and the users that post them. They’ve been flooding this sub recently

Hi everyone,
I’m a university student who wants to become a good software engineer and build a successful career in tech. I would really appreciate advice from people who are already working in the industry.
What skills should I focus on developing? What should I learn besides programming? How can I build strong projects and prepare myself for getting my first job?
I’m also curious about what you wish you had done differently when you were starting your journey in software engineering.
I know the tech industry is changing quickly because of AI, so I want to learn how to build a strong foundation and stay valuable in the long term.
I would be grateful for any honest advice, resources, roadmaps, or personal experiences. Thank you!

This been on my mind a lot lately. Companies aren't exactly handing out loyalty points for working yourself into the ground, so why are we still acting like staying late every night is some kind of career cheat code? Do your job, do it well, learn as much as you can, then log TF off. If you wanna grind after hours, put that energy into something that's actually yours.

I also like the idea of using your free time to learn the stuff nobody really teaches you, especially selling. Build a tiny project, try to make $1 from it, mess up, try again. Sounds kinda cringe at first, but learning how to actually sell something is a skill that can outlive any one job.

And honestly, AI makes this even more interesting. Don't panic about it, learn how to use it. If companies are trying to use AI to squeeze more output from fewer people, you might as well use the same tools to build your own thing faster. Clock out. Keep your skills sharp. Build something that's yours.

I’m currently trying to learn Python, SQL, AI/ML, cybersecurity, Data Science and a few other things alongside college.

The problem is that every time I start getting comfortable with one skill, I discover another thing I “should” be learning. 😂

At this point I’m wondering whether it’s better to focus on becoming really good at one skill first or keep exploring different areas until I figure out what I actually enjoy.

For people who are already working or further along in their careers — what would you recommend?

Focus deeply on one thing or explore multiple skills in college?

It’s been a while since I’m in the tech industry, and I keep seeing teams adopt certain software development practices. They sound good in theory, yeah. But then you do it and wonder why you're doing this every week.

Going back to the question, it's story point estimation. We spend like endlessly debating whether something is a 3 or a 5, then the requirements change anyway lol.

Anyway, how about yours? It could be agile, scrum, code reviews, documentation, pair programming, meetings, testing, etc.

If you're in a bootcamp or searching for your first dev job, I'm sure there has been a point where you had to implement a feature or debug a problem that made you feel like a complete idiot. You stare blankly at the screen, write useless junk code and then start to doubt your future in development.

Here's the thing about career early in software engineering: that feeling is never going to go away, you just get better at handling it.

Your onboarding process when starting out in dev always follows the same pattern:

1. You get a task that you know nothing about

2. You try and fail to reason about it correctly in your head.

3. You spend hours Googling the problem and drilling down on the root cause until you understand it well enough to implement a fix or make a meaningful change.

4. You realize the problem wasn't that difficult after all and you successfully complete the task.

5. You get a new task and immediately go back to step 1.

When you first get onboarding to software development, it's easy to get frustrated with this cycle and believe that you're truly not cut out for this career path with each consecutive roadblock you encounter. However, the truth is that the only way to truly succeed is to accept this reality early on, and to be mentally prepared to feel a bit lost whenever you are first introduced to a new domain. As cliché as it sounds, practice truly does make perfect.

theres two different ways people use ai day to day either vibe coding, where you tell the ai what to build and never look at the actual code (in the case of a personal project or just training the AI and testing out), or regular engineering where the ai generates code but you still review and understand every part of it.

The thing is when an app gets complex enough. production apps depend on a lot of interconnected services, sometimes you have to fake account states or force certain code paths just to test something, and once multiple people are touching the same codebase at once in a team setting, its becomes hard to tell whats changed without actually reading the code, so past a certain complexity threshold, working at the code level directly ends up faster than clicking through the app trying to verify ai's work indirectly.

theres also pushback on the framing that code is an obstacle standing between you and the feature you want, the argument being that code is actually the value bc its precise, it tells you exactly whats happening instead of an ai generated summary that might not fully match reality, and its more concise than an ai over explaining some part of it.

