The landscape of software development has shifted irrevocably over the last three years. By March 2026, we have moved far beyond simple autocomplete suggestions and basic completion lines. We are now in the era of autonomous coding agents, where AI tools don't just finish your sentences—they architect
AI agents are the next frontier: instead of one-shot Q&A, agents reason, plan, use tools, and iterate until they solve complex tasks. This guide covers: * What makes an agent vs a chatbot * Core agent architectures (ReAct, Plan-and-Execute) * Tool calling & function execution * Memory & state management * Real-world examples
Choosing an LLM API? Cost can make or break your budget. A naïve implementation can burn $10k/month where a smart one costs $500. This guide breaks down real pricing (March 2026), shows cost-per-task examples, and reveals hidden tricks to slash your bill. Quick Comparison Table Legend: * ⚡ = Slow (10–30
Welcome to This Week in LLMs — your curated digest of the most important AI and language model news. Here's what happened March 24–30, 2026. 🚀 Major Releases GitHub Copilot Adds Claude Opus 4.6 TL;DR: GitHub Copilot now supports Anthropic's Claude Opus 4.6 at
Ollama is the easiest way to run large language models locally. Think "Docker for LLMs" — download a model, run a command, and you've got a local AI assistant. No cloud APIs, no data leaks, just your hardware. This guide covers everything: installation, model selection, performance tuning,
The explosion of large language models has created a crucial decision point: run models locally or use cloud-based APIs? Each approach has distinct trademarks in cost, privacy, performance, and complexity. Quick Decision Matrix When to Choose Local LLMs ✅ Best for: * Privacy-sensitive work: Medical, legal, financial, internal comms * High-volume inference: Running