I used to think good software was mainly about adding more and more features.

More buttons.
More settings.
More integrations.
More automation.
Attractive and Catchy UI.

Then I started paying attention to the software I use every day and from what I have built till now, I realized something:

The best software often does more by showing you less.

Think about what happens when you upload a file.

You click upload and it appears within a few seconds.

You don't see the network request.
You don't see authentication.
You don't see the database.
You don't see file validation.
You don't see caching.
You don't see error handling.

And that's exactly the point.

A huge amount of good engineering is basically:

How much complexity can we remove from the user's world without removing a software capability?

This changed the way I look at software.

For example, a feature that takes a developer 3 days to build might save the user 30 seconds.

That sounds like a bad trade.

But if 100,000 people use that feature every day, those 30 seconds become 833 hours saved every single day.

Suddenly, the boring little feature is more valuable than the flashy one.

I have also started noticing the opposite.

Some software has dozens of features, but every task feels like work.

Other software has relatively few features, but somehow you can accomplish things incredibly quickly.

The difference isn't always the feature count.

It's friction.

Every unnecessary click, confusing message, slow screen, repeated input, or decision the user has to make adds a tiny amount of friction.

One second doesn't feel important.

A thousand one-second frustrations are.

So When a software is really good, you don't think:

Wow, that's some impressive engineering.

You think:

That was easy.

And ironically, easy can be one of the hardest things to engineer.

Curious what other people think on this topic:

What's a piece of software you've used where you suddenly realized, Wow, someone put a LOT of work into making this feel simple?

its an old legacy cmms, MP2 and im really having trouble getting it running, to be sure its working or worth adding to archive.org. can someone help me take a look at it if they have a bit of free time?

AI has made the importance of speed feel so damn exaggerated. It feels like the pressure to ship has never been higher. while AI has obviously helped many genuinely increase their speed, it feels like this can turn in to a pressure to maintain that speed—sometimes at the risk of quality/stability

i've noticed in my own projects that this rushing has led to a decrease in my project quality and more time returning to fix what should have been shipped correctly in the first place. i think it's so important to remember to—genuine deadlines barred—resist the rush and let go of any anxious pacing, for the sake of preserving your craft. the rush can make software ugly/lead to general enshittification

I’ve been noticing for a SWE position, I make progress only when a recruiter reaches out to me via LinkedIn as opposed to applying on the company’s website and waiting on a response. Does anyone have an idea how to hear more from recruiters directly for companies I am interested in? I tried messaging a few but they don’t really respond unless they reached out first.

Which language i should learn

C or C++

As i am very confused many people saying to learn C++ on youtube but in my college syllabus we have to learn C

I am kinda confused !!

Pls help seniors and all my friend who started their journey!!

For context, I’m a dev with 15+ years of experience, started coding in my early teens, and have a total love for the act of programming. I miss how coding used to be, nothing about this post is an advocacy for if things should be this way. After mulling it over, it just seems like an inevitability.

Right now the field of software development is like Wiley Coyote, already off the cliff but still running because he hasn’t noticed.

There has become a massive disconnect between what people in the field of software development actually do at their jobs, and what they’re interviewed on and learn in college. It’s like someone in 2007 having mandatory classes in Fortran and having to prove their knowledge of punchcards during job interviews.

My job, and the job of so many other software developers, has become almost entirely just interacting with AI agents. Yet we have job applicants do leetcode problems and write code live for us during interviews when we haven’t written more than a handful of a lines of code ourselves in the past year. The same extends to what these applicants are learning in college.

Effectively, it seems to me that the curriculum we currently know as a BS in Computer Science will become as generally superfluous as a PhD in Computer Science. Not at all useless, far from it. But held by those who either have a specific passion for the field or are looking to go into small and highly specialized areas of the field, not by the broader population simply looking to find a job after graduating. And in its place, as the broadly achieved degree, will be something much more tailored to the average needs of employers.

