Everyone is talking about AI-generated code.

But after using Claude Code, I realized the real shift isn’t code generation—it’s how we interact with software development itself.

Instead of writing every line manually, we’re moving toward a model where engineers define goals, constraints, and architecture, while AI handles much of the implementation.

A few observations:

-AI is excellent at accelerating repetitive coding tasks.

-It can navigate large codebases faster than most developers.

-It helps reduce context-switching by keeping architectural discussions, debugging, and implementation in one place.

But it still struggles with ambiguous requirements.It doesn’t replace system design, domain knowledge, or engineering judgment.

The engineers who will thrive in the AI era won’t be the ones who can write code the fastest.

They’ll be the ones who can:
• Break down complex problems
• Design scalable systems
• Write clear specifications
• Validate AI-generated solutions
• Ask better questions

Claude Code isn’t replacing software engineers.

It’s changing what software engineering looks like.

The future may not be “AI vs Engineers.”

It may be “Engineers who effectively use AI vs Engineers who don’t.”

What’s been your experience with Claude Code or AI coding assistants so far?

#AI #ClaudeCode #SoftwareEngineering #GenAI #LLM #DeveloperProductivity #SystemDesign

🚨 AI will definitely replace a lot of Junior and Mid Level Developers

We are hearing these kind kind of most from so long now but it might not be developer but the work it is going to replace.
I recently built multiple Angular + .NET applications using ChatGPT Codex.
Some of the projects include:

✅ Employee Management System
✅ Support Ticket System
✅ Doctor Appointment Booking System
✅ Survey Application
✅ Quiz Platform

All these application were role based project , some has excel file Upload, One to one and One to Many Relationship and all real time project scenarios.

The scary part? Writing code was no longer the hardest part.
Understanding the requirement was.
AI can generate components, APIs, CRUD operations, SQL scripts, and boilerplate code surprisingly well.
The bottleneck is slowly shifting from coding to thinking.
The question is no longer:
"Can AI write code?"
The question is:
"Can you use AI better than other developers?"

Wheather its Codex, GittHub Copilot or Claude all perform greater.
After building these projects, I am more convinced than ever that developers who learn to work with AI will have a huge advantage over those who ignore it.
🎥 I'll share the complete project playlist in the comments for anyone interested in seeing the actual development process. Each video is live on GitHub page so you can check live Version too.

🔁 Repost if you've noticed the same trend.
👉 Follow me Chetan Jogi for Angular, .NET & AI Development content and Interview Questions

#AI #ChatGPT #Codex #Angular #DotNet #SoftwareDevelopment #FullStackDeveloper

Opus 4.8 in Claude Code and Claude AI with “Effort control, dynamic workflows, mid-run API updates”, and a model you can trust running unattended — a builder’s first-look at what Claude Opus 4.8 changes.

A new meta-analysis on GenAI coding assistants landed on arXiv this month, and I think it is a useful cold shower for both sides of the argument.

The paper looked across 23 studies and found a statistically significant productivity gain from GenAI assistance in programming. Not magic. Not fake. A real effect.

But the effect was moderate, highly context-dependent, and smaller in open-source and enterprise settings than in controlled experiments. It also found no statistically significant learning gain.

That is basically the whole AI coding debate in one sentence: the tools help, but the demos are not the work.

Over the past few months, I’ve been creating projects/applications with AI-powered coding assistants, and the experience has been nothing short of transformative. What started as curiosity has evolved into a fundamental shift in how I approach software development. Here’s what I learned about the capabilities, limitations, and best practices of coding with AI.

The 7 AI coding skills I use every single day.

(All free to download):

If you spend any time in AI circles online, it's easy to come away thinking you need hundreds of skills, dozens of plugins, and an ever-growing stack of MCP servers to be productive with coding agents.

I've come to believe the opposite.

The engineers I see shipping the most consistent, high-quality work tend to use a small number of well-designed skills that map to the workflows they repeat every day. Planning, implementing, reviewing. That's most of the job.

I've spent the last couple of years building, breaking, and rebuilding my own toolkit. It's settled into just a few skills that I genuinely use every day across professional projects, and that I'd happily defend as the only ones most engineers need.

