Since this morning (October 31, 2025), I’ve been getting the “Rate exceeded” error every time I try to sign in. I’ve already gone through all the usual fixes — cleared cache and cookies, switched browsers, disabled extensions, reinstalled the desktop app, and even tried without a VPN — but the issue persists.
How to build production-ready AI capabilities that actually compound over time:
You’re Working Harder, Not Smarter
Your terminal is open. Claude is running. You’ve got that new AI coding assistant everyone’s raving about. You’re typing faster, generating more code, feeling more productive than ever.
Then you measure actual throughput. Features shipped. Bugs closed. Pull requests merged. The numbers don’t match the feeling.
Research from METR drops an uncomfortable fact: developers using AI tools often complete tasks slower than those working without AI — while consistently rating their own productivity higher. This isn’t a rounding error. It’s a perceptual gap large enough to drive a truck through.
I’ve spent over twenty years building production systems, from early web applications to modern distributed architectures. I’ve watched this movie before.
How Anthropic’s new Agent Skills framework turns general-purpose AI into specialized experts — and why it changes everything about building AI agents.
A summary of my experiences as a CTO. My name is Alireza Rezvani and CTO at a HealthTech Startup based in Berlin. AI Coding assistants became a part of my daily routines and I am eager to share these experiences with you on my Medium channel.
The message arrived during those quiet hours when introspection tends to surface most honestly:
“Can we talk tomorrow? I need advice.”
It came from one of my senior engineers — someone I deeply respect, someone whose technical judgment has shaped our architecture in profound ways. The next morning, over coffee, they shared something I wasn’t expecting:
“I watched our new hire solve in hours what would have taken me weeks, using tools I don’t even understand yet. And I felt… scared.”
15-minute technical read with working code examples
Hours into debugging, I watched user sessions bleed across accounts in production.
The AI-generated authentication code looked flawless. Clean syntax. Thoughtful comments. Proper error handling. It passed every test I threw at it locally.
But under concurrent load, everything fell apart. The AI had confidently generated a singleton pattern where none should exist — a mistake no senior developer would make, hidden inside code that looked professional enough to ship.
Imagine being able to sketch out an AI agent workflow on a canvas – dragging nodes, wiring logic, connecting to APIs, layering safety checks – and then turning it live in hours, not months. This is no longer futuristic hype.
Last month, I watched three companies in my network question every line of agent infrastructure they’d written over the past six months.
One team had four engineers spending $120K building custom orchestration layers for a procurement agent. Another was three months into a multi-agent research system that still hadn’t shipped.
Tired of wrestling with AI that writes spaghetti code? Meet CLAUDE.md – your project’s persistent brain that teaches Claude how to code like a pro. Below are 10 killer CLAUDE.md prompts (with examples) that transformed my agentic AI coding workflow from all-nighters to autopilot.
