Building Your (Local) LLM Second Brain
summary
A fully local “second brain” runs on a laptop using Ollama + Granite models served via OpenWebUI, with LangChain/llama_index for RAG and CrewAI/Autogen-style agents; it performs tool calls through an MCP proxy to Todoist. Latency on Apple M chips measures 2B ≈ 10–15s and 8B ≈ 25–35s. Local quantized models need careful prompt/chat-template engineering, may require custom gguf quantization or fine-tuning, and benefit from extended contexts up to 128k to reduce hallucination.
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chapters (18)
- 0:00Local LLM second brain intro
- 3:35AI hype cycles and local models
- 9:13Defining a second brain
- 12:30Technical requirements
- 13:13Ollama for local serving
- 15:22Small model performance
- 17:49Small vs large model trade-offs
- 18:54Prompt engineering for small models
- 22:01Chat templates in LLM inference
- 24:31Chat interfaces for local models
- 26:57Bringing notes into second brain
- 27:19RAG pipelines for local data
- 30:10Custom RAG pipelines
- 31:56Agents for local reliability
- 34:19Agent frameworks
- 36:25Tool calling for local actions
- 38:35MCP servers integration
- 40:24End-to-end local architecture
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