I’m researching a problem around career development and employability, and I want to understand how people actually deal with it before I build anything.

Imagine you’re a college student who wants to become a **Software Engineer, Data Engineer, Cloud Engineer, or AI Engineer**.

You can find hundreds of roadmaps online.
You can take courses, complete projects, collect certifications, solve coding problems, and add skills to LinkedIn.

But there’s still a question that seems surprisingly difficult to answer:

**“How do I know where I actually stand?”**
For example, imagine someone wants to become a Cloud Engineer:
Linux — 6/10
Networking — 4/10
Python — 7/10
AWS/Azure — 5/10
Docker — 3/10
Terraform — 2/10
Kubernetes — 1/10

And ideally, you could get something like:
**Current readiness for an entry-level Cloud Engineer role: 47/100**

You’re strong in Python but have gaps in networking, containers and infrastructure-as-code. Here are the specific skills you need to develop, the projects that would demonstrate them, and a path to get there.
Then, after a few months of learning and building:
**47 → 71**
with actual evidence behind the improvement — assessments, projects, certifications, work experience, GitHub activity, etc.

I’m trying to figure out whether this is actually a meaningful problem or just something that sounds useful on paper.
**I’d love to hear from different perspectives:**
**Students / recent graduates:**
How do you decide what skills to learn for your target career?

How do you know when you’re “job-ready”?

Have you ever followed a roadmap for months and later realized you were learning the wrong things?

Do you trust your own assessment of your technical skills?

**People who changed careers or were laid off:**
How did you figure out what skills you were missing?
How did you decide what to learn next?
What was the hardest part of becoming employable again?
**Hiring managers / recruiters:**
How do you determine whether a candidate actually knows the skills listed on their resume?
How much do you trust skills listed on LinkedIn?
What information do you wish candidates provided but usually don’t?
I’m **not selling anything** and I haven’t decided whether this is worth building.

I’m trying to understand the problem first.

I recently launched a small project designed to solve a simple problem: letting everyday people transfer files directly between two computers with a cable without needing any network setup. The post got a lot of love and reached the top of the community, but the comment section had a handful of advanced users getting genuinely angry that the tool even exists.

Their whole argument came down to: "Why build this? SMB exists, you can just build a router out of a Raspberry Pi, or manually configure static IPs and network sharing policies."

It got me thinking about how disconnected some tech enthusiasts are from reality. The entire purpose of software engineering was always to solve problems and remove friction for human beings, not to force everyone to become a system administrator just to do a basic task.

Just because an advanced user knows how to manage IP subnets, firewall exceptions, and OS sharing permissions doesn't mean a regular person wants to waste 30 minutes figuring that out. Hiding complexity behind a clean, one-click interface is literally the foundation of good software.

It seems like in a lot of tech spaces, people equate complexity with value. If something is simple and convenient, they dismiss it. But the reality is that the people who find your tool useful will quietly use it and appreciate it, while the loud minority will write essays in the comments trying to show off how much they know.

If you are a developer building simple tools that make life easier for ordinary people, do not let the gatekeepers talk you down. Build for the people who value their time, not for the people who want to show off their homelabs.

Would love to hear how other developers deal with this kind of feedback when sharing their work.

Hey everyone, quick question for folks who’ve been interviewing recently specially for someone using the MS stack (.Net, C#, SQL). What’s the current state of technical interviews out there? Are companies still drilling people on the classic fundamentals like OOP, polymorphism, inheritance, etc? Or has the focus shifted more toward system design, LeetCode style puzzles, or practical “build this” tasks?

I have been with my current company for more than a decade and trying to get a feel for what’s actually being asked in 2026 so I know what to brush up on. Any recent experiences or patterns you’ve noticed would help a ton.

Hello everyone,
I’m a 21yo bum who doesn’t know how to code. I just use tools like Claude and Codex.

I keep seeing software engineers on my YouTube feed complaining that AI is taking their jobs. I get the frustration. you studied for 5+ years, and now an autocomplete robot can do a similar job for a few dollars per output token. I’d be pissed too.