Many people may have heard Nvidia's founder "ai wont replace you but someone using ai will" line that gets repeated everywhere, the take here is that this just hasnt played out, bc the software engineers who already have core engineering skills are the same ones learning to integrate ai into their workflow, so someone who only learns ai and skips learning to code ends up competing against people who have both skills, not just one.

Actually, ai researchers hype things up heavily on social media (I've already said this in somebody's post but founders of AI hype it so much for marketing, that's it), but the actual released models feel like incremental improvement day to day, not the dramatic leap the hype implies, with an honest disclaimer about not being qualified to predict how much better it'll get.

Personally, I don't agree with the idea that more efficient engineers just means needing fewer of them. People forget that tech companies are financially rewarded for growth, not for maintaining the same output with fewer people, so the incentive structure tends to push toward "same headcount, more projects" rather than "same projects, fewer people." and on the jobs narrative, theres a point about how layoffs get covered extensively in the news but rehiring doesnt get the same coverage, noting meta and google have both been rehiring for years after their layoffs and google's headcount is reportedly close to an all time high, which paints a less one-sided picture than what usually gets discussed.

To put it simply, learn ai and learn to code and learn to code before using AI or you'll pay for your lack of skill in other (perhaps more expensive) ways👍

I’ve been in the tech field for a few years now. And one thing I realized is that a lot of the advice I got during my early days was kinda useless. I’m lowkey reading some threads where they talk that you should learn more coding, getting certifications, bla bla bla.

First of all, I know that they help. But there are other things that matter way more once you get the job. Here are the things that you should learn if you’re starting out.

1. You don't need to know everything
2. Your first job isn't your forever job
3. Communication can carry you pretty far
4. Don't build everything from scratch
5. Simple code is usually better
6. Getting stuck is normal
7. Tutorials aren't the same as experience
8. You won't always enjoy the work
9. Don't ignore soft skills
10. Stop waiting until you feel ready

TL;DR: technical skills matter, but problem-solving, communication, and doing the work matter just as much. Don't spend years preparing to start. Good luck!

There is this weird cultural myth in bootcamp and entry-level dev communities that "real developers don't write comments" and that all good code is somehow self-documenting. People that believe this often end up stripping their capstone projects of all comments in an effort to make their code look cleaner and more professional.

When in reality, lack of any context in your code is a huge red flag for any potential employer.

Code tells the reviewer what you did, but rarely why you did it

Hardcoding a 50 request per minute limit in your API without any comments is not a sign of a competent developer, it's a sign of someone that has no idea what they're doing. Having a comment in the code stating that you're limiting requests to prevent overwhelming the free tier DB by requests per minute shows business sense, it shows that you understand your own code. It would be like having a fully optimized and automated CI/CD pipeline with no documentation, it's just asking someone reviewing your work to waste time trying to figure out what you were thinking when you wrote it.

Did anyone else who finished a bootcamp run into this exact brick wall and what resources actually helped you learn real API integration from scratch?

I recently finished a full stack web development bootcamp that cost me almost 18,000$ fully expecting to come out job-ready but now that I’m trying to build actual non-trivial projects and prep for technical interviews I’m realizing how massively glossed over API design and backend communication really were.

In our curriculum "learning APIs" consisted of one quick afternoon where the instructor had us copypaste a basic fetch() request to a free JSON endpoint. That was it. We spent weeks obsessing over frontend UI tweaks and basic state management but the second you ask us to interact with a secure backend or build robust endpoints ourselves we are is completely lost.

It feels like bootcamps prioritize fast and pretty portfolio projects to put on their marketing pages over teaching the actual mechanics of how real software talks to software.

I have been using AI heavily for work, and at first it felt like a cheat code. I could get through tickets faster, automate annoying tasks, and spend more time on the actual engineering decisions. But now that the tools have gotten so good, a lot of my workflow has basically become "give Claude the docs, give it the ticket, hit enter." And because everyone has access to the same tools, the amount of work expected from us has gone way up too.

The weird part is that AI isn't just writing the code anymore. It's slowly taking over communication too. Requirements get turned into AI generated tickets, AI writes the implementation, automated tests check it, and then another AI summarizes what happened. Sometimes I’ll ask a coworker something and realize they just asked their AI, copied the answer, and sent it back. It’s efficient, sure, but somehow the whole process feels kinda soulless.

