u/areyprabhu

I joined a new company recently, but my previous company still wants me to work with them on good terms. The work has nothing in common with my current organisation, no conflict, no competition.

But my new company says they don’t want me working anywhere else.

And honestly, this is what confuses me.

Outside office hours, that’s my personal time. As long as I’m not harming the current organisation, leaking information, or affecting my performance, shouldn’t I be free to decide how I use my own time?

Feels strange how companies expect “ownership” over employees even beyond work hours.

I was working on an MVP a few weeks ago and decided to go deep into the whole agentic AI setup because honestly that’s what the entire internet makes you believe is the future.

So instead of keeping things simple I built this elaborate workflow with multiple agents tools memory retrieval loops autonomous retries the whole thing. In my head I thought I was building some next generation autonomous system that could practically run itself while I slept.

And technically it did run itself while I slept.

The problem was one of the agents got stuck in a loop overnight.

Not a dramatic crash. Not an error message. Just endless retries context loading tool calls and recursive reasoning chains burning tokens every few seconds like a taxi meter that never stops running.

I woke up the next morning and realized I had burned almost $4K overnight.

That moment genuinely changed how I think about AI systems. Everyone online talks about bigger context windows more agents more autonomy more orchestration but almost nobody talks about operational discipline. Rate limits. Budget guards. Kill switches. Loop detection. Fallback logic. Hard caps on retries.

I learned the hard way that when people say “inference costs are going down” they usually ignore what actually happens in real systems. One badly designed workflow can destroy weeks of budget before you even notice.

Now my entire philosophy is different.

I still believe in AI and I still build with it every day. But I no longer try to build the most complex autonomous system possible. I try to build the simplest system that reliably solves the problem.

Honestly that $4K loss was probably the most valuable engineering lesson I’ve had in years.

Everyone keeps saying software engineering is dead and the job market is horrible.

I honestly don’t think that’s true.

What’s actually happening is that the market for average skill sets is getting destroyed.

I started job hunting 2 months ago and already have 2 offers in hand while still interviewing with other companies. Opportunities are there, but the expectations have changed.

A lot of people are still spending months manually grinding LeetCode or writing everything from scratch while the industry has already moved forward.

My advice:

- Learn coding deeply enough to understand architecture, debugging, optimization, and how systems work.
- Stop glorifying manual coding.
- Use AI coding tools aggressively.
- Build large, difficult projects fast.
- Put REAL projects on your resume instead of tutorial clones.
- Learn system design because that’s what separates engineers from coders.

And during interviews, energy matters a LOT more than people think.

If you sound unsure, low-confidence, or passive, interviewers feel it immediately. Be confident, communicate clearly, and lead the conversation instead of waiting to be judged.

Companies don’t just hire coding machines.
They hire people who look capable of solving problems at scale.

Recently received an offer for a Senior AI/ML Engineer role. The package is good, work looks challenging, and I’ll be leading around 12–15 juniors.

On paper, it sounds like a great opportunity. But something feels off. The HR I was talking to already resigned, and her manager gave me a slightly toxic vibe during the process. It’s also a relatively new startup, which makes me wonder how stable or healthy the work culture actually is.

Part of me feels this could be a huge growth opportunity. Another part feels like this might turn into constant pressure and burnout.

Would you still join if the compensation was good, but the culture already felt questionable before even joining?

In the last 2 months of interviewing for AI/ML roles, I’ve noticed something odd.

Different companies, different roles, but almost the same 4–5 questions every time. It feels less like a conversation and more like a script. Which makes me wonder, are interviewers actually evaluating candidates, or just repeating a checklist. Because when every interview sounds the same, it’s hard to believe the process is really measuring how someone thinks.

Feels like we’re optimizing for predictable answers, not real understanding.

Curious if others have experienced this

I’ve gone through a few AI interviews recently, and the experience felt quite disconnected from real-world work. The questions were often too theoretical or niche, and there’s a strong expectation to answer in a very structured, to-the-point format.