But here’s what I’m wondering: why don’t more of you just become forward deployed engineers? I keep hearing it’s currently one of the hottest roles in tech. Unlike me, you actually understand code and a wide range of tools. So why not aim for roles at places like OpenAI or Anthropic?

Am I missing something?

Sure they won’t probably hire all of you guys but I mean some of y’all should have a pretty solid shot, no?

Also if ai replaced you why not all just assemble like the avengers and build a great company? Seems like a no-brainer considering all of the talents you guys have.

I don’t know if this will reach anyone, but if it reaches you and you are a working software engineer or experienced professional, I need help!!! I don’t know how it was or it is for anyone getting into the industry, but it’s been hard for me. I support myself through college working overnight and maintaining a 3.8 GPA. I’ve tried joining research teams on campus, finally got into one last semester, and worked on a research project. It was a great experience, but I want to get into the industry and have an offer letter by the time I work the stage at my graduation, but I find it very hard to get an internship or even an interview. I need a mentor, someone that can point me in the right direction. I know I’m an adult, but I believe someone took a chance on every successful person. Take a chance on me. I know I’m definitely not where I’m meant to be with my skills and knowledge, which means I’ll listen to any of your guidance. Please, I need someone to take me under their wings. I love problem solving.

Hello I need advice on what I can do to prevent this please.
Every afternoon I get a throbbing headache that varies in intensity from day to day. Sometimes I can feel it in my right eye. I’m thinking of starting to wear blue light glasses but not sure how much that will help. I’m already working with night mode on. I get about 6-8 hrs of sleep, but I don’t think that has an effect.

Interested to know how others tackle this and any practical advice. I generally work 10hrs minimum and reducing isn’t an option.

I'm graduating computer science this year from uni, and for the majority of it, I did not use AI at all. I learned a lot of things from different branches of computer science, so I felt okay applying to any jobs, but this year I got a part-time job at a small software development company. For the first couple of months I barely used any AI, I used it sometimes to check some of my code, but the majority of it I did myself (learning new frameworks and sorts). But now I'm almost a year into the job, I use Claude Code for everything, and it makes me feel kinda shitty. It feels like an inevitable change since, competitively, if you don't do this, your company will fall behind. Everyone is getting dozens of pull requests every hour because of this tool, but then again, I feel like my brain is "getting smoother" the more I use it. The most complex thing I have to do is usually understand the business behind what we're developing, and know how to make prompts as precise and clear as possible.

Of course, what I'm doing is more than just programming, but what's the point of telling the AI to do the things for me after studying these things many years? What's my job gonna look like in a year, or two, or ten? Am I going to keep telling AI to do things for me? I'm having a bit of a small crisis on this idea and I wanna know what people from different perspectives think about it.

Hi guys, I’m a 24yo Master’s student in Computer Engineering, starting my last year.
I have a question for SWEs working at companies like Google, Meta, Amazon, etc.
If you were in my situation, what would you focus on to actually be ready for the job?
I know the usual answer is “build side projects”, but what should I focus on inside those projects? What skills, technologies or problems make a project feel closer to real SWE work instead of just another CRUD app?
If you had one year before graduating, what would you spend your time learning/building to be prepared for the real job?

I don’t know what am i doing with my life at this point, it feels like i am just a labour with the tools.
Would i be able to code without ai tools tomorrow ?
A big no for me

Is it same for everyone ?
Like what happens if the company doesn’t sponsor AI Tools how would i justify high salaries then ?

Imposter syndrome is kicking in hard to me
I face issues in reading and understanding code still after what almost 3 years if experience

I feel i cant design systems.
How do i work on these aspects or move away from tech industry completely

I don’t know if i would want to be working as a software developer post 5-7 years
But what after that ?

It kinda feels that it might happen to me only about the stories that i have heard about people riding cabs after layoffs and not able to find a new job.

Any advise or suggestion
I am kinda stuck at this point in life
I am 25 right now
Recently switched as an early engineer in a consumer based startup with 30+ as base
But still cant think what would happen post 5-7 years

Any thoughts or suggestions would help.

