This is mainly a question for those of you who are employed somewhere where AI coding is enforced. Do your employers still expect you to be accountable for the code you ship? Or has that part of your job description been altered?

I would personally not be comfortable accepting a job where I'm pushed to maximize my velocity with AI, as that would mean I wouldn't be able to understand the systems I was shipping. The only reason I would think that would be acceptable, is if I did not have personal responsibility for code that breaks, security vulnerabilities, etc.

Anyone with a photocopier could have copied our product. What they couldn’t copy was the trust of students and the media coverage that put 80,000 visitors on our site in a single day.

I think about that hall often now, because software is becoming the photocopier.

AI is turning code into a commodity. The second-order effect few people discuss is what this does to the software talent market.

If code stops being the differentiator, what happens to the premium paid to write it?

Here is what I expect:

→ Demand moves to moats that aren’t code. Proprietary data, distribution, physical operations, trust, regulation. Companies without one face margin compression. Companies with one won’t need a 500-person engineering team to defend it.

→ The compensation premium compresses. For a decade, pay was lifted by cheap capital and the rarity of execution. When execution becomes table stakes, benchmarks recalibrate. Not overnight, and not for everyone, but the direction is clear.

→ The top of the market pays for something different. Not just the best builder of scalable systems, but people who understand unit economics and CAC, who orchestrate AI workflows to do the work of a team, and who choose business leverage over engineering vanity.

I’m 7 months into my third startup. We have no moat yet. Building was never the hard part. Getting the right customer to trust us is.

Code used to be an asset. Pure code is now a commodity.

Distribution is the prize.

If you hire engineers: what do you screen for today that you didn’t two years ago?

Is the future of programmers becoming architects? They will specify the architecture for AI, decide on the database to use, communicate between different modules, and design the debugging system. I believe that a completely black-box system is untrustworthy, and allowing AI development to proceed without proper oversight will ultimately lead to chaotic architecture.

Hi everyone,

I have around 4 years of experience as a PHP developer, mainly working with Laravel. Recently, I started learning Java and Spring Boot, and I'm becoming really interested in this technology.

I want to switch my career to Java development and would love to hear from people who have made a similar transition.

How was your experience? What kind of job opportunities did you get, and how difficult was it to land your first Java developer role?

Any advice would be appreciated. Thanks!

Hi guys, I’m just here to vent a little.
I’m a software engineer, and while I think AI is great for what it is, my real passion lies in software development itself. Lately, I’ve been feeling like I’m not being taken seriously at work because I’m not constantly showcasing AI-related innovations.
I bring forward ideas and innovations around software development, but I feel like they’re often underappreciated. I even tried proposing an AI-related innovation once, but my supervisor shelved it. Meanwhile, my supervisor and others seem to praise almost anyone who comes up with something AI-related, even when those innovations aren’t necessarily complete, production-ready, or scalable.
It’s frustrating because I feel like my software engineering skills aren’t valued as much as they should be. I genuinely enjoy software development, learning about system design, building scalable applications, and improving my technical skills. But now I’m starting to wonder whether I should put some of that aside and focus more heavily on AI.
I recently got an AI textbook because I’m considering shifting more of my attention towards AI, even though it isn’t necessarily where my passion lies.
I feel terrible about the whole situation. It’s almost as though, no matter how well I perform as a software engineer, the people working on AI-related projects will always be seen as more valuable.
I also recognise that I have areas to improve. For example, I think I sometimes get too technical when discussing software development, and I’m working on communicating my ideas in a way that’s easier for others to understand and appreciate.
I’m not against AI at all. I just wish good software engineering received the same level of recognition and excitement.
Has anyone else experienced this? How do you navigate an environment where AI seems to get all the attention, even when you’re delivering solid engineering work?

Got another unsolicited recruiter email today, this time for an engineering role at a Series A startup. I was about to delete it when this line caught my eye:

No equity or relocation support listed.

I checked the actual job listing. No equity.

Has the job market really gotten this ridiculous? A 25-person startup wants engineers to work onsite five days a week in San Francisco, and they’re offering zero equity?

For most people, the main reason to join a startup is the chance that your equity might eventually be worth something. You take on the risk, the long hours, and the chaos because you’re betting on the upside. Take that away, and I honestly don’t know why anyone would bother.

This company sounds cheap as hell, but it also says something about how much power employers have right now. They can keep pushing worse terms because they’re confident someone will take the job anyway.