I spoke to a few others, and their experience was pretty similar. Interestingly, some who actually cracked these interviews admitted they relied on tools like ChatGPT or Claude during the process.

Not sure if this is the direction we want hiring to go in, but it definitely feels like the system is being gamed more than it’s evaluating real ability.

P.S. I do use tools for grammar, but these are my own thoughts.

Been interviewing for AI/ML roles for the last 1.5 months.

Every interviewer asks the same thing,
“Why are you looking to leave your current job?”

And I realized something. They don’t actually want the real answer. Because in most cases, the truth is simple: better pay, better growth, better opportunities. But the moment you say that directly, it changes how they see you.

So instead, you’re expected to give a well-structured, diplomatic answer. Not the truth, but the “acceptable version” of it. It’s strange, both sides already know the real reason,
But the process rewards how well you can package it.

Curious if others have felt the same

A court in Hangzhou, China ruled that companies cannot fire employees just because AI can do their job. In one case, a worker’s role was replaced by AI, then he was demoted, had his salary cut, and was eventually fired. The court said this was illegal under labor law.

The key point was simple, AI adoption alone is not a valid reason to terminate someone. On paper, that sounds like strong protection.

But in reality, it doesn’t really stop what’s coming. It just changes how companies approach it.

They don’t need to say “AI replaced you.” They can raise expectations, restructure teams, and shift work to people already using AI effectively.

Now one person with AI produces more, and others start to look like they’re underperforming. No rule broken, just a different path to the same outcome.

Had an interview where they asked me to write 5 different programs back to back. Not DSA. Real coding. But the catch, no documentation, no AI tools, nothing. I solved two, then stopped. Because this isn’t how I work.

In real life, I use docs, I use tools, I build systems. I don’t memorize every library function like a parrot. And that’s what confused me.

If AI already writes most of the boilerplate, and real engineers rely on docs daily, then what exactly are interviews trying to measure Memory Or problem solving?

I’m an ML engineer. My job is to build, ship, and solve problems, not recall syntax under pressure. Feels like the hiring process is stuck in a different era.

Curious how others see this

A top MNC shortlisted my resume. I went through 2 interview rounds and did pretty well.

HR told me I was shortlisted, but before the final round she said they want to lower the salary we discussed earlier. I had already adjusted my expectations at the start, so I refused. She said no problem and told me she would schedule the next round.

Then came multiple follow ups from my side. Either she was on leave or the panel was busy or she didn't picked the call and then one day, I get a message saying my profile is rejected. No final round. No explanation.

PS: I got an offer today where my expectations are respected.

I started in 2nd year, so I have around 4 years in ML. As an ML engineer, I’m supposed to solve maths, stay updated, and read research papers every week.

I never did competitive programming. I focused on real work, training models and freelancing. That’s how I got my first job. Then ChatGPT came, and exposed something and a lot of people spent years solving patterns, but struggle to build anything real.

Now AI writes most of the boilerplate, debugging is faster, learning is faster. So what exactly are you optimizing for? I’m not saying DSA is useless. It’s just not the edge anymore.

The edge now is building, shipping, and knowing how to use AI better than others. If all you have is solved questions, you’re replaceable and That’s the uncomfortable part.

Curious how many people actually disagree with this

Recently I had an interview...

He asked few questions and I gave the suitable answers.

Then he asked something that is really irrelevant (I confirmed it after the interview) So, I told him that whatever he is saying is not correct and tried to explain him the exact answer.

I think I somehow demolished his tower of EGO.

After that the interview went pretty bad, not because I'm inefficient but because he was asking for the things that I explicitly told him, I don't know this stuff.

PS: This one didn't ruined my day because I got an call from another HR and most likely I'm going to get an offer letter this week🤗

Be strong in DSA, theory, math, classical ML, deep learning, computer vision, NLP, LLMs, agentic AI… and somehow also keep up with every new framework that drops every other week.

At this point, it feels like the bar isn’t “be a good engineer”, it’s “outperform an LLM on token throughput.”

PS: Just a mildly frustrated engineer trying to keep up.