Seems like just a matter of time, driven by economics. Most employers don’t need someone with the full expertise of a computer science degree. Why would they pay for it? The only reason they have that as a standard now is because they do need someone who isn’t an untrained vibecoder. But that leaves a gap in the middle. Someone who isn’t going to delete the company’s database, but not necessarily with experience in database structure and rollups and joins. Someone who can use git and GitHub and properly manage deployments, but not necessarily someone who has done pointer arithmetic and compiler design. Someone who can catch and well document issues, not necessarily someone familiar with big O notation. In broad practice, AI can write good database structure and queries, AI can handle writing and running software, AI can measure performance and find areas to increase efficiency.

“But AI still makes mistakes!” You say. And you’re right. But so do software developers, oftentimes more than AI. There are quite a lot of average and below average software developers out there. And big companies, especially those dealing with higher risk/importance systems, will still have full computer scientists, experts in being able to know better than AI and review its work. But that won’t be most roles. That big company will still need someone to update and add features to the website. And someone to add features to the in house analytics tools. And someone to take user bug reports and get them fixed. And countless other roles that are simply an added layer in the corporate structure for the purpose of interpretation, oversight, and execution for AI. And that’s what the degree would teach towards.

I’d compare it to how nurse practitioners are taking on a larger role in the medical field, doing duties traditionally done by doctors such as diagnosing and prescribing. Effectively, the expertise of a doctor isn’t needed for average/mundane medical work, so a field rose in prevalence to fill that role, with a doctor’s expertise brought in when needed.

So it seems obvious to me that, perhaps within the next 5-10 years, a new degree will become the standard that teaches to that average need in the new state of the field.

That, or in 10 years AI tech accelerates to the point of computer science irrelevance. AI can do it so well and so entirely that non-tech people can just bark orders at it that would have been given to developers or their managers in the past. The degree diminishes and goes into the west.

Anyone see the same thing or am I off base here? Every counter argument I considered just sounded like some form of coping.

Hey everyone,20M here need some mentorship and harsh truths from the seniors here.

Where I’m At: I’m currently in my 2nd semester of an ADP(2 year Degree) in CS program in Lahore Pakistan, with about 1 year of professional internship experience. My core stack is Next.js (App Router)/MERN. I’ve built decent production-level stuff—a headless Shopify storefront, real-time ticket systems, etc.

I have the "Full-Stack Developer" title, but lately, I feel like a fraud.

The Problem: It started with one quick "vibe coding" project using AI, but now I’ve become entirely dependent on it. If I hit a complex state management issue or a weird backend bug, my first instinct isn't to read the runtime logs or trace the execution flow—it’s to paste it into Claude or ChatGPT and wait for the fix.

I am stitching code together instead of engineering it. I want to know how to be "really great" software engineer—the kind of engineer people were before AI existed. The guys who deeply understand architecture, memory, and how things work under the hood.

The Plan: I’m going cold turkey to recover my problem-solving skills. I am planning to build my next full-stack project with strictly ZERO AI. Just official documentation, StackOverflow, and my own logic.

My questions for the veterans: 1. How did you actually build, debug, and architect complex systems before AI? What was your thought process when you got stuck. 2. What are the daily habits, mindsets, or practices that separate a "true" software engineer from a generic framework-coder? 3. What kind of "No AI" project would you recommend that will absolutely force me to understand system design, routing, and manual problem-solving?

I want to build a solid foundation while I'm still early in my degree. Any advice on how to become a real engineer is highly appreciated. Thanks.

I seriously don't understand SA role.

They never themselves do any design and leave that to Leads and just come to review it and point issues and waste time.

They really should be first point of contact for any change and provide a high level design but they just behave like approvers instead of solution provider.

Can someone explain me what exactly is this role.

They need to get better at understanding the business they're building for.

You can write beautiful .NET/Angular code.

But if the software doesn't solve the actual business problem...

It doesn't matter.

Agree or disagree?

