Dear Coder: Open This If You're Feeling AI FOMO

Cesar, good to see you again after the AMA post. Your point on fundamentals holds true in my own RAG work too. An AI can wire together a retrieval pipeline in minutes.

It cannot tell you if the chunking strategy is wrong or if the embeddings match the real query pattern. Bad retrieval fails silently.

It just returns confident, wrong answers. That gap only shows up if you understand the fundamentals yourself.

Saying "pick your battles" out loud is the useful part of this. Nobody can keep up with a new MCP/agents tutorial every week, and deciding what to ignore is a skill in itself.

• N+1 problems
• Missing pagination
• SQL injection Most were written before the AI hype. Now imagine the chaos inside vibe-coded codebases.

True! If AI is just mixing what everyone has written in the past, the future can only be messy, at least until training sources are curated.

And yeah, just programming has expanded to so many fields and techniques, that it's impossible to comprise it all. Just learn what you need.

This really resonates with me. I use AI pretty heavily in my development workflow, and for a while I definitely felt that pressure to understand everything, agents, MCP, new models, new tools, whatever appeared that week.
but what I’ve been realizing is that learning how software actually works matters more, not less, when AI is involved. AI can generate an implementation incredibly quickly, but I still have to understand the architecture, recognize when something is wrong, test it, debug it, and decide whether the solution even makes sense.
So lately I’ve been trying to treat AI as leverage rather than a substitute for learning. Build things, encounter problems, learn the fundamentals those problems expose, and let AI accelerate the execution.
And honestly, “pick your battles” might be the most important part. Nobody can keep up with the entire AI ecosystem anymore. 😅

I tried for the most part avoid AI in the beginning but right now it's become integral for day-to-day work and not using it means dropping behind extremely quickly. However, you don't really have to stay on top of everything cause I feel like everything is drastically over hyped in the space these days, any AI skill, plugin or tool comes out and then it's sub 100k stars on GitHub.
You just have to use it, keep an eye out for important decisions, make sure it sticks to the best practices that were built and used in the codebase, and most importantly, try to understand what the output is, what every task's goal and context is and not just auto accept slop with your eyes closed. I found this to be a really good work flow, and one thing is helping me much with this today is a skill I came across from a design engineer at Notion called explain diff, it gives you like a quiz, intuition and background on every task so you are still grounded down in the weeds if every task

I think there's another side to AI FOMO: it's not only the fear of missing a new tool. It's the fear of missing a path. Every week there is a new model, agent, framework, MCP server, workflow, or prompting technique. If you try to learn all of them, you can end up spending more time tracking the ecosystem than building anything.

For me, the better question isn't:

“What AI technology should I learn next?”

It's:

“What am I trying to build, and what problem do I need to solve?”

For example, if I'm debugging an API that suddenly becomes slow, I don't need to learn the newest AI agent framework first. I need to understand what is actually happening.

Maybe the endpoint is loading 10,000 records, triggering an N+1 query problem, or returning an unnecessarily large payload.

AI can help me investigate, generate a query, suggest an optimization, or write a test. But I still need to recognize the problem, evaluate the solution, and verify that the optimization actually improves the system.

That's how I see AI. Not something I need to constantly chase, but another instrument in the engineering process.

'Problem → Architecture → Build → Test → Learn → Improve'

If a new AI tool helps that loop, I'll use it. If it doesn't, I don't need to learn it just because everyone is talking about it. Sometimes avoiding FOMO is simply knowing what 'not' to learn yet.

The line that hit me: "Don't forget about AI. But build real skills first. Then leverage AI — in that order."

I'm the exact person this post is talking to. Three weeks into Python. Writing beginner tutorials. Trying to break into technical writing. And surrounded by people talking about MCP, agents, and "loop engineering" while I'm still figuring out why range(1, 6) stops at 5.

The FOMO is real. I've felt it. There's a small voice that says "why are you writing about lists and dictionaries when everyone else is building agents?"

Here's what keeps me sane: the fundamentals aren't a stepping stone to something more impressive. They're the thing that makes everything else make sense. When I eventually learn MCP or agents, I'll actually understand what's happening — because I'll have the foundation.

I published 16 articles in three weeks. Every single one is about something basic. And I've learned more from writing about basics than I ever did from reading about advanced topics.

The hype cycle will keep spinning. The fundamentals won't.

Good post. Saving it for the next time I feel that voice.

BTW, your gumroad link is wrong, you missed the middle /l/ part in the post.

I really agree with the point about software engineering fundamentals remaining important. With AI writing more of the code, understanding those fundamentals is arguably even more important because you still need to evaluate, verify, and make good decisions about what the AI produces.

At the same time, I don't think we can ignore that AI is here and becoming a major part of how software is built. Learning to work with it can make us significantly more productive without necessarily weakening our skills — as long as we don't completely hand over the reins.

For me, the goal is finding a good agentic development workflow where AI handles more of the execution, while the developer remains responsible for the architecture, decisions, context, and verification. The challenge now isn't just learning AI tools, but learning how to use them without losing the engineering thinking behind the work.

Absolutely agree on the principle and practice of retaining fundamentals of programming and software engineering.