Full recruiter email below:

I’m working with Fulxxxx, a 25-person company building AI for commercial insurance brokerages. They’ve raised a $25M Series A, grown from zero to eight figures in revenue in 18 months, and serve approximately 30% of the top 100 US brokerages.

They’re hiring a Member of Technical Staff (Product Engineer) to own backend-focused full-stack products, from new MVPs to scaling established workflows. You’ll work on document processing, browser automation, third-party integrations and a self-service platform.

This is onsite five days a week in San Francisco. They want 4+ years in full-stack or backend engineering, CI/CD and production deployment experience, strong ownership and cross-functional collaboration. Startup experience is a plus.

Stack: TypeScript, Python, React, Flask, Stripe and CI/CD.
Comp: $170K-$320K Base. Health, dental and vision insurance. Visa transfers supported. No equity or relocation support listed.

I've been away from programming for a couple of months now. I've built a dozen simple projects, both web and mobile, and deployed a few basic web apps to Vercel, but I've never shipped anything serious to production.

I constantly feel like I'm not good enough. With the current job market and AI changing the way people write code, I'm stuck between two approaches. Some people say to focus on fundamentals and learn to code everything yourself, while others say to learn agentic AI and adapt to how the industry works now.

For context, I studied software engineering at university. I'm neither a complete beginner nor an experienced developer. I've done a few small freelance projects, mainly for other university students, but nothing particularly complex.

My problem is that I understand a lot of concepts, but I struggle to implement them from scratch. For example:

I know what Nginx is, but I've never actually configured or used it.

I understand what useRef does, but I'd probably struggle to implement it correctly in a real project without looking things up.

I understand idempotency, but I've never implemented an idempotency key in a POST request.

I know Redis and its basic data structures (strings, sets, lists), as well as caching and Pub/Sub, but I've never used them in a real project.

I understand some software design principles, but sometimes I still struggle to write relatively simple code without getting stuck.

It feels a lot like tutorial hell: I know what things are, I understand the concepts, and I can follow along with examples, but when it comes to building something independently, I struggle.

I really need a job, and I don't know how to approach this situation.

Should I spend the next few months strengthening my fundamentals and repeatedly building things from scratch without AI, or should I start learning AI-assisted development and agentic coding workflows to align with what the market expects?

How would you approach this if you were in my position and trying to land an entry-level software engineering job in today's market?

I'd genuinely appreciate advice from developers who have been through this stage or who are involved in hiring junior developers.

Hi everyone, just wanted to have an opinion about the situation I’m in and express my struggle. I’m a software engineer with 10+ years of experience. Never had an issue with confidence in my career, even managed to change my stack and move away from web development.

Lately, for the last yea, it feels like I’m stuck in self development. I have 2 full-time jobs, both are forcing AI and agentic development, and after a year of working like this, it’s like I’m falling behind. After failing my eval assessment for the next career title, all this bothers me even more. I‘m starting to forget how to code and solve issues.

I’ve been trying for a month now to get back on track, but I can’t. I fail, get angry, and this basically stops me. All this situation with the AI future makes things worse. What do you think about this kind of situation? Do you have struggles like this? I’m sharing my story to hear some thoughts from others in the industry.

Hello,

I've been grinding fairly consistently to improve over at least the last year. That effort feels like it has made a tangible difference, but I realize I am still quite green.

I know a lot of It is just time in the seat. I've been doing this professionally at a medium sized startup for 3 years now. I started to learn how to code on my own 5 years ago and mostly the basics of web development. No degree. No professional courses or boot camps. Just figuring stuff out with either YouTube, MFCC, and Udemy.

It worked well and got me off the ground. I've been writing full stack applications for 3 years and I can comfortably say that I have progressed significantly. My boss would agree. But I know I want to do more and be better.

A senior on my team who was a great mentor and an absolute workhorse who basically represented our team as the lead (unofficially) left abruptly. Him leaving left a big hole. Myself and another colleague who has similar work experience as me (but with a traditional CS degree) really picked up and filled that hole. We're both considered mid level now, which is great. The senior leaving caused me and him to really step up and level up.

But I want more. I don't necessarily care about titles or money. I just want to be an expert. I want to be an absolute expert in my field. A top notch engineer. Whether it's an ego thing or whatever, I don't know, I just want to push myself to be excellent.

I love this field. I really floundered for a few years before this not knowing what I would do with myself. I am so invested in this career path. For the first time in my life, for the last 3 years I have found myself enthusiastically studying this stuff outside of work just for pure interest. I have never had that before at any job.

