- How to Run an LLM Locally: Hardware, Quantization, Tools and First Steps Running an open-weight LLM on your own machine means offline use and private data. Learn what hardware you need, how quantization helps and which tools to use.
- Transformer The Transformer is a neural network architecture built around attention, introduced in the 2017 paper Attention Is All You Need. It underpins modern LLMs.
- Vector Database A vector database stores and searches high-dimensional vectors, quickly finding the most semantically similar items. It is core infrastructure for RAG.
- AI Agents Explained: What Separates an Agent from a Chatbot An AI agent is an LLM application that plans steps, calls tools and adapts to results. Learn its parts, its core loop, good use cases and common myths.
- LLM Tokens Explained: What They Are and Why APIs Charge by the Token Tokens are the units LLMs read and write. They set how much fits in context and how API usage is billed. Learn how tokenization works and how to save.
- MCP Explained: A Universal Plug for Connecting AI Apps to Tools MCP is an open protocol that lets AI apps connect to tools and data sources in one standard way. Learn the problem it solves, its architecture and risks.
- RAG Explained: Letting an LLM Look Things Up Before It Answers RAG retrieves relevant passages from a knowledge base and hands them to an LLM before it answers, easing stale knowledge and hallucination. Here's how.
- Yann LeCun: Pioneer of Convolutional Networks, Advocate of World Models Yann LeCun's LeNet, built at Bell Labs, laid the base for modern computer vision. He founded Meta's FAIR, won the Turing Award and backs world models.
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