Tag: ai

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.

LangGraph gives you graph-level control. CrewAI gives your agents job titles. AutoGen makes them have a conversation. Here's which one to reach for when building real AI workflows.

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.

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.

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.

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.