Explaining codebases, service flows, documentation, technical concepts, legacy systems, and planning changes

✅3 simple ways on how I use AI as a software engineer at Google in my day to day life.

1️⃣. Understand Faster

Whenever I pick up a new feature or work area, I ask AI to explain the code flow, tell me the services involved and generate sequence diagram.

The main thing I ask for is the exact files and code links of the flow. Then I verify every step, it’s still some manual effort but I am sure of my understanding, I gain the knowledge for future and am 100% confident to make any design choices.

2️⃣. Code Better & Faster

AI is great at writing code. But it might skip some details, like sometimes a function may already exist in the hierarchy but it defines it again.

I usually avoid it with the first step- understanding things in and out. I also prompt AI to review the code as a lead engineer.

The same goes for tests, there can be issues. I always make sure to list down all the edge cases I can think of. Then I compare that checklist against the generated tests to make sure nothing is missing. I once wrote 45 test cases for a 8 line change.

3️⃣. Write Docs Faster

Take design doc for example: I attach all reference docs where I got all the info from & a design template & then I turn on voice mode and just talk and explain my thought process.

I get a really good draft and starting point. From there, I iterate and if it’s not working out in 5 prompts I write some sections manually(great for long term memory).

For me, AI really helps me with everything, has made me so much productive and saves me a lot of time.

There are many other ways I use AI, for example custom agents etc. Let me know in the comments if I should share that next.

#AI #SimpleUseCases #Productivity

AI didn’t replace me — it changed the way I think, build, and solve problems.

A few years ago, my day as a developer looked very different.

I would spend hours searching Google for solutions, reading Stack Overflow threads, watching YouTube tutorials, and trying different approaches until something finally worked.

Claude Code vs Codex vs Cursor vs Local AI Coding Agents — my honest experience as a Python developer

AI coding agents are everywhere now.

Every week, some new tool says it can write your code, fix your bugs, understand your project, create full apps, refactor your files, and maybe even replace a junior developer.

But after using AI coding agents in real work, I learned one simple thing:

Most AI coding agents are impressive in demos, but very different when you use them inside your actual project.

A demo project is clean.

Real projects are messy.

Real projects have old files, confusing names, half-written logic, wrong comments, hidden bugs, weird dependencies, and that one file you are afraid to touch because even you don’t fully remember how it works.

So I tested four types of AI coding agents in my own workflow:

Let me save you some time upfront: I’m not going to tell you it changed my life.

A month ago, I wasn’t even looking for a new AI coding tool. I already had a workflow that worked. Claude.ai for code reviews, Axios for API calls, VS Code for everything else. Then I kept seeing developers talk about Claude Code — the terminal-based version, the one that reads your entire codebase automatically.

Some said it was the best coding assistant available. Others said it wasn’t worth the price. Most reviews were either suspiciously positive or completely dismissive.

So I stopped reading opinions and started testing.

For 30 days, I used Claude Code on a real production project. Not a side project. Not a tutorial app. A form-heavy medical examination system built for a state government — complex conditional logic, multiple examination form components, Java DTO integrations, file upload flows, and zero tolerance for bugs.

Most "Claude made me 10x faster" posts are about generation — scaffold this, write that function. The usage data I keep coming back to points the other way.

A senior engineer at Sentry actually instrumented her own Claude usage across 116 sessions and classified every one. The split: 67% comprehension, 2% code generation. She works in a codebase that's 15+ years old with ~100 PRs merged a day — and in that environment the bottleneck was never "write the function." It was "why does this exist," "where did this regression come from," "what changed while I was out."

Here's the part worth stealing...

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

A practical look at Anthropic’s agentic coding tool — what it actually does, how it changed my workflow, and whether it’s worth your time.

I'll be honest — I've always been a bit cynical about the glorified learning posts on LinkedIn. But here we are.

6 weeks ago I had never written a single line of code. I always wanted to learn - but never took the time! I had a rough concept of vibe coding - but no idea what to build. Until I found my "have to fix it" problem.

Territory planning reviews were taking hours of pivot tables, multiple dashboards, and customer PowerPoints - and still leaving gaps in the story. That felt like something worth breaking.

So I did. With GitHub Copilot powered by Claude as my co-pilot, I vibe coded my way through React, TypeScript, and data joins I didn't fully understand until they fell apart. Then I rebuilt them. Then broke them again. Then GitHub Copilot fixed them!

The result? A self-contained territory planning web app for our Security business - multi geo versions, one file, no install. Uniformed approach across our entire team.

What I took from it:
→ You don't need to understand everything to build something useful
→ Breaking things is faster than reading about them
→ AI doesn't write your solution — it helps you think through the problem

If you're sitting on a problem you think needs a developer to solve, it might be worth trying it yourself first.

Worst case you learn something. Absolute best case you ship something.
Middle ground - you have a great demo and someone far more qualified takes it from there. (we still need developers!!)

I've been a software engineer for basically my whole life, at least ever since I first taught myself to write HTML code when I was 12 years old, and in the modern era when Artificial Intelligence and Large Language Models are commonplace, I of course have adapted with the times, as I had done before whenever a new paradigm shift in the engineering landscape had come about.

In this modern era of A.I. vibe coded apps that get built and deployed while full of bugs and security holes, which then get trivially hacked in short order and the company's data all leaked online, I thought I'd write a little about how I, a career software engineer since 2008, approach the use of A.I. in my projects.

If you happen to be a junior developer just getting started in this space and relying heavily on A.I. to do most of your job, maybe reading about my approach to A.I. could be helpful to inspire you on a different way of doing it, and help you grow as a self-sufficient developer who uses A.I. only as a tool to automate the tedious parts but without it being a crutch that you rely on too heavily that you couldn't survive without it.

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.