The Huge List of AI Tools: What's Actually Worth Using in May 2025?

Unlocking Real Leverage with AI Delegation

Starting to delegate to AI feels awkward. It is a lot like hiring your first contractor: you know there is leverage on the other side, but the first steps are messy and uncertain. The myth of the perfect plan holds many people back, but the reality is you just need to begin.

The payoff is real, but the start is always a little rough.

Here is how I do it.

How I Make Complex AI Changes

Most technical leaders know the pain. You get partway into an ambitious AI project, then hit a wall. You are not sure how to start, or you get so far and then stall out, lost in the noise of options and half-finished experiments.

Recently I tackled this head on. I did this live, in front of an audience. I used a framework that finally made the difference.

The challenge: could I take a complex change, break it down, and actually finish it, live on stream? My answer: yes, with the right approach. Here is exactly how I did it.

Building AI Cheatsheet Generator Live: Lessons from a Four-Hour Stream

I built an entire AI-powered app live, in front of an audience, in just four hours. Did I finish it? Not quite. Did I learn a huge amount? Absolutely. Here is what happened, what I learned, and why I will do it again.

The challenge was simple: could I build and launch a working AI cheatsheet generator, live on stream, using AI first coding and Kaijo¹ as my main tool?

Answer: almost! By the end of the session, the app could create editable AI cheatsheets, but it was not yet deployed. A few minutes of post-stream fixes later, it was live for everyone to try. (Next time, I will check deployment on every commit!)

Try the app here: aicheatsheetgenerator.com

AI: The New Dawn of Software Craft

AI is not the death knell for the software crafting movement. With the right architectural constraints, it might just be the catalyst for its rebirth.

The idea that AI could enable a new era of software quality and pride in craft is not as far-fetched as it sounds. I have seen the debate shift from fear of replacement to excitement about new possibilities. The industry is at a crossroads, and the choices we make now will define the next generation of software.

But there is a real danger: most AI coding assistants today do not embody the best practices of our craft. They generate code at speed, but almost never write tests unless explicitly told to. This is not a minor oversight. It is a fundamental flaw that risks undermining the very quality and maintainability we seek. If we do not demand better, we risk letting AI amplify our worst habits rather than our best.

This is the moment to ask whether AI will force us to rediscover what software crafting¹ truly means in the AI age.

Why Graph RAG is the Future

Standard RAG is like reading a book one sentence at a time, out of order. We need something new.

When you read a book, you do not jump randomly between paragraphs, hoping to piece together the story. Yet that is exactly what traditional Retrieval-Augmented Generation (RAG) systems do with your data. This approach is fundamentally broken if you care about real understanding.

Most RAG systems take your documents and chop them into tiny, isolated chunks. Each chunk lives in its own bubble. When you ask a question, the system retrieves a handful of these fragments and expects the AI to make sense of them. The result is a disconnected, context-poor answer that often misses the bigger picture.

This is like trying to understand a novel by reading a few random sentences from different chapters. You might get a sense of the topic, but you will never grasp the full story or the relationships between ideas.

Real understanding requires more than just finding relevant information. It demands context and the ability to see how pieces of knowledge relate to each other. This is where standard RAG falls short. It treats knowledge as a stack of random pages, not as a coherent whole.

Time for a totally new approach.