I want to really level up. Not to become a senior or whatever. Just to be the best version of myself.

I realize I am lacking quite a bit of foundational stuff because I didn't go the traditional route.

Right now, I am reading two books: A Common Sense Guide to Data structures and Algorithms by Jay Wengrow, and Computer Systems: A Programmer's Perspective by Randall Bryant and David O'Hallaron. I am reading these to hopefully fill some gaps in knowledge that I know I'm lacking and have been conveniently hiding.

I did try to start DDIA, but had to put it down because I found myself lacking some of the foundational stuff hence the two above books.

I've read a few language agnostic books about broader concepts. Reading Clean Architecture and understanding the arguments made there also helped a ton.

On top of this, I work on long lived personal projects outside of work. Building applications of various kinds for either learning or fun or some pie in the sky personal business ventures. I am partially entertaining the idea of trying to step into some open source spaces.

For clarity, I work in application code at work. Microservices with all the standard technologies that often accompany working in a distributed manner. Backend and frontend, with a little bit of infrastructure code every now and then.

It's not so much a specific technology or a certain domain I want to focus on. I just want to be a better general programmer. The best I can be. Hence the books.

What did you find levelled you up the most in your career? I'm not necessarily asking what helped you go from junior to mid level or mid to senior etc.

I'm more asking, was there one thing (or many) that really helped you become a better engineer? A certain project or type of project? A certain book and way of thinking? What made you realize you had come a long way and felt like you were a worthy engineer?

I'm not really deterred or anything by AI. To me it's just a tool, and if anything I feel more invigorated to become better at my job than ever before.

Hey everyone,

I have a BS in Software Engineering and work as an AI Test Analyst, but I'm having a massive career crisis.

I don't just want a normal tech job or to test other people's models. My burning ambition is to build groundbreaking, world-changing technology—the kind of massive leap forward where your name is actually in the history books (think the ambition of the Manhattan Project or the Wright brothers, but for modern tech). I want to push my brain to the absolute limit and build things that fundamentally alter human civilisation.

How do I go about doing this?????

We're expected to implement and test features in increasingly short timeframes, especially with AI speeding up development.

But one engineer on my team consistently leaves 20+ comments on PRs even sometimes 70+, mostly about naming. Why is this variable named this? Why this class name? Why this file name? Why this enum or string? Why we don't have comment here?

For example, even when the documentation explicitly uses names like LeadingEdgeExt or RpuClass, he'll comment asking "What is Ext?" or "What is Rpu?"

I'm all for code quality, catching bugs, and improving maintainability, but at what point does a code review become excessive nitpicking?

Is this normal, or should code reviews focus more on actual problems, especially with today's development pace?

I have been a software engineer for a long time and am now the CTO of a startup. When I open reddit, I see endless discourse about AI usage in tech, and how the role of the software engineer will evolve.

The discourse can be frustrating to parse because two people that both say “all of my code is written by AI” can mean entirely different things.

I put forward two broad categories:

“Out of the loop” AI usage, where features and changes are entirely or primarily conceptualised, planned, designed, and implemented by AI without the involvement of the developer. This group may contain some experienced devs, but is disproportionately composed of inexperienced or junior developers who rely on AI in this way because they have no choice - they couldn’t conceptualise changes themselves if they wanted to and can’t weigh the pros and cons of features and architecture produced by AI.

“In the loop”. A flow will look more like: developer plans a feature conceptually -> discusses planned implementation with agent to rapidly search the code base for seams and edge cases -> reviews agents proposals for how to “fill in the blanks” of the design -> skim over the implementation plan -> agent types it all out -> review at your discretion.

Both technically have 100% (or close to it) of their code written by AI, and both produce similar volumes of changes, but in-the-loop uses AI more like robotic hands that type very fast where out-of-the-loop is unable to progress in their development beyond the capabilities of the latest model, and is essentially grooming themselves to be replaced as soon as possible.

I have no interest whatsoever in hiring or working with out-of-the-loop developers, I simply cannot see what value they bring to the table. In-the-loop developers, however, are producing interesting stuff at a faster pace than ever.

When you read through online tech discourse, please consider what category the person you’re reading the thoughts of falls into. Both sides are swamped with cope at the moment, only time will tell how AI is used in tech long term.

The main takeaway I want you to have is what I said at the start, two people can both argue “all my code is AI generated” while being on polar opposite ends of the quality spectrum.

