Every day I see another heartfelt post on Reddit about “resisting AI use” or “keeping the craft pure.”

Those posts remind me of myself. If you met me two years ago, you’d see me tastefully craft each line and function of the code I write.

Fast forward to today, and I’ve let go of control and I let the AI handle a lot of my codebase. Not because I necessarily want to, but because I’ve realized that programmers simply don’t have a choice anymore.

The industry has already decided, and it’s not waiting for any of us to catch up.

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.

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?

Last week, I moved to New York and joined the Recurse Center.

This is after being in San Francisco for a month, staying next to the Embarcadero and attending On Deck Founders.

I’m living in Downtown Brooklyn now, fortunate to find a place that’s just a five minute walk away from the Recurse Center.

It’s been a pleasure to stay in both the cities. Some differences:

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.