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 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

I didn’t start because it was trendy. I started because I was tired of the repetitive friction.

I didn’t pick up AI because everyone was talking about it.

I picked it up because I was doing the same kind of work every day.

Writing similar test cases.
Debugging the same API failures.
Reading requirements that looked complete but clearly weren’t.

At some point, it stops being interesting.

AI didn’t change my role. It just removed a lot of the low-value effort that comes with it.

This is how I actually use it now.

I Don’t Start From Zero Anymore

This is the biggest change for me.

Earlier, every feature meant opening a doc and thinking from scratch. What are the scenarios? What can break? What should I validate?

Now I take the requirement and drop it into tools like ChatGPT or Claude and ask for a rough set of:

Functional scenarios

Negative cases

Edge cases

Collectively, we're only really months into AI-assisted software development, and the abyss is already clearly visible.

I'd say I'm (currently) a 'maximalist traditionalist' - I use AI to help me build better, reusable code as well as deeper test suites than I would previously have had the patience to grind out. I'm getting more of a feeling for when it's bullshitting me. I'm also able to debug things more effectively. Overall, I'd say I'm operating about 1.8x than before, with a probable ceiling of 2.0x. That's a combination of a bit faster, a bit more comprehensive, and a bit more effective all at the same time.

But for the kind of high-stakes embedded codebases I work on, I don't want to ship code I don't understand or can't completely explain or justify. In many ways, my ideal scenario would be finding ways to use AI to help me ship less code than before (i.e. that's compact, easy to understand and share, and better all over).

And so I look across to agentic stuff, and the gap between how I work and how that works seems like an abyss. The appearance of working doesn't mean it is actually working; passing tests doesn't mean the code wasn't gamed to pass those tests; black box code is a liability, not an asset; code review is next to impossible at scale; and so forth.

So where do we go forward from here? I'm guessing traditionalist maximalists like me will find ways of using agentic development for specific kinds of task, such as early prototyping, client-facing UIs, CI support tooling, etc. But that doesn't feel like a major step change.

More and more, I'm coming to think that the programming languages we use aren't fit for purpose at the scale and speed we're now able to develop at. Or perhaps we're missing a whole load of AI development patterns? There's certainly a lot to think about.

"AI will replace us all."

That's what most posts are saying right now.

But the reality is a bit different.

I use AI daily.

First with Cursor, then with Claude Code.

And I have to say: it gives a real boost for simple tasks.

It's useful for:
1.debugging (classic rubber duck debugging situation)
2.explaining business logic
3.speeding up my workflow — both on the code side and on unit tests

But this works because I know the fundamentals.
Frontend. JavaScript. TypeScript.

Recently, I tried something different:
porting a side project to Go, as a learning exercise.

I also used AI as a learning companion.

Asking it to explain Go concepts, syntax, and patterns I wasn't familiar with.

And here's what I noticed:
AI is strong at converting logic.

It's also decent at explaining things.

But without building the fundamentals myself, could I actually solve the problems that came up?

Not to mention - a lot of the generated code is potentially inefficient.

And without knowing Go, I wouldn't even spot it.

So yes, AI is powerful.

But only if you know the fundamentals of what you're doing.

You can use it to learn faster.

But you still have to do the learning.

Did you experience something similar?

🚀 I built this project using Claude Code but not casually.

I applied real workflow engineering principles behind it.

After going deep into how top teams use Claude internally, I realized most AI frustration is not about capability.

It’s about workflow.

So while building my upcoming project, I followed these principles:

1️⃣ Plan Mode First

Before writing a single line of code, I:
• Broke tasks into clear steps
• Wrote specs
• Reduced ambiguity
• Designed verification before implementation

No rushing into coding.

⸻

2️⃣ Subagent Strategy

For complex problems:
• Used multiple parallel explorations
• Offloaded research and structure analysis
• Kept main context clean and focused

Think of it like running a small AI engineering team instead of a single assistant.

⸻

3️⃣ Verification Before Done

Nothing was marked complete unless:
• Logs were checked
• Edge cases reviewed
• Behavior diffed between versions
• Production state verified

No “it works locally” mindset.

⸻

4️⃣ Autonomous Bug Fixing

Instead of micromanaging fixes:
• Pointed AI at logs
• Let it trace distributed flows
• Forced root cause analysis

Real debugging. Not patching.

⸻

5️⃣ Skill Reuse & System Thinking

Turn repeated tasks into reusable skills.
Reduce context switching.
Design process once, reuse forever.

⸻

6️⃣ Continuous Self-Improvement Loop

After every correction:
• Document the lesson
• Update rules
• Reduce future mistake rate

AI improves when your workflow improves

Code review has always been my least favorite part of being a software engineer. Ever since we’ve started using AI at work though, I’ve noticed that most of my day has become reviewing code.

I genuinely don’t understand how some people are enjoying this more than coding by hand. Sure, debugging has gotten WAY easier but building things is just not as fun anymore. It’s like the difference between doing a puzzle yourself vs telling someone to do it and checking their work.

My theory: maybe I’m stuck in a loop of reviewing and correcting because my prompts are not precise enough. Maybe if I spent a lot more upfront time thinking about design and tradeoffs, that’ll get my creative juices flowing again.

Has anyone managed to get that “craftsmanship” feeling back in the age of AI?

I’ve been using the Copilot CLI on a daily basis for coding, review, planning, design, and for debugging production systems. It’s awesome! Glad to see it reach GA. 👏

I expected to slow down. I didn’t expect to realize how much I’d already lost.

Let me be upfront: I’m not anti-AI. I use it every day. I’ve written about it. I’ve defended it in code reviews when teammates were skeptical.

But three weeks ago, I closed Copilot, stopped opening Claude for code, and went cold turkey for two weeks.

Not because I hate the tools. Because I started noticing something that scared me more than any production incident I’d ever worked through.

I was getting faster. And dumber.