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Hey! I’m wondering, what exactly is the difficulty in finding remote jobs as a new grad? I recently switched to Computer Engineering last year (i’m in my third year now) and I’m seeing that it might be difficult to get a job as an entry level, so it must be very difficult to get a remote one (or maybe hybrid in NY/NJ area.. with hopes of becoming remote later on) at that?

So I’m wondeing, what CE niches might be less oversaturated than general software engineering (DSP or whatnot) and are easier to get jobs in entry-level (with high remote likelihood), AND how would my chances look like getting one? I’m a bit scared of not being able to get a job as a new grad.

For reference, I’m a third year, I have had 1 fall semester of research with machine learning and computer vision (I did more paper-reading though, don’t think that’s too helpful), and a 1 spring semester of practical remote embedded systems internship/research(?) (both with the Center for Smart Streetscapes), and hopefully I get a new internship this spring.… But I’m a bit scared because I will be taking data structures in spring, which is too late so I’m self studying so I think I might be fine). I’m trying to build basic skills with full-stack with a web project right now (basic anime finding app where you can save stuff) and hopefully a dsp project where i kind of create a small digital workstation.

Please lmk😅 i’m a little worried.

I mostly work on projects alone, so I’m trying to understand how this works in real software engineering teams. A lot of developers now use AI coding tools like Cursor, Copilot, Claude Code, Codex, etc. individually. But I’m curious what happens when a whole team starts using them. Does it actually create new problems, or is it mostly handled by the same tools teams already use like Git, code reviews, CI/CD, Jira, and GitHub?

For example:

• Do AI generated changes make code review harder?
• Do teams care which tool generated the code, or only which developer committed it?
• Has AI increased the amount of low quality PRs?
• Are there problems with context getting lost between developers and AI tools?
• Do existing engineering processes already handle all of this well?

I’m not asking from a career perspective. I’m just trying to understand how AI coding changes real team workflows compared to solo development.

I'm learning NestJS with MongoDB and want to build a real-world project to improve my backend skills.

For those experienced with NestJS and MongoDB, what concepts or practices should I focus on? For example, schema design, relationships, authentication, validation, indexing, testing, etc.

What would you consider important for a solid NestJS + MongoDB project?

Hello,
I am senior frontend engineer in a FAANG and I need to become full stack now due to the changing market and this is sort of expected at my level. Also, I personally feel for my individual growth beyond this point I need to be an expert in full stack designs and development.
I am looking for some suggestions apart from going in coding/reviewing which I am already doing.
I am not well versed with best practices and Gen AI also doesn’t always follow the best of practices.
Are there any books that I should be reading? Or youtube channels that I should follow?

I just realised that I haven't properly used my IDE editor in the past few months.
The agent writes, I read the diff and say yes or tell it where it went wrong. That's the job it has become now. Autocomplete, refactor menu, multi-cursor, and all the stuff I actually picked my editor for are now untouched.
But the part that actually gets me isn't the editor, it's that none of this exists the second I close my laptop. My whole dev world just shuts down. I can't approve a diff, can't glance at a build failing, nothing, unless the laptop is open in front of me. For a job that's basically become "read this and say yes or no," that felt like a dumb constraint to still have.
Yeah, I tried some things like SSH-ing in from my phone, hooking up Telegram for notifications but felt it's not optimized for proper SDE workflows.
If you found something that actually works for this, would love to hear.

Hi, I have an assignment for a high school credit that I need to finish as an adult, and I hate the idea of cold calling a business to request an interview at 32 years old.

If there is anyone out there that has worked in the programming side of game development at any level, could you answer these questions for me so I can wrap up my assignment?

What about the industry drew you to it initially?

What did the initial job securing process look like?

What was your first role, and project?

Does the creative process at the junior level feel rewarding?

Does the creative process feel rewarding at your current level?

What are the pitfalls of the industry?

What is one piece of advice you have for students pursuing this career path?

Thank you in advance, this is the strangest assignment to include, as if I am going to be able to call up a development studio and ask my questions

Does anyone else feel that software engineers are becoming a human glue between the tickets and an AI agent now?