Thank you for reading have a nice day :)

Hello. I see a lot of people, who are on Reddit that claim they don't write code anymore and do 100% of the work using agents. On the other hand, everyone I know in person that is working in the field, doesn't trust AI as much, and uses it as a buddy, uses it for boilerplate code or lets it write methods at most.

How come opinions are so vastly different?

EDIT: I've noticed that comments that say stuff like "if you still coding, you are doing something wrong" are all 1 year old, have their comments and posts set to private... 😮‍💨

Getting laid off can be stressful. If you are going through this right now, take a moment and read this with an open mind.

Ask yourself one question: If there were X employees in the company, why did the company lay off only a certain number of people instead of everyone? Why were you among those selected?

There could be different reasons, but let's look at two possibilities.

Option A: It was the company's fault - poor decisions, bad management, financial problems, restructuring, or other business reasons.

Option B: There might be something you need to improve in your skills, performance, or professional value.

Of course, both can be true at the same time but if you believe Option B might apply to you, ask yourself these questions before moving on to your next opportunity.

1. Are you genuinely strong in the technology you work with?

Do you fundamentally understand the technology, or do you mostly search Google for solutions and piece things together just to complete assigned tasks?

Using Google, documentation, or AI is completely fine but do you understand why a solution works? Can you debug it, explain it, and solve a similar problem independently?

2. Are you building skills that will remain valuable as AI evolves?

Are you simply writing code, or can you also analyze problems, make technical decisions, debug complex issues, and design reliable solutions?

AI is changing the way software is developed. Ask yourself whether you are adapting to that change or continuing to work in the same way without upgrading your skills.

3. Do you understand the broader technology stack?

For example, if you are a frontend developer, do you also understand backend fundamentals, APIs, databases, cloud infrastructure, deployment, and AI integration?

You don't need to master every technology but relying entirely on one narrow skill without understanding how the larger system works might limit your opportunities.

4. What value do you bring beyond your assigned tasks?

If you complete the tasks assigned to you, that's good. But what else do you bring to the table?

Can you identify problems before they happen? Improve performance? Simplify complex systems? Reduce costs? Help teammates? Take ownership of a feature from beginning to end?

5. What kind of company are you prepared to work for?

Are you ready for the expectations of the companies you're applying to?

Evaluate your current skills, experience, problem-solving ability, and technical depth against the roles you want. Are you targeting companies that match your current capabilities, or are there gaps you need to address first?

Be realistic about where you stand, identify those gaps, and work on them.

My point is simple.

I am not saying every layoff is an employee's fault. Companies make bad decisions, businesses struggle, and good developers get laid off too.

But if you only blame the company and never evaluate yourself, you might miss an opportunity to improve.

Sometimes, the most uncomfortable questions are the ones that help us grow the most. Take the feedback, evaluate yourself honestly, improve where necessary, and move forward.

What do you think? Is a layoff sometimes an opportunity for professional self-evaluation?

I’m a software engineer with around 7 years of experience. I’ve had 20+ interviews recently and made it to several final rounds, but I haven’t been able to convert them into offers.

For those who’ve successfully switched jobs in the last couple of years, I’d love to hear what worked for you.

• How did you prepare, and what would you prioritize: coding, system design, or behavioral interviews?
• In your experience, what tends to make the difference in final rounds?
• How did you manage nerves and the pressure of really wanting a job?
• Did mock interviews or any specific resources make a noticeable difference?

I’m trying to figure out what I need to change rather than just keep repeating the same preparation routine. Any honest advice would be appreciated.

There will be a little paraphrasing here and there, but I had my weekly one-on-one with my direct supervisor/manager at the company I have been working at for nearly 10 years. I have been in the sector as a developer, coder, and software engineer for 18 years. Recently, the company I work for was acquired by a company who is very keen to make AI do all of the work and turn a profit.

I have been a bit of a slow adopter during the AI boom preferring to use AI as a tool rather than a crutch, but if you want to take the fun part of the job out with the coding and problem solving, I guess I'll do what you want. Some of the work has taken me twice as long as I try to train the agents for a system that has none, nor skills, nor structure in a 15+ year plus brownfield project we have been bringing out of the dark ages for quite some time (also I have been exempted from every AI training course that has come through as they have given it to one of my team members). However, I still like to know what the AI is doing when it writes the code - it is going to have my name on it, after all, when it goes through the pull request and I have always felt that putting your name on something shows you understand what you're putting your name on. Plus, I feel there is something to be learned with some of the code it generates - new tricks or functionality you didn't know before.

