Using AI tools does not make you less of a developer…Implementing them correctly can make you far more productive.

I used to scroll past developers talking about Claude Code, Codex and Gemini and quietly think are they actually writing code or just prompting their way through it?

Then I started using them properly. And I had to check my own bias.

Because the developers using AI well aren't skipping the thinking.
They're thinking faster.
They're shipping more.
They're spending less time on what they already know and more time on what actually requires them.

The keyword is: correctly.

AI tools used without understanding will give you code you can't debug, can't explain and don't actually own. But when you use them as an extension of what you already know, they multiply you.

That's not cheating. That's leverage and honestly, it's the direction engineering is heading whether we're comfortable with it or not. It becomes cheating when you’re still a beginner and you don’t know even the basics yet.

🌸 Developers avoiding AI tools to prove a point will soon be competing with developers who used that same time to build twice as much.

That's just the reality.
~ The tool doesn't replace your thinking, it responds to it
~ Understanding what the output means is still 100% on you
~ Using the right tools at the right time is part of the craft

Are you using AI tools in your workflow? Or still figuring out where they fit?

First time seeing my posts? I'm Nicholas Katende, a fullstack dev learning that building in public isn't about looking smart. It's about being real enough that people see themselves in the journey. Always happy to connect

I used AI every day for 30 days as a backend engineer.

Not for side projects or experiments — but in real work: debugging issues, writing code, understanding systems, and reviewing logic.

Some things became 10× faster.
Others actually made me worse.

Here’s what actually changed.

At first, I thought AI would mostly help me write code faster. That’s how it’s usually presented, as a way to speed up implementation or generate code automatically.

But after using it daily for a while, I realized that this is not where it actually brings the most value.

AI doesn’t really change how fast I type code. It changes how I approach problems, especially the parts of the job that are slow, repetitive, or mentally expensive.

The way I think about it today is simple: AI behaves like a very fast junior engineer.

It can generate ideas quickly, suggest implementations, and help explore possibilities. But it doesn’t truly understand the system, the business context, or the consequences of a decision.

Once I started treating it this way, it became much more useful — and much less dangerous.

I use Claude Code to build software every day. I describe what I want. It writes the code. I review, adjust, and ship. What used to take a developer a week now takes an afternoon.

But here's what nobody talks about in the "AI replaces developers" discourse: it only works if you know what to build.

Claude Code is the best junior developer I've ever worked with. Tireless, fast, zero ego. But it has no taste. No customer context. No sense of which features actually matter versus which ones just sound impressive in a demo.

I get disproportionate value from it because I've spent 20 years talking to customers, running experiments, shipping products, and watching what works. I have the judgment layer. The AI handles the execution layer.

A first-time founder using Claude Code without customer validation will build the wrong thing 10x faster. That's not leverage. That's expensive speed.

The pattern I keep seeing is that domain experts with 5-15 years of industry knowledge are the ones getting disproportionate value from AI coding tools. They don't need someone to tell them what to build. They just needed the building to get cheaper.

Three barriers collapsed simultaneously: the technical co-founder barrier (AI reduced it), the capital barrier (lean validation eliminated it), and the runway barrier (you can validate before you build). But the scarce resource shifted from capital to execution to problem clarity. And problem clarity can't be prompt-engineered.

AI made building cheap. The expensive part is now knowing what's worth building. That's where validation comes in.

Are you using AI to build faster, or to build smarter? There's a difference.

Cursor, Copilot, zero manual typing and the code review that made me want to delete my GitHub account.

You ever submit a pull request feeling like an absolute genius, only to watch it get dissected like a first-year biology frog?

Yeah. That was me. February. Twelve commits deep into what I genuinely thought was my best work in years. My reviewer a senior engineer who communicates exclusively in terse Slack messages and code comments that feel like parking tickets came back with fourteen inline comments. Fourteen. On code I didn’t fully write. On code I couldn’t fully explain.

“What does this function actually do? Walk me through it.”

I stared at that comment for a solid three minutes. I had accepted the suggestion. I had seen it pass the linter. I had shipped it. But walk you through it? That was going to be a problem.

Here’s the context: for the entire month of January, I ran an experiment. Every line of code I wrote or rather, every line of code that went into my repos came from an AI tool. Cursor for the heavy lifting. Copilot for the inline fills. I wrote prompts. I reviewed suggestions. I pressed Tab…

My thoughts on AI-assisted programming.

There is no shortage of AI tools aimed at developers right now: chat assistants, IDE completions, agents that promise to run your tests, and new products every month with overlapping features. I am not going to argue which one is “best.” Instead, here is what I am actually using today on hobby code: ChatGPT for quick, low-context questions, Cursor when the work needs my repository in the loop, and GitHub Copilot for fast inline help while I type. That trio might change, but it reflects how I have learned to spend money and attention in 2026.

The through-line is simple: match the tool to how much context the problem needs. That stops me from dumping half a repo into a browser tab for a vague design question, or firing up an editor assistant when I only wanted a two-paragraph explanation of something I could read in the docs.

This is not a product review. It is a snapshot of how I work, using the projects on my projects page as concrete examples.

I used to think AI would replace developers.
Now I use it every day — and it made me a better engineer.

When I started learning web development, everything felt slow:
• Debugging took hours
• Understanding docs was confusing
• Writing boilerplate was repetitive
• Building backend logic felt complex

Then I started using tools like ChatGPT.
And everything changed.

⚡ Faster debugging
⚡ Clear explanations of complex concepts
⚡ Quick boilerplate generation
⚡ Faster learning of frameworks like Next.js and Node.js

But here’s the reality:

AI didn’t replace my skills.
It amplified them.

Because AI still cannot replace:
• System design thinking
• Backend architecture decisions
• Scalability planning
• Real-world problem solving

Lesson
The future of software development belongs to developers who:
✔ Use AI as a tool
✔ Focus on solving real problems
✔ Build scalable systems

AI is not your competitor.
It’s your productivity partner.

Are you using AI in your development workflow?
Or are you still avoiding it?

#AIForDevelopers #SoftwareEngineering #DeveloperProductivity #WebDevelopment #Programming #FutureOfWork #FullStackDevelop

Every developer has a workflow they've spent years refining.

Mine lives almost entirely in the terminal. I'm SSH'd into a development machine. I organize my sessions with tmux, edit code in Vim, and use git worktrees to juggle multiple tasks in parallel.

When people talk about AI-native development, the conversation sometimes assumes developers will converge on the same new workflow. A new IDE, a new interface, a new way of building software.

A senior engineer’s journey to using AI tools like ChatGPT, Claude, Cursor, and Gemini to improve productivity and rethink modern software development. From an AI chatbox to an architect of AI agents, this is how artificial intelligence is changing the way we work today.