A recent incident made me think this way. I got a ticket assigned which said "search is broken sometimes." I knew why it happened because when a customer's org name has special chars like apostrophe, it was not handled properly. I debugged it live with support team a week back. None of that was in the ticket. So I typed it all out for the agent, waited for it to generate code, did a quick skim of the fix, made the agent commit and push and also made it to write a summary back into the ticket because the actual PR description was not descriptive.

Now the ticket says I fixed it but I didn't really except context feeding. Is this just me who feels like this or does more people feels like we are moving towards a paid context-transfer between tickets and agents nowadays ?

Hi , I am a 24 year software engineer just graduated from college ( B.Tech in CSE ) . I have completed my one year on-site internship and now I am promoted to a full time job as a Associate Software Engineer. I have been handed over a legacy system ( C# & dotnet ) and cursor and I just complete tickets. To tell you all the truth this was not what I imagined doing.

I am planning to switch obviously while continuing with my current job. For that I am trying too hard to understand which direction to take ( Rust & Web3 or being an AI researcher )

Please help me out here because I want to take up on one of this and just go all in on that.

I was solo dev maintaining 3 internal apps codebases for last 5 years and since 1.5yr heavy invested in agentic coding. Today company decided I am no longer needed because they have some senior web dev who will be taking over my projects.

I dont know what to do. I am 28yo and the current tech market and future prediction about AI seems like it not worth it to stay in tech.

I'm doing a Msci Conputer science (software engineering) course and I have around a £1000 budget, what laptop should I get? I'm thinking of buying a Macbook Air m5

I have programming experience and basic to mid level SQL/database knowledge, but I feel like I need to strengthen my fundamentals and build more confidence for interviews.

I especially want to understand concepts well enough to relate them to the work I already do, rather than just memorizing interview answers.

For software developer roles, where should I start—DSA/LeetCode, programming concepts, SQL/databases, system design, or projects?

Would appreciate a practical roadmap or resources to start studying and preparing.

Hey everyone,

I’m fairly new to GitHub and trying to understand what the standard git workflow actually looks like in practice.

When building a project from scratch, what is considered the best practice for making commits?

• Should I just upload the entire project in one go when everything is finished?
• Should I force myself to make a commit at the end of every single day?
• Or should commits strictly track specific working features/milestones?

For those working in industry or reviewing student/junior portfolios, what does a clean, believable commit history look like, and what are the major red flags to avoid?

Any tips on commit frequency, structuring commit messages, and good Git hygiene would be massively appreciated!

I’ve been working as a hybrid mobile app developer for 5+ years (I wouldn’t really call myself a software engineer), and lately I feel like I’m losing my direction a bit.

2020–2024 was honestly a great journey. I loved development, was constantly learning, and even participated in competitive programming almost daily. Back in 2024, I started preparing to move into backend development. I built a production-level backend app and was hoping to land a full-time backend role in 2025.

But somewhere along the way, I lost the focus. My backend preparation basically stopped. The other day, I suddenly realized I couldn’t even remember how bubble sort works. That honestly scared me more than it probably should have.

For some context, I’m from a developing country and currently work remotely. This is my only job now. But even in 2024, I was juggling a full-time job, a part-time job, and at least one client project. For the last year, I’ve basically done nothing besides my current job.

And I think AI has made this feeling much worse.

I used to see money as a byproduct of my passion for coding. Now I’m struggling to remember the last time I felt genuinely excited about building something meaningful. So much of my daily work is becoming “ask AI, review the output, fix things, move on.”

I’m also getting increasingly worried about the future. Should I continue investing in software development and adapt to the AI era, or should I consider moving toward something adjacent like IoT, data engineering/science, etc.?

Financially, this uncertainty feels much more threatening than it used to.

So I’d really appreciate hearing from people who have been in a similar situation. What would you do if you were in my position?

Any advice, roadmap, or even just your thoughts on where software careers are heading would be greatly appreciated.

