Tag: ai

ComfyUI, Fooocus, A1111, and Forge compared for local AI image generation — which to pick based on your GPU, Docker comfort level, and workflow needs.

Text Generation Web UI vs KoboldCpp: setup, model formats, samplers, APIs, and performance compared so you can pick the right local LLM frontend fast.

Two ways to route LLM traffic across providers — OpenRouter as a hosted gateway, LiteLLM as a self-hosted proxy. Which one fits your home lab in 2026?

Local LLMs can call tools, query APIs, and run code if you set them up right. Function calling on Ollama and llama.cpp explained — patterns that actually work.

Gemma 4 vs Qwen3.6: sizes, reasoning, coding benchmarks, and which model you should actually pull for your home lab rig.

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.

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.

You can pull and run a model. Now learn Modelfiles, GPU layer tuning, the REST API, running multiple models without OOM-killing your server, and useful system prompts.

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.