Last week, I was asked to approve the pull request of a peer, but since it is going into main for some security issues that cropped up, I wanted to do my due diligence with a proper code review. I know that he uses AI prompting for all of his code as of late since his team is very behind, but this wasn't a lot of changes (only around +85/-15), so I chose to do it by hand with him in a call with me while I did it (mostly because he can't just send a message - he always has to call for some reason).

There were five things that the AI did that I did not fully understand and of those two I had to go do my own research for. Of those five, my peer did not know why any of them were done that way. He just told the AI to do, and so it did. I was a bit agitated, but I carried on and logged it away for the titular touchbase with my manager, since he is my peer's direct superior as well. Overall, the AI did a bit too much in its fixes, but it wasn't enough stop approving the pull request.

This takes us to today where I did bring it up to my manager. I stated that I was not upset with my peer using AI to do the work, it probably saved him a lot of time; what I was upset about was (among the five, I picked) one example: OPTION(RECOMPILE) was added to a SQL stored procedure. Now, adding it didn't seemingly do anything worth noting on a stored procedure that was only a few lines long. I ran my own diagnostics later to see if I was missing something and the both the execution time and the read count were actually worse with its addition, but not by much or worth calling back the PR.

What upset me was that:

1. My peer was surprised that the line was there at all.
2. My peer didn't know why the AI put the option into the stored procedure.
3. My peer didn't know what OPTION(RECOMPILE) did.

For each of those three points, I had heard him typing away into the AI for the answer which... that's fine, but why did it get all the way to the pull request approval stage and my asking the question? The AI said to do, so it did.

My manager (who knows my hang-ups about AI efficiency versus AI doing everything) said that that's why we do the code review stage of the pull request. I said, "Sure, but why did it take to get to this point before he even saw this line get added?"

My manager stated to me that clean and readable code doesn't matter with the AI - what matters is getting a serviceable product to the end user. If the AI is delivering performant and working code, does it really matter how it was made?

I said that I believe that it does. How are we supposed to be able to properly manage and maintain the code if its not readable? He stated that we'll be using the AI to manage and maintain the code, so we need to be better with it to turn results into the consumable product - that we're still in the early phases and we have to treat it like a junior developer and teach it how to do the work better.

I asked, "Then what's the point of peer code reviews?".

"What do you mean?"

"If we're suppose to put the faith in the AI and not have it clean or readable to a human, what is the point of having peer code reviews?"

My manager then paused for longer than I would have liked and replied, "I guess there isn't one. But if we think of it like, I guess, the old paradigm that you have where it all needs to be reviewed line by line to understand how it works, then that would make the testing work that the QA team is doing with automated test irrelevant since all of their tests are surface level tests because that is the user experience. I don't need to know what's in the black box to provide a valuable product - just that it works and passes testing. It doesn't really matter how it gets from start to finish."

I countered again with his stating at the start of the conversation that this is all still too new for us to have that faith. We have to know what it's doing. He stated early on in that it's like a junior developer or an intern. Would you put your faith implicitly in a junior developer or intern on the deadlines that we have or the budgets we're trying to get ahead of? He countered on me that the client's reception of a button click is just a button click, as long as it does what it's supposed to, it doesn't really matter.

I just got to a point where I didn't want to have the conversation anymore - clearly his mind is set and my arguments to the contrary is "old paradigm".

This is not an indictment against AI or the people that refuse, I just needed to vent.

Which one is the best from your perspective? For me, those options are somehow resistant in this AI context.

Asked a developer working under me to run the website he was supposed to demo. Bro opened the IDE agent and typed, “Run this app.” 💀

He is not even an intern. He is SDE 1😭.

EDIT : I am not angry with him or find this stupid. It just feels funny.

I’m a backend developer at a service company working on mainframe systems. I’m actively transitioning to Python backend and targeting early stage startups and product based companies.

I’ve built multiple projects using FastAPI, PostgreSQL, Redis, Celery, Docker, AWS. No professional Python experience yet, only personal projects.

Two specific questions for anyone who’s made a similar transition:

1. What actually moved the needle — was it projects, DSA, networking, or something else entirely?
2. Are FastAPI projects enough to get noticed at product companies, or am I missing something obvious that insiders know?

Honest feedback appreciated. Not looking for generic advice — looking for what actually worked for you.

Pretty much the title, was recently laid off and took some time off as i gave this company 10 years and they just threw me to the side…..an email in the early morning and all access revoked immediately(don’t have words to describe how it felt). Total exp 10 years with same company and now that i am starting my prep again I don’t know what to prepare the coding interviews were diff before but now I don’t have any idea….. can anyone help on what to expect?

