Ivan Horvatić

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