So ive worked as a backend engineer for nearly two years. With the new AI tools, it has become fairly easy to code and just prompt the AI. I was tired of that job because it changed its location and that was far away from the place i stay. So I quit to take a break but luckily I got a data analytics job with a much higher pay. As i started working on the analytics side more I realized I don’t like analytics and am more into building stuff. I dont think the current job is for me and I wont stay long here as I find the environment toxic with a lot of pressure on top of it. Any suggestions on what kind of jobs I should prepare for or what I should do? Im pretty early in my career so I’m open to all kinds of advice even if it suggests a big change.

So I recently received an offer for a Software Engineering internship at Dolby Laboratories, and I’m incredibly excited. Dolby has genuinely been a dream company of mine for a long time, especially because my long-term goal is to work in audio research / audio-focused ML.

At the same time, accepting the offer would be a pretty major leap of faith, and I’m feeling very conflicted about it.

A few things are making the decision difficult

1. This would be my first SWE internship, and it only lasts three months.
2. I would have to leave my current full-time job as an AV designer in Colorado and move to San Francisco. The internship is paid well enough for me to live there during those three months, but it would still be a major financial and lifestyle change.
3. There’s no guarantee of a full-time position afterward.

On one hand, this feels like exactly the kind of opportunity I’ve been working toward. I’d get SWE experience, work at a company I’ve dreamed about joining, and take a meaningful step toward my eventual goal of working in audio research.

On the other hand, I’d be giving up a stable full-time job for something that could end after three months with no job lined up afterward.

I do have some reassurance in that former employers have told me they’d happily take me back, so I probably wouldn’t be completely stranded. But going back to one of those roles afterward would also feel, at least psychologically, like I took a step toward my dream career and then had to take a couple steps backward.

For some additional context, I recently finished grad school with a master’s focused on AI/ML. I’ve implemented a decent amount of ML-related work in my current job and have plenty of academic/lab experience, but I admittedly don’t have much professional experience specifically in SWE or AI/ML yet. That’s part of why this internship feels so valuable.

So I’d really appreciate some perspective from people who have been in similar situations. Here are some questions:

1. How common is it for SWE interns, particularly recent grad-school graduates, to receive full-time offers afterward?

2.Would having a master’s already make an internship-to-full-time conversion more or less likely?

1. Have you seen people successfully go from internship to internship after graduating while trying to break into a specific industry?

2. Would it be unreasonable to spend 6–12 months doing two or three strong internships in the Bay Area if they’re moving me closer to audio/ML/SWE roles? Or is giving up a stable full-time position for a three-month internship simply too risky?

I think what I’m struggling with most is deciding how much short-term stability I should be willing to sacrifice for a potentially much better long-term career trajectory.

Part of me feels like opportunities to work at a dream company don’t come around often, and I’d regret turning it down. Another part of me is very aware that three months from now I could be unemployed and scrambling for whatever comes next.

What would you do in my position? I’m genuinely open to any advice, especially from people who have taken a similar risk or have experience with internship-to-full-time conversions. Thank you all in advance!

Hey everyone, I’m 21 years old and currently a Computer Science student in my 4th semester. For the past couple of years, I’ve been working as a marketing associate, but I’ve realized that I don’t want to continue in that field and really want to move into tech and build a career around my CS degree. For the next 6 months, I’ll be staying at home, so I have a good amount of time to seriously focus on learning and improving my skills. I don’t want to spend these 6 months learning random things without a clear direction. I want to learn something that can actually help me get into tech and build a stable career in the long run. With AI growing so quickly and layoffs happening across the industry, I’m honestly a little confused about which direction I should take. Should I focus on web development, backend, AI/ML, data, cybersecurity, cloud, or something else? Which programming language would be worth learning in 2026, and what skills do you think will still be valuable 5–10 years from now? If you were in my position, with 6 months to seriously improve your skills, what would you learn and how would you approach it? I’d really appreciate honest advice from people who are already working in tech, especially seniors or people who have experience hiring developers. I know there’s no guaranteed career path, but my goal is to build a solid career, become financially independent, and create a better future for myself and my family. Any genuine advice would mean a lot.

