Tag: ai

Self-Supervised Learning Explained

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

Ollama Model Management: Beyond ollama run

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

Continue.dev vs Cody vs Tabby: AI Code Help Without the Cloud

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 vs CrewAI vs AutoGen: AI Agents Without the Hype

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.

Qdrant vs Weaviate vs Chroma: Vector DB Showdown

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 vs LlamaIndex: RAG Framework Showdown

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

The Embedding Model Choice Nobody Explains

Most people use OpenAI's embeddings because it's easy. But local embeddings exist. How to pick and when it actually matters.

GPU Memory Math: Will This Model Actually Fit?

Before you download a 70B model, calculate if it fits. The formulas, the gotchas, and a quick calculator you can actually use.

Beyond RAG: When a Virtual Filesystem Works Better

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.

Running Gemma 4 Locally with Ollama

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 LLMs: The Quantization Endgame

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.

AMD Lemonade: Local LLM Serving for AMD GPUs

AMD finally has a fast, open source local LLM server that uses both GPU and NPU. If you've been jealous of Nvidia users, Lemonade is worth your time.