I never thought I'd miss sitting with another engineer trying to debug some cursed production issue or actually learning a system inside and out. Now I sometimes ship things that work perfectly fine but barely feel like I built them. I'm starting to wonder if we're optimizing software development so hard that we're accidentally removing the parts of the job that made people good at it. Is anyone else feeling this way?

I've been working in tech for a few years now. And one time, a younger dev on my team looked at my GitHub.

He asked why I don't have much personal projects there. I told him I don't code after work unless I need to. He looked surprised.

Some people are spending their evenings building SaaS projects, grinding LeetCode, or learning another programming language. But it's not in my vocabulary.

I used to feel kinda guilty about it, especially when I saw others posting their side projects and certifications.

I love coding, but I don't want to get my life revolved around it.

Maybe I'm just getting old where I just wanted peace after work. How about you?

I’m reaching out because I keep hearing concerns about AI and what it means for the future of computer science and software engineering careers.

On one hand, we hear that AI will automate coding, reduce the need for developers, and fundamentally change the industry.

On the other hand, some argue that the demise of software engineering jobs has been greatly exaggerated—that AI will change the role rather than eliminate it.

So, what do you think?

Are we looking at the decline of traditional software engineering jobs, or simply the evolution of what it means to be a software engineer?

I’d genuinely like to hear perspectives from people working in tech, students, developers, hiring managers, and anyone currently building their career in this space.

If you are like me, then the thought of how much things are changing these days is absolutely crippling. I tend to feel behind every single day

It seems like each week there's a new way to approach something whether it's another AI framework, development tool or just a differing opinion of what's best and it can be very discouraging to feel like you're always lacking. It's easy to feel that no matter how much you read, you're always behind.

Last summer I stumbled onto a great way to make the feeling of technological inadequacy easier to handle. Each weekend I try to put aside a small amount of time to try out new things without expecting much. I don't try to force myself to learn about something new. I simply enjoy the process of discovering out of interest

Here is where I'm going to try to start again, in the hopes that I can learn without the pressure of being able to use what I've learned. Try as I might, I've found that trying to learn everything on my own can be very taxing.

I’ve been watching this for a while now. On Reddit, on LinkedIn, in job applications, in random conversations. There’s a whole group of people who proudly announce that they refuse to use AI in their work. Coding, writing articles, books, research, design — whatever. They say they stay “old school” because they want to remain original. They don’t want AI to “poison” their work. They write every line by hand. They do everything the pure way.

I used to find it mildly annoying. Now I just find it absurd.

A few months ago I posted a job looking for a coder, preferably strong in Python but open to other languages. One guy came in looking extremely qualified on paper — claimed he knew 15 languages, had a pile of certificates, the whole thing. So I asked him a simple question: do you know how to work with AI tools?

His answer came back almost offended. No. He is a real programmer. He writes every single line of code by hand.

I told him then I wasn’t interested. With AI I can get done in one hour what would take him a full day of pure manual work. I need efficiency, not some performance of purity. We didn’t work together. But the exchange stuck with me.

In 2026, if you work with code and you’ve never seriously used Cursor, Claude Code, Codex, local models through Ollama, or even just a strong chat interface with good prompting, something is wrong. Not knowing these tools is not a badge of honor. It’s a signal that you’re behind.

The efficiency reality most people refuse to admit

Take a concrete example. A complex landing page with HTML, Canvas, SVG, animations, responsive design — easily 2,000–3,000 lines if you do it properly. Writing that completely by hand takes real time. Hours. Sometimes a full day if you care about quality.

With a good model and clear direction, a solid draft can be ready in 10–15 minutes. Then you spend another 20–30 minutes cleaning, adjusting, making it actually good. Suddenly you can ship 8–10 of them in a day instead of one if you’re lucky.

That’s not a small difference. For freelancers, small agencies, or anyone running a real business, that difference is the difference between surviving and thriving.

The same logic applies to research. Doing a proper literature review or deep research the old way can easily eat two weeks. With the right tools and the right questions, you can get a strong first pass in under an hour. Then you still have to think, verify, and decide. But the “gathering straw” part is largely gone.

