This guide walks you through setting up a fully private, local LLM for coding on your own hardware. From model selection and hardware planning to IDE integration with Ollama and Continue.dev, you’ll build an AI coding stack where no code ever leaves your machine.
Best AI Coding Agents in 2026: The Complete Beginner’s Guide
The best AI coding agents in 2026 don’t just autocomplete your lines — they plan, execute, and debug entire features autonomously. Whether you’re a weekend hacker or a seasoned engineer, this guide breaks down Google Antigravity, Claude Code, OpenAI Codex, OpenCode, and KiloCode so you can pick the right tool and ship faster.
A step-by-step guide to building a simple RAG system in Python
Build a small, practical Retrieval-Augmented Generation (RAG) system in Python: chunk your docs, embed them, store vectors in Chroma, retrieve top matches, and have an LLM answer using only that context. Includes a runnable example and common pitfalls.
Add Memory to AI Agent: Python Tutorial for Beginners
Discover how to add memory to AI agent systems using pure Python—no frameworks required. This hands-on tutorial walks you through building short-term conversation tracking and long-term persistent storage, transforming stateless chatbots into context-aware assistants that actually remember your conversations and preferences across sessions.
Model Context Protocol For Beginners: Superpower of AI Agents
Model Context Protocol (MCP) is Anthropic’s open standard that lets AI agents securely access your tools, databases, and APIs. Think of it as USB-C for AI—one universal protocol connecting language models to everything they need to actually get work done in your environment.