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The AI Code Security Field Guide: 10 Vulnerabilities You're Shipping Right Now
AI-generated code looks clean and ships fast — and quietly introduces a predictable set of security holes. Here's an OWASP-style guide to the most common AI code vulnerabilities in 2026, with real examples and fixes.
How to Build AI Agents for Business Automation: A Builder's Guide
The technical playbook — architecture, tool design, eval, deployment, and monitoring. Everything you need to ship an agent that survives contact with real users in 2026.
Why Infinite Canvas Beats A/B Testing for AI Work
A/B testing assumes you know the two options worth testing. With AI, the bigger win comes from generating twenty options on a canvas and picking by eye. Here is why and when.
Multi-Agent Orchestration: Patterns, Frameworks, and What Actually Works in 2026
Orchestrator-worker, swarm, supervisor, hierarchical — the real patterns behind multi-agent systems, with honest takes on LangGraph, CrewAI, AutoGen, and when to skip them entirely.
Multi-Framework AI Generation: One Spec, Four Frontends
Generating the same UI component across React, Vue, Svelte, and Solid with AI in 2026 — why it matters, when it actually works, and the prompt pattern that makes it usable in production.
I Rebuilt My RAG System Three Times. Here's What I Wish I'd Known.
A first-person account of building, breaking, and rebuilding the same RAG system three times in 18 months. The lessons that only show up after you've shipped it twice and regretted it.
The Vibe Coding Revolution: How a Joke Tweet Rewired the Developer Job
Vibe coding started as a Karpathy throwaway and ended as a labor restructuring. Here's the cultural and industry essay — what the developer role actually became, who won, who lost, and what's next.
Why Your RAG Chatbot Sucks (And How to Fix It)
Most production RAG chatbots are bad in predictable ways. Here are the real reasons — chunking, retrieval, eval, prompting — and the fixes that actually move the needle in 2026.
How AI Agents Actually Work: Tool Use, Memory, and Orchestration Explained
'Agentic AI' is the buzzword of 2026 — but what's actually happening under the hood when an agent books your flight, refactors your code, or runs a 5-step research task? A plain-English breakdown with real examples.
MCP (Model Context Protocol) Explained: The 'USB-C for AI Agents'
MCP is the plug standard that lets any AI model connect to any data source or tool — Gmail, GitHub, Notion, your filesystem — without bespoke integrations. Here's what it is, why it won, and how to actually use it in 2026.
Multi-Model AI Workflows: Routing Prompts to the Right Model Automatically
Using one AI model for everything is like using one screwdriver for every job. Here's how to route each task to the best-fitting model — cheap for bulk, expensive for hard, fast for interactive — and cut your AI bill by 60% without losing quality.
RAG vs Fine-Tuning vs Long Context: When to Use Each in 2026
Stop picking RAG by default. With 2M-token context windows, 90% prompt cache discounts, and cheap fine-tuning, the right choice for 'teach my AI about my data' has changed. Here's the real decision framework.