AI for Application Developers

You don't have what it takes to be a programmer!

That is what my high school informatics (that's what it was called at the time) teacher told me. She wasn't entirely wrong. In the 90s, being a programmer often required a strong aptitude for mathematics—something I clearly lacked. Back then, if you needed a list sorted, you had to implement the algorithm yourself. We were "tortured" with QuickSort and MergeSort in class; virtually everything we did with computers was rooted in manual math and low-level algorithms.

Fast-forward thirty years. Today, millions of software developers build amazing solutions without ever touching trigonometry, linear algebra, or stochastic calculus. While algorithms remain essential, we operate at a significantly higher level of abstraction. For lack of a better term, I call this group Application Developers. We are the people who design and build the libraries, tools, and end-user applications that run the world. I've occupied that space throughout my career, from junior developer to technical leadership.

The Missing Middle Ground

In my observation, application developers are struggling the most as the AI revolution unfolds. The landscape is heavily polarized. On one extreme, you have the scientific perspective: academic papers bloated with linear algebra equations and Greek symbols we haven't seen since university. On the other extreme, you have "Look ma, it can do..." marketing: flashy demos that glorify specific products without explaining how they work.

Neither is helpful if you want to understand the AI/ML space as an architect. You don't necessarily want to train your own models from scratch, but you also don't want to settle for just calling a "black box" API without understanding the implications. As the old engineering adage goes: you must understand at least two layers of abstraction below the one you are currently working in.

For the past two years, I've been collecting notes to build a mental map of this space—primarily to guide my own software architecture decisions. Last year, I converted some of those notes into a talk called AI for Java Developers. When it placed in the top ten talks at DevBCN 2025, it became clear that I wasn't alone. Many developers are looking for a practitioner's perspective: the practical pros and cons of different implementation patterns.

The Notes Collection

"Why don't you just publish your notes? You know, as good, old-fashioned knowledge-sharing," someone asked me recently.

I've decided to do exactly that. I've realized that waiting for the "perfect" expert to explain this is a mistake—the industry needs more input from people who actually ship code and maintain systems. Because my notes are currently too chaotic to share as-is, I'm committing to converting them into a series of coherent blog posts.

My goal is one post per week, probably on Mondays so I can prep it over the weekend. I'm not launching a Substack or a YouTube channel to build an audience; I'm just sharing the mental models that helped me stop feeling like an outsider in this new stack.

The Roadmap

I'm posting this for a few reasons. First is to make a public promise that forces me to stick to the commitment.

Another reason is to get early feedback on what you actually want to learn. If you're an application developer, please use the comments section below to tell me what would be most valuable to you. Are you wondering when to use a model vs. a traditional algorithm? Are you trying to figure out how to run these models locally instead of relying on a third-party API? I can't promise I'll have all the answers, but if I've hit that wall already, I'll share how I climbed over it.

Finally, this post will serve as a table of contents for the series. I'll update the list below as each new post is released.

From code to AI/ML model - simplest example
Understanding model types and architectures (not just LLMs)
How Inference Works
Inference Runtimes
TBD

I've added a "Get new posts by email" feature to the blog section of my website for those who want notifications, but you can just as easily use the RSS feed or track my social media posts.

Finite and Infinite Startups

Three weeks ago, Adam Wathan, the creator of Tailwind CSS, released a podcast containing an honest confession – "We had six months left" – and an explanation of why they had to let some folks go. For days, my social media feeds buzzed with discussions about the subject. I saw everything from "AI is killing startups" to "he doesn't know how to run a company," and even ridiculous accusations of deliberate exaggeration (why say 75% when it "only" affected three people). I never cease to be amazed by how many people fill the gaps left by scant information with assumptions and rush to judgment.