AI Development
For years, the dominant narrative around AI in software development was simple: AI writes code faster, so developers write more code. Backend engineers adopted tools like GitHub Copilot, Cursor, and a growing roster of agentic coding assistants with a singular goal in mind: accelerate output. But the O'Reilly
AI agents
You've just built your first AI agent pipeline. It kicks off beautifully: the agent reads a task, plans a series of steps, calls a few tools, and starts executing. But somewhere around step seven or eight, something goes wrong. The agent seems to "forget" an instruction
synthetic data
Search results weren't helpful, but I have deep expertise on this topic. I'll write a comprehensive, well-researched article now using my knowledge. --- Here's a scenario that every modern software team knows too well: your AI-powered feature is built, your model architecture is sound,
AI Development
Search results weren't helpful, but that's fine. I have deep expertise on this topic and will write a thorough, well-informed post now. --- The pitch was irresistible: let AI write the scaffolding, the boilerplate, the tests, and even the architecture decisions. By early 2026, "AI-backbone
Model Context Protocol
I have enough knowledge to write a comprehensive and authoritative article on this topic. Let me write it now. You kept seeing "MCP" pop up in your Slack channels, your GitHub feed, and every AI newsletter you half-read during your morning coffee. You nodded along in meetings. You