AI code generation is error-prone. Why, then, are programmers still using it?

Everyone from YC partners to Fiverr’s CEO has been proclaiming that “90% of code is AI-generated” or that they’re becoming “AI-first” companies.

The subtext they’re forcing on us is clear: programmers who don’t embrace AI will be left behind.

But after two years of daily AI coding — from the earliest Cursor version to the latest agentic tools — I’ve uncovered the truth: AI coding tools are simultaneously terrible and necessary.

MCPs are a way for AIs to interact with the outside world. An MCP can allow AI to read emails, post tweets, message your friends, and much more.

We are used to interacting with the digital world via apps and windows—but MCPs enable an AI to do everything that humans do, without using any apps.

Here’s a quick guide on setting up and using your first MCPs in Windows.

You know by now that AI can dramatically speed up your development process (when used correctly.)

But the key is knowing how to communicate with the AI properly.

Here’s my collection of prompts that actually work in real-world scenarios.

The first time I encountered Big Tech was at age 15 when I won Google Code In. They flew me and my family to San Francisco and showed us around the Googleplex. I arrived with wide eyes, eager to see where the “smartest people in the world” worked.

But, what I found… disturbed me.

Everyone wore the same badges, slept in nap pods, played the same games, and ate at the same cafeterias. I couldn’t escape the realization that I was looking at a daycare for adults.

That day, I silently promised myself I would never work in such an environment.

Imagine if you could build your own apps and tools just by describing what you want. That’s now possible thanks to new AI tools.

This guide will show you how to do it! This guide is specifically for beginners who have never done anything technical before.

What We’ll Cover

1. How to turn your ideas into real working apps
2. Which free tools to use
3. Step-by-step instructions for your first project
4. Common mistakes to avoid
5. How to make your creation available to others

Is it just me, or are the code generation LLMs we’re all using not that good?

For months, I’ve watched developers praise LLMs while silently cleaning up their messes, afraid to admit how much babysitting they actually need.

I realized that LLMs don’t actually understand codebases — they’re just sophisticated autocomplete tools (with good marketing.)

After two years of frustration watching my AI assistants constantly “forget” where files were located, create duplicates, and use completely incorrect patterns, I finally built what the big AI companies couldn’t — or wouldn’t.

I decided to find out: What if I could make AI actually understand how my codebase works?

AI is incredibly powerful, but it needs guidelines. “Vibe coding” might work initially, but as the project grows, it creates more mistakes than it solves. After fixing countless AI implementations, I’ve distilled it down to three core principles that actually work.

The current wave of AI tools promises to 10x your development speed. What they don’t mention is how they can also 10x your debugging time if implemented poorly. I’m building tools to solve exactly this problem, and I’m sharing some lessons I’ve learned along the way.