Tag: ai

AnythingLLM is the closest thing to a real private NotebookLM you can self-host. Workspaces, RAG, agents, document chat, running locally on Ollama in 20 minutes.

Pixtral, Qwen3-VL, and Gemma 4 compared for local multimodal use in 2026. LLaVA is dead; here's what to run in Ollama for OCR, screenshots, and vision tasks.

Model Context Protocol turns your LLM into a tool-using agent, file access, APIs, your home lab. Build your first MCP server in under 50 lines of Python.

Most RAG demos look great until you ship them. Ragas measures faithfulness, context precision, answer relevancy, the metrics that actually predict user trust.

How tiny 7B and 8B models keep punching above their weight, knowledge distillation, the teacher-student trick that makes local AI actually usable on home hardware.

Self-supervised learning is the technique behind GPT, BERT, and modern LLMs. Learn how models teach themselves from unlabeled data.

GitHub Copilot is great until you read the ToS. Continue.dev, Cody, and Tabby bring AI code assistance to your editor with local or self-hosted models, no code leaves your machine.

Every RAG tutorial says 'just use Chroma.' Then you hit production. Here's what Qdrant, Weaviate, and ChromaDB actually offer and when each one earns its place.

LangChain does everything and LlamaIndex does one thing brilliantly. Here's how to pick the right RAG framework without regretting it at 2 AM.

RAG is the default answer for giving LLMs access to documents. But chunking, embedding, and retrieval introduce failure modes that a virtual filesystem sidesteps entirely.

Google's Gemma 4 is the best open model they've shipped yet. Here's how to pull it, run it, and actually use it for real work with Ollama on your own hardware.

1-bit models store weights as -1, 0, or 1. That sounds insane until you see them run a 100B parameter model on a laptop CPU. Here's what's actually happening.