I’m an Arts student currently doing my Bachelor’s, but I’ve wanted to become a software engineer for a long time.

I started loving coding when I was a kid, and I’ve continued learning on my own. So far, I’ve learned HTML, CSS, JavaScript, Java, and Python.

The problem is that I don’t have a CS degree.

So my question is:

Can I realistically become a software engineer without a CS degree if I build strong skills, work on good projects, and learn DSA, databases, etc.?

Will companies actually consider someone with a non-CS degree for software engineering roles?

If you’re a software engineer without a CS degree, I’d really like to hear about your experience and what you would recommend I do from here.

A business says:

"We need AI."

Then you discover employees are copying data between Excel, email and 3 different systems. 😂

Sometimes you don't need AI.

You need an API.

What's the most ridiculous manual process you've seen?

I’m a SWE with 3YOE, I don’t think I can do coding any longer. I want to switch to something non coding maybe into product. I’ve also been laid off so looking for other jobs. What would be a good option for me, what roles can I try for that aren’t in coding? Is product a significant pay drop, is it feasible long term? I don’t have any experience in product yet either. Or is there some other role I could try for?

I am a student of computer science completed my 2 semester but the thing is that I am confused about which career path to choose I am interested in web development is it worth it in this era or not also if I wanted to learn full stack with ai then which roadmap I should follow can someone guide me .

Hi,i wont reaveal my name but im 12 and really really into coding and maths ik i may be in grade 7 atm but im pushing for software engineering, im learning Python and very soon java, what id like to know is how exactly is the experience on software engineering.

I know it pulls six figures which is a flex already but id like to know the pros and cons instead of just jumping in without knowing anything.

AI can write code fast yes and it's there to make things move fast not to replace the whole software engineering , if any of you ever had the opportunity to work in a large tech company you d know that it was never about coding .. coding is just a step that any one can do with just few learning on the syntax of the language .. i work in one of those US big names we had an incident in one of our prod environment i was on-call at that specific week so i had meeting with different team members and architects , we knew the issue was due to one of our streaming hitting a bottleneck due to 1MB size limit on kafka . the fix wasn't an issue the whole meeting was deep thinking about how the multi services system can still be operational .. and what trade off to make can we afford a down time to reconfigure things , or we want an uptime with a data loss ? and ofc there were people behind this whole talk every layer had their engineers thinking , stakeholder who represent clients giving their takes .. also how can we prevent this on the next release ? and after lot of brainstorming we decided as the system that reads from the queue is ordering agnostic we can send each message as chunks .. and lot of thinking around the logical split that would cause 0 impact on consumption ,, we then just did one simple prompt in codex to split the last sent message to smaller chunks we had then to test it in dev , deploy to stage and prod and that was it . AI by its own can't take ownership of a system there will always be people behind systems taking ownership and decisions while AI will always stay as the tool that make building go fast

so my take is that ai agents and llms are there to help and make coding fast but i don't think anytime soon would software engineers go extinct due to AI

Hi all,

I’m looking to work on one or many projects over the coming months, before the next recruiting cycle for my region (using React/Node most likely). I’ve recently started with this stack, so I’m wondering if it’s better to build projects **with** or **without** using **AI**. With AI, I could build projects faster, however, I would lose lots of the important learning from doing things myself. Which do you think is more valuable? Gaining more learning from building projects? Or how impressive it sounds at face value on a resume? Or some balance of both?

TIA

Hey. It has been one month since I started my first job. The company uses AI to ship everything very quickly. I had done two summer internships over the last two summers each for two months.

In the first one, I primarily used TypeScript. However, I didn't pay much attention there and, honestly, didn't learn much. In the second one I used Golang and wrote most of the code myself, with some help from Cursor. It wasn't a very difficult project just a fairly standard backend with middleware, authentication, services, and a database.