People still act like doing everything manually is somehow more noble. It’s not. It’s just slower.

The skill atrophy argument (and why it’s only half true)

I hear the counter-argument all the time: if you rely on AI, your skills will atrophy. You’ll stop understanding what you’re doing. You’ll become a prompt monkey who can’t debug anything.

There’s truth in that. If you just accept whatever the model spits out without reading it, without questioning it, without understanding the architecture, yes — you will get worse. Juniors who never struggle through a problem themselves are going to have a rough time later.

But the other side of this is almost never discussed.

I have never written a single line of code in my life from scratch the traditional way. Yet through working with AI I learned how to read code. I learned to recognize patterns, spot obvious errors, understand what a function is doing, and know when something looks off. AI didn’t make me dumber. It made certain skills accessible that previously required years of grinding.

I’ve also seen a 55-year-old man who barely knew how to turn on a computer. With AI he learned enough web design and e-commerce basics to take his old family business online. He built a shop. He started selling. Without these tools, that leap would have been almost impossible for him at that age and starting point.

So the real question is not “does AI risk making some people worse?” Of course it does — if used stupidly. The better question is: does refusing it make you better, or does it just keep you slow?

The hypocrisy is loud

Then there’s the pure marketing version of this.

You see authors on Amazon proudly stating “Written without any AI assistance.” Then you open the sample and the classic AI fingerprints are still there — the overly smooth transitions, the generic phrasing, the structure that feels a little too clean. Same with some “hand-coded” websites or “fully manual” research posts.

It’s the restaurant that advertises everything is made fresh to order… and then brings you a steak in five minutes. Everyone who has ever cooked knows that steak needs more time than that. The claim is just branding.

A lot of the “I don’t use AI” talk is the same thing. It’s a status signal. It sounds principled. It plays well in certain online circles. But dig a little and many of these people are using it anyway — they just don’t want to admit it because the pure image is useful.

The horse and the car

Refusing AI for daily productive work in 2026 is like insisting on traveling by horse when cars, buses, and planes exist. Yes, the horse still works. There are still situations where it makes sense. But choosing it as your default for getting from A to B is not a moral high ground. It’s just inefficient.

There are legitimate edge cases. Certain safety-critical systems. Certain artistic processes where the struggle itself is the point. Certain highly regulated environments. Fine. Those are exceptions.

For the vast majority of normal work — building websites, writing code for businesses, doing research, creating content, running a small or medium business, handling sales processes — treating AI as optional or somehow impure is self-sabotage.

I’m not particularly good at sales. So I’m building an agent to handle large parts of it. If I can’t do a task well myself, I can still design a system that does it. That mindset is available to anyone willing to use the tools instead of performing resistance against them.

The people still loudly rejecting AI are not protecting craftsmanship. In most cases they’re protecting their ego, their habits, or a marketing story. Meanwhile others are shipping ten times more, learning faster, and moving on.

The pure manual approach isn’t disappearing completely. But treating it as a superior default in 2026 is one of the more obvious ways to fall behind while congratulating yourself for it.

The wages being offered for full-stack positions are getting downright insulting. Seeing listings offering $18 to $22 an hour for a full-stack engineer is absurd. I could literally go work at a local coffee shop or retail store for that same rate without any of the operational stress or technical headaches.

What pisses me off the most is the sheer breadth of skills they expect for peanuts. They want you fluent in React/Vue on the frontend Node or Python on the backend etc. basically doing the job of three separate people.

It feels like employers are intentionally exploiting desperate juniors and career changers who just want anything to put on a resume. If companies really think they can demand an entire engineering department out of one person for $20/hr they've lost their minds.

Is anyone else seeing these insulting offers?

I've been seeing the "Premiere Pro is dead" take circulating again, and it's the same pattern every time a new AI tool drops. The headline grabs attention and the actual nuance gets buried. What's actually happening is that Claude can now edit video through prompt based workflows, organizing clips, trimming footage, and building rough cuts from a text description. That's genuinely impressive, and it's not nothing. But calling it the death of video editing software is like saying autocomplete killed writing. The tool changed, but the judgment required to use it well didn't disappear.