But deadlines don't give me a chance to do so! So I find myself having to use AI to finish quickly.
What should I do then? It's a paradox.

I joined a company and was asked by a senior engineer to develop a data framework.
Speed was a key requirement. He claimed Python couldn’t scale because threads don’t work for parallel downloads.
Except Python threads handle I/O concurrency perfectly well. Network bandwidth, remote server limits, and disk throughput were the main bottlenecks.
Anyway, I developed the framework.
Over time, my colleague increasingly positioned himself as the architect. He attended meetings, repeated what the team leader said, and acted as though he was directing the project.
Meanwhile, I was implementing the functionality. Because he’d initiated the project, he received much of the architectural credit.

But the funniest part was happening on another project.
While I was building the framework, he was leading an ambitious initiative: developing an in-house analytical database that would supposedly outperform established cloud data warehouses.
You can already smell the wheel being reinvented.
After two years, his team presented an in-memory encoding technique to compress repeated values while maintaining fast access.
Except columnar compression, dictionary encoding, run-length encoding, and vectorized processing are well-established technologies. There was no convincing comparison with existing open-source solutions or meaningful production benchmark.
And here’s the masterpiece.

The project was abandoned because keeping the entire dataset in RAM was too expensive.
Who could possibly have anticipated that storing petabytes of data in RAM would cost dramatically more than object storage?
The difficult part isn’t making queries fast by putting everything in RAM. It’s achieving good performance without doing that.
You need intelligent caching based on actual production workloads, efficient disk access, compression, query optimization, and benchmarking.
Which data should be cached? What should be evicted? How do you optimize performance per dollar?
That’s the actual engineering challenge.
If your entire strategy is “RAM is faster than disk,” congratulations, you’ve discovered computer architecture.

If a newly graduated intern told me after two weeks that storing everything in memory would be prohibitively expensive, I’d say: “Good observation. Now let’s investigate caching and disk-based architectures.”
But we’re talking about an experienced engineer, formerly in a leadership position, with years of low-level programming experience and compensation well into six figures.
After two years, there was no production system, no demonstrated competitive performance, and no convincing technical justification for the architecture.
And he got promoted.

Meanwhile, the framework I developed became an important part of our organization’s infrastructure.
My manager recognizes my contribution and has given me more responsibility, but promotions are constrained by internal politics.
I didn’t fight over the promotion. Making powerful enemies at work is a terrible investment.
But the gap between technical contribution, perceived authority, and compensation is fascinating.
An engineer who spends two weeks identifying why a project won’t work might save a company millions.
An architect who spends two years discovering the same thing might get promoted for leading an ambitious research initiative.

Sometimes the most scalable thing in software engineering isn’t multithreading, caching, or distributed computing.
It’s taking credit for someone else’s work.

I’m not sure if this is the right subreddit to post this, but I wanted to get this off my chest and see if anyone else feels the same way.

I’m a front-end developer, and I got my first job a little over a year ago. The thing is, I don’t necessarily love what I do. I don’t hate it either. I appreciate my job mainly because of the financial stability it gives me and the things it allows me to do outside of work.

But I’ve noticed that almost everyone on my team seems genuinely passionate about tech. They keep up with the latest technologies, enjoy talking about programming during their free time, and some even code on weekends or work on personal projects just for fun, and honestly, I can’t relate to that at all.

When I finish work, the last thing I want to do is code or read about tech. I have other interests, and I just want to enjoy my life outside of work

Sometimes this makes me question whether I’m even in the right field. I often feel dumb or behind compared to my coworkers, especially when they’re discussing technical topics I’m not familiar with

And something I’ve realized about myself is that when I try to learn something new, it’s rarely out of genuine curiosity or excitement. Most of the time, it’s because I don’t want to feel stupid or be the only person in the room who doesn’t understand what everyone else is talking about

I know there’s always going to be something new to learn in this field, and I’m willing to learn what I need to do my job well. But I struggle with the feeling that I’m supposed to be genuinely interested in all of it, even outside working hours.

I guess what bothers me the most is feeling like I’m surrounded by people who have found their passion, while I’m just doing a job that pays my bills and allows me to enjoy other parts of my life.

I don’t know if this is just imposter syndrome, if I’m still adjusting to being relatively new to the industry, or if software development simply isn’t for me

Does anyone else feel this way? Or if anyone have you managed to build a good career in tech without being particularly passionate about it?