code analysis: Understanding your RAG implementation
- File editing: I can modify your code files
- Terminal access: I can run commands
- Web search: For looking up information
- Context awareness: I know about your workspace structure
So the Answer:
Just 1 model - Claude 3.5 Sonnet (me)
But I'm an agent because I:
- Use tools (file reading, editing, terminal)
- Orchestrate responses
- Have context about your workspace
- Can perform actions beyond just text generation
The Distinction:
- Raw Claude model: Just generates text
- GitHub Copilot (me): Claude model + VS Code tools + workspace context = Agent
So you're talking to 1 model (Claude Sonnet) that's been enhanced with agent capabilities through VS Code integration!