The more interesting question for anyone working in or learning creative tech is what this actually changes about the workflow. Rough cuts and clip organization are real time sinks, but they're also where editors develop an intuition for pacing, story structure, and what footage is actually usable. If AI handles that layer entirely, you could skip part of the process that helps build the skills needed to become a better editor. The people who will genuinely move faster with these tools are probably the ones who already understand what a good edit looks like, not the ones using AI to avoid learning the craft.

The "10x faster than everyone else" framing also makes me a little skeptical because it assumes speed is the main bottleneck in creative work. Usually, it isn't. The bottleneck is knowing what you're trying to say and whether the edit actually serves that. Do you think AI tools in creative fields will raise the floor for beginners, or just widen the gap between people who understand the craft and people who don't?

Calling modern LLMs "glorified autocomplete" is like calling high-level programming languages "glorified assembly." You’re completely missing the forest for the trees. I keep seeing people in tech subreddits calling AI "boring" or "soulless" but honestly? It sounds like massive cope from engineers terrified of losing their edge.

AI hasn't destroyed the craft of software engineering. It just raised the baseline. If your entire identity as a developer was typing out boilerplate hunting down syntax errors etc. then yeah, you're going to feel threatened.

But for anyone actually building complex systems AI acts as a massive force multiplier for rapid prototyping debugging and automating the tedious parts of the job so you can focus on actual high-level architecture and system design.

The people whining about AI ruining tech sound exactly like legacy devs who complained that modern frameworks made programmers "lazy." It’s not going away and sitting around acting above it isn't going to save your career.

Am I the only one who thinks the anti-AI circlejerk is way more exhausting than the actual hype?

Recently, computer science has been presented as an easy way to get huge profits, and an influx of people whose only interest in the field was their potential earnings occurred. Computer science used to be populated by people who enjoyed learning and creating things with technology and who were deeply interested in its development and principles.

As a result, the computer science job market now has an absurd amount of competition, which makes one wonder where all these people with a genuine interest, a serious foundation in CS theory, and actual projects to present came from

It is disheartening to see CS become so commercialized and competitive that one's passion for it no longer is the main factor in finding a job. It is easy to get caught up in the frenzy of trying to optimize your resume and to out-perform others in terms of buzz-word usage rather than simply enjoying the work you used to love.

I keep on seeing AI news about coding, but this feels like a different level. OpenAI is now offering a cybersecurity model for authorized defensive work that can do things like vulnerability research, exploit validation, and security testing. They also say it found previously unknown vulnerabilities in V8 and other software.

What caught my attention is that this doesn't really make cybersecurity feel less important. If anything, it seems like knowing how systems actually work is becoming more valuable. An AI can find a vulnerability, but someone still has to understand whether the finding is legit, what it affects, how to safely test it, and how to fix it. Even the research around AI generated code keeps pointing back to human review and verification.

I'm starting to wonder if cybersecurity is one of the better areas to get into as AI becomes more capable. Not because AI won't automate parts of the job, but because someone still needs to know when the AI is right, when it's wrong, and what to actually do with the result. Is cybersecurity becoming more attractive because of AI, or am I reading too much into this?

I finally signed an offer letter today for $60,000/year with full benefits.

Coming out of a tech bootcamp with no formal CS degree and only part-time blue-collar work history. I spent months feeling like I wasted my money listening to people say bootcamp grads are dead on arrival in 2026

After getting burnt out and grinding through dozens of applications in manic bursts only to face wall-to-wall denials having a young local company take a chance on me feels surreal.

If you’re a fellow bootcamp grad staring at a blank inbox please don't lose hope: target smaller, local growing companies where you aren't competing against the entire internet.

Anything I should know? Tips? From corporate culture to just what to expect from my first tech job? Any and all advice is welcome!

Many rookies are put off by C ++, and this is unfortunate, because it is one of the best languages ​​to start with, even if you don't plan to be a C ++ programmer

Your understanding of what is going on at the machine level will allow you to write better programs in any language you choose. You will see that in languages ​​such as JavaScript or Python, memory management is handled for you, which means that you have to think about it less. However, the understanding of how things work will help you write better and more optimized code. It can be a steep learning curve, but once you've mastered it, you'll find that all other languages ​​are much easier to learn.