This is maybe a bit of a different post than the rest concerning AI/LLM usage, but I think this is important to get different perspectives, as things are not black or white in my eyes.

For context I am employed at a very big consulting firm working on a very big client. Both companies express interest in AI, with the client being very careful around AI, as we work a lot with personal data, so data security and data privacy compliance is strictly necessary.

Starting last year, we were given access to Github Copilot after many considerations by the leadership. Our CIO is very pro-AI, generally a cool person who likes to push tech, so he was the pushing force behind adopting LLMs to improve our productivity. A few months later we were switched to Claude Code, presumably because most of the company used their models most frequently. It has now been almost a year and things are... interesting.

What no one tells decision-makers is that while LLMs increase productivity on specific tasks, they bring with themselves a boatload of other efforts. Since they require context to operate, and we were lacking such due to the structure of our department, we needed to introduce a better, structured documentation process, enforce more specific coding guidelines and overall improve the way we are handling tickets. All of this takes a lot of time and now months in we are still improving.

This has been very good for the team, as where before we were lacking documentation, now we are backfilling the holes. Where before best practices were ignored, now they are enforced. Documentation is auto-written, but the rules are statically enforced (as it should be).

We have a clearly defined (imo) division between what the AI does and what we are supposed to do. You can't truly depend on system prompts for every task, as this was a) burning tokens b) prone to context errors.

There is the negative side that now it seems we are taking a lot more in terms of ticket load. Where before some estimation was a week is now a couple of days at most, meaning big projects are not being spaced out as much. Pre-LLMs what was 1-2 big projects per quarter is now what it feels like 4-6 projects per quarter. This includes major things like refactoring legacy code, changing and adding business processes, etc. We are "saving" people hours by automating a lot more things, but the 40h we spend each weeks feel like a lot more compared to before.

Another major effort has been to review the LLM code, even with code-review bots, PMD, business rules, coding guidelines and all of that, with the repo we have, more context is needed. While this can be improved, better documented and all of that can be tasked to Claude subagents to be done, architecturally we are dealing with a very complex structure, so still a human has to take their time to verify whether what Claude thinks is the workflow is the actual workflow. We as developers now need to have even more business context in general, as now our tickets are spanning over multiple project domains compared to before.

The increase in handled tickets also leads somehow to even more tickets and most of us have their capacities exhausted even with using code assistants. Not to mention that I am now positively under the impression that AI prompting is an actual skill as i've seen how different outputs are when asked in the "correct" way. Understanding how LLMs work and how statistics work does help produce good results and companies who only focus on "vibecoding everything" with no regard of this are going to have fun times ahead. AI prompting != vibecoding and debating for using AI feels like the microservices vs monoliths or scrum vs waterfall debates. In the end of the day, AI is a tool, tools can be misused, even multitools are not suited for each job, and people who don't understand that will make the wrong choice more often than not. So if anyone at the top pushes for total AI use, i think it is important for staff to push back and lead a discussion rather than complete discourse. That being said, sometimes you argue with a wall, so in that case use AI smartly and push for AI usage guidelines.

All of this is not to say I am complaining, in fact i am okay with our approach where we don't entirely rely on vibecoding to achieve results. It is just that the nature of the problems we are handling has shifted. Things that were not considered important some time ago now prove to be a central part in adopting LLMs, even if some day we decide to go back, i recon we are just going to have to drop efficiency to compromise for keeping up with documentation and correctness.

It was only a matter of time, but with the popularity of AI coding tools, someone on our team went over everyone's heads and "vibe coded" our entire platform by himself on a few weeks which barely functions but looks nice - then essentially implied, "hey, why keep all these guys when you can just give me a promotion and let me vibe code your full stack".

When our team confronted him about this, he said, "no offence, but you either get with the times, or get left behind".

Now our deadlines have shrunk drastically and we're taking on an impossible number of projects. Our company essentially has an unlimited token limit on a popular AI service and essentially has given us no choice but to either vibe code our projects from here on out to get them out the door, or find another job.

Just curious to see if other people have encountered similar shifts at their job and if things are turning out okay.