All the articles with the tag "llm".
Traditional automation is just very fast copy-paste. When your email filter breaks because someone wrote "URGENT" in lowercase, you realize rule-based logic has limits. Connecting n8n to a local LLM turns "if this then that" into "figure this out and do the right thing."
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.
Want to run AI locally but not sure if your GPU will cooperate? Whether you're rocking an NVIDIA card, an AMD GPU, or just a CPU and sheer determination, here's the honest breakdown of what works, what technically works, and what will make you question your life choices.
Everyone's talking about AI agents like they'll solve world hunger by Tuesday. But which framework do you actually use? We compare LangGraph, CrewAI, and AutoGen — with working Python examples, brutal honesty, and a healthy dose of skepticism about your robot assistant booking flights to Reykjavik.
OpenAI Whisper is genuinely impressive speech-to-text — and you can run it entirely on your own hardware. Add Faster-Whisper into the mix and suddenly you've got transcription that's 4x quicker, uses less VRAM, and doesn't phone home to anyone. Here's how to set it all up without losing your mind.
Your app calls OpenAI, your side project calls Anthropic, your homelab whispers to Ollama — and your codebase looks like a crime scene. LiteLLM and vLLM are the dynamic duo that puts a single sane API in front of every model you'll ever run, local or cloud.
Stop paying per-token to ask questions about your own documents. This guide walks you through building a fully local RAG pipeline with Ollama and ChromaDB — from Docker Compose to Python code — so your AI can actually know things without hallucinating them.
LangChain does everything and LlamaIndex does one thing brilliantly. Here's how to pick the right RAG framework without regretting it at 2 AM.
Most people use OpenAI's embeddings because it's easy. But local embeddings exist. How to pick and when it actually matters.
Before you download a 70B model, calculate if it fits. The formulas, the gotchas, and a quick calculator you can actually use.
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.