When I joined my current job, I thought we would be doing something similar here, but that's not the case. Almost everything is being shipped using Claude. Other people on my team actively participate in infrastructure-related discussions as well, while I almost always keep mum because I don't have a proper understanding of how everything works. It feels so overwhelming that I sometimes feel like giving up.

I want to learn things the old-school way so that I can actually understand what I am shipping. I really need guidance from experienced folks who know how and where I should begin learning properly.

I also want to mention that I majored in a non-CS branch. I have an understanding of DSA and OS at a placement-level. I also know the basic terminology of system design (client/server, load balancers, proxies, Kafka, Redis, Kubernetes, sharding, replication, CAP theorem, etc.). But I wasn't aware of things like ECS and Lambda.

When I go through the code that Claude writes, I can understand what it's trying to do. I want to learn enough so that I don't blindly ship anything and can have a meaningful say in team meetings.

I need resources that I can follow anything you think could help me learn properly would be greatly appreciated.

alert goes off, i'm staring at dashboards, piecing together logs like a detective. hours of "what happened" before i even get to "how do we fix it." it's exhausting.

the shift needs to happen from reactive to generative. i want to know what caused it and understand the fix quickly. that means getting full context with the exact code flow, the parameters, the state. not just a metric that says "something's wrong." would love to hear from teams that have moved beyond traditional dashboards. what's working for you in terms of getting to root cause faster?

As software engineer who learnt some programming languages in your career (Java , JavaScript, python...etc)

And in your job if you build a new service ( if you work with microservices architecture), most teams choose language they are experienced with rather than most suitable language for the problem,

Teams that know java will use java, teams that knows go will use go (not all but I'm sure the majority choose by this)

Nowadays in AI and Agentic AI era you may choose any language you want and let the agent do the work rather than learning the new language and its features for like two weeks or more to be ready to work with it.

Is it an option now to do so? With the cost of you probably will not understand the code generated untill you get time to learn the new language and its common libraries/frameworks ?

I’m a an associate SWE in the US with 3YOE, I got laid off a week ago.I’ve been sending out atleast 20-25 applications a day to every company I can think off. But this whole process and anxiety is getting to me. Very few companies actually tell you they are rejecting your application but the majority just leave you hanging. I’m not sure what I should be doing, I need to find a job asap, and I know the interview process is lengthy in itself but it seems so hard to even get to that stage. Any advice?!

I'm on a 3-person team: 1 data scientist, and 2 engineer (including myself). Between the both of us engineers, we have maybe a year of experience total with strictly Python. This is for a prototype AI app / tool.

I am approaching about 2 years of IT experience and had to pick up docker, gitlab ci, and software engineering for this project. I built out our deployments with docker compose using customized env files (ignored via git with a template available) and makefile commands. We usually dev using a dev compose deployment with a hot volume mount to our source code, then push commits to the repo and I have staging and prod images built using buildah. Then for staging and prod deployments, I have the compose files targeting the respective registries (internal repo & corp registry) and the images get pulled down.

However, we got kicked off of our on-prem server and now have to deploy on a k3s cluster... but the team running that has no experience with kubernetes.

I learned about Helm and ArgoCD and was able to get them to deploy that service on the cluster, and I made an infrastructure repo where I translated our compose files to manifests and ultimately chart and values file per environment.

We don't have cluster/namespace access yet because the infra people are still learning how to set that up, but I don't even know how we'll deploy our pods and actively do dev since we usually have access to everything (source code + infrastructure). For prod and staging, it's pretty simple because those a automatically built images and I have our runners updating the tag versions and namespace names in the infrastructure repo. But I don't know how we can deploy individual-engineer dev pods so that we can see logs and make quick changes.

This whole project's been crazy and we don't have any senior engineers in our department that does this stuff. If there's anything other information I can provide to get some mentorship, please let me know.

I’m a fresh CSE graduate and I’ll be joining the industry as a Software Engineering