Notes on Six Months of AI-Enabled Building
Moving Toward Focused Building & My Last cohort
Six months ago, I left my job. I had about three months of savings in my bank account. It was pretty reckless.
I wanted to learn more. I had ideas I couldn’t implement inside a company. I wanted to build my own products, create my own courses, and have the freedom to make mistakes and to try paths others were confident were wrong.
I knew AI was powerful, but I didn’t know the best way to use it. I had theories to test and bad ideas to explore. This is the story of that exploration, how AI enabled it, and what I’m doing next, and why the January Cohort will be my last one.
Five Experiments in Six Months
I created two courses and began building two products. I rebuilt those products in four frameworks I had never used before, learning each well enough to understand what was happening under the hood. I could never have imagined doing all this at once. AI was the catalyst.
1. The AI Coding Course
I started an AI coding course with Eleanor Berger. To succeed, we needed to market it, but I had no social media presence. This project forced me to use AI for everything: not just for coding, but for creating content, writing blog posts, making presentations, and producing videos.
It became a research project on using AI effectively. It was the single most transformational thing I did. Every other experiment tied back to the lessons learned here.
2 & 3. Building Products (and My Tech Stack)
My brother and I started building two products: Raw2Draft, a content management tool for my course, and plot.builders, a tool for writing fiction.
How do you review generated code? Where is the line between useful and slop? Should you understand every line? Is it possible to prove that code is good without understanding every line? How do you use agents well? Is vibe-coding useful for building product? Does framework really matter? How do you use AI without giving up all the speed gains back in maintenance, bugs, and review?
We rebuilt the apps in several frameworks to find out, and developed strong opinions that inform the course.
We tried many frameworks, here’s a few.
Next.js and React: AI support was strong, but the complexity was high. The time saved generating code was lost in the review cycle.
FastAPI and React (with Mantine): We simplified the backend with FastAPI, which we could test in isolation, and used AI for the frontend UI. This was better, but still challenging.
Air: I started talking to Danny and Audrey Roy Greenfeld. Danny was working on bringing HTML composability into FastAPI, an idea that evolved into a full framework called AIR. I rebuilt my apps with it and was blown away.
The code was simple, and the AI support was phenomenal. I attribute much of this to the documentation that have clear, consistent patterns that gave AI something concrete to work with. Well-documented code gives AI clear patterns to follow.
Astro is another framework worth trying. I am keen to dig into it more
4. The Retrieval Course
I created a retrieval course for boot.dev to see how helpful AI was for creating educational content. It was helpful, but not as much as I’d hoped.
Its best use was generating a rubric to evaluate my lessons. The rubric prompted me to add some things, but I disagreed with more than half its suggestions. It was still a net win.
It was also helpful when I could transcribe what I was thinking and have AI create a rough first draft from the transcription.
5. The Book
I wrote and edited a 22-chapter book. It is not yet published, but the process solidified my thoughts on writing, structuring ideas, and maintaining continuity. AI was extremely helpful here, more so than with the retrieval course, because the book was documenting what I do daily.
Teaching fundamentals requires breaking down concepts in ways you don’t naturally think about anymore. The mental model you use after mastering something is very different from the one needed to learn it and that transformation isn’t obvious and requires significant work.
What I Learned About AI
This six-month sprint was an intensive research project. My productivity is drastically higher than it was before. Here are a few important lessons.
AI Is a Learning Accelerator
The biggest speed up is because I learn faster. And learning is the bulk of new work for me. I was blocked as a writer for over ten years. Now when I sit down to write, I have 10 to 15 new things I learned that week worth writing about - not just AI techniques, but new APIs, async patterns, decoupling strategies, entire domains I’d never touched before.
There was no single “this changes everything” moment. It’s been a gradual compounding. My throughput keeps increasing, week by week, and I keep getting more and more excited.
Structure is a thinking tool
To think clearly, we learn to structure our thoughts with project plans, pro/con lists, specs, tickets, and other methods for comparing options. AI is excellent at creating these frameworks. You can accelerate your thinking by transforming your raw ideas into such structures.
For example, you can record a jumble of thoughts and have an AI organize them into a pro/con list, extract key components, sort them by difficulty, or provide a summary. This is a powerful way to clarify your ideas and speed up the entire process.
Your Thinking Style Determines Your Success
AI is a tool for thought. How you think about problems determines the leverage you get.
Spec-First Thinking: If you think through specs and requirements, like a product manager, AI can be a powerful code generator. People with product backgrounds get massive speedups with AI because of this.
Code-First Thinking: If you think by writing code you’ll see less benefit from generative AI. This approach doesn’t provide the detailed, context that AI needs to generate good solutions.
There are many thinking and problem-solving structures. Software engineers will get a ton of value by adding other approaches, such as Spec-First Thinking, to their toolkit alongside Code-First Thinking.
Note: I said “adding spec-first” not “replacing”
Vibe Coding
I hoped “vibe coding” was viable. The idea that I could just describe what I want and see it appear. That I could build based on feeling and imagination.
I spent significant time trying to vibe code a real, working product that I actually used daily with real edge cases. Not a script. Not a blog. A product. It does not work at that scale. Our thoughts are not structured enough. AI is phenomenal for prototyping and closing that feedback loop, but you cannot “vibe” your way to a finished product.
Spec-first agentic development is not vibe coding.
What’s Next: Focused Building
My period of expansive exploration is shifting to a more focused one. Every domain is big enough to devote a lifetime to and I am so excited to use AI to build new things that I want to focus there
I have two areas of focus:
Developer Tooling: I see very few people using AI well. I want to build tools that help developers get real leverage from it. Specstory is doing great work here. Amp’s experimentation on the review process is also exciting.
Retrieval: The best way to improve LLM output is to control the context you provide. Retrieval is a key, and it’s a field that is lagging. Mixedbread is doing critical work in this space.
Conclusion: My Last Cohort
This journey has given me clarity. It has shown me what I can build and what I want to focus on.
AI is a tool that amplifies your ability to think and learn. There’s far more depth to using it effectively than people let on. I devoted six months to researching this nearly full-time. Nothing I’ve accomplished was impossible before, but I simply did not have the time to do it until I could use AI as an accelerator.
I still want to teach, but as a byproduct of my learning, not for income. This January will be my last cohort co-teaching the AI coding course with Eleanor. I'm sharing everything I've learned. If you want to learn how to use AI to accelerate your own work, join us. Eleanor is taking the course forward with exciting plans for the community — she'll share more in the new year.