I just put together a full video walking through all seven, with live demos in Codex (though they work fine in Claude Code or any other agent).

I show how I use each one, why it earns its place, and the pattern underneath them that I think matters more than the list itself.

https://lnkd.in/e_r62kya

Every skill is free and linked in the description so you can grab them and try them yourself.

If you've got a skill you swear by that you think I'm missing, let me know in the comments.

The best part of working in public is the steady stream of better ideas coming back from people who've solved problems I haven't noticed yet.

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♻️ Repost if you found this useful.

AI isn’t replacing software engineers - but it *is* changing who gets hired.

In this video, I break down what’s actually happening in the dev market right now - based on real experience, real data, and what I’m seeing inside teams.

I use AI every day to write code. It’s made me faster, more productive, and honestly… more valuable. But there’s a tradeoff most people aren’t talking about - and it’s quietly reshaping the job market, especially for junior developers.

We’ll cover:

* What AI is actually doing to mid-level and senior engineers
* Why junior roles are disappearing (and what the data says)
* The biggest mistake most junior devs are making right now
* What you need to do differently to actually get hired
* The 4 career paths that are still growing in 2025

This isn’t hype or fear - it’s a realistic look at where things are going, and how to adapt.

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📌 If you're:

* Trying to break into tech
* Already working as a developer
* Or just curious about the future of AI and jobs

This will give you a clear picture of what’s changing - and what to do about it.

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🎥 **Watch next:**
Python Isn’t Enough to Get Hired in 2026 — Here’s What Actually Works
👉 https://www.youtube.com/watch?v=aBymUMEu0PM&feature=youtu.be

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💬 Let me know in the comments:
Which of the 4 paths are you focusing on?

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⏱️ Timestamps
0:00 - AI made me more valuable… but we hired fewer engineers
0:45 - Python → C++ in 2 weeks (real example)
2:50 - How AI is making developers more efficient
5:00 - The Shift in the Junior Dev Market (and how to prepare!)
5:40 - The real hiring formula
7:20 - Stop being a generic developer
8:05 - The 4 buckets that are hiring
10:00 - The future of software jobs

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👍 If this helped, consider subscribing - I post breakdowns of the dev industry as it actually is right now, not as it used to be.

I was skeptical at first.

Not about AI in general — but about whether it would actually fit into my workflow. I work mostly with legacy PHP and jQuery. The kind of codebase that was written before half the frameworks people talk about today even existed. Some Vue.js here and there in newer parts, but a lot of the core is raw PHP, procedural logic, and jQuery doing things you probably don't want to know about.

It's not glamorous. But it's real work, and it has its own kind of complexity.

When I started using Claude more seriously, I wasn't expecting much. Turns out I was wrong.

AI Product Management Software Development

One-shotting is everywhere right now. Write a single prompt, maybe drop in a screenshot, and watch a full app materialize while you keep your fingers crossed. It’s a great demo. It’s a terrible workflow.

The problem isn’t that AI can’t generate a lot. It can. The problem is that a massive all-in-one output gives you no seams to work with. If something is wrong, which is less likely with frontier models but never impossible, you’re either redoing the whole thing or accepting the mess. There’s no middle ground.

One-shotting also doesn’t scale with complexity. A simple landing page, sure. A real product with multiple pages, components, navigation, visual consistency? The output starts degrading fast. You end up with something that looks right in a screenshot and breaks in production.

This gets worse in a team. If everyone is one-shotting independently, there’s no shared reference point. Components drift. Decisions contradict each other. A spec gives the team a common language. It also gives the AI a common language across multiple sessions. It’s the difference between everyone improvising and everyone playing from the same sheet.

I’ve been through this, and I think I’ve figured it out.

I had a draft post sitting in my local repo for a while, where I was about to scream about how AI is overestimated. Well, that post aged pretty badly. I never published it, and looking back at the notes I’m glad I didn’t. So what I’m going to write today will only be about my current workflow and how I actually use AI in my daily work — no hype, no predictions, just what I’ve found useful.