Building AI Agents: The Fundamentals

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs

<blockquote> <p>I created a new website: <a href="https://programmingcentral.vercel.app/books/typescript/" rel="noopener noreferrer">Free Access to the 8 Volumes on Typescript

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wxfdt9/qwen38_27b_1_x_r9700_262k_context_half_a_million/"> <img alt="Qwen3.8 27B | 1 x R9700: 262K context,

<p>Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand

<p>Your agent passed all the tests. It's been running in production for three weeks. And it has been quietly wrong the entire time.</p> <p>This is not a hypothetical. Galileo's

<h2> TL;DR </h2> <ul> <li> <strong>DeepSeek V4 shipped April 24, 2026</strong> in two flavours: <strong>V4-Pro</strong> (1.6T MoE / 49B active) and <strong>V4-Flash</strong> (284B

<h1> Google Remy and Meta Hatch: The Technical Architecture Behind 24/7 Personal AI Agents </h1> <p>Two big AI agent stories broke this week that every developer building on top

<p><em>Originally published on <a href="https://aideazz.hashnode.dev/multi-model-llm-routing-why-76-of-your-inference-shouldnt-touch-gpt-4" rel="noopener noreferrer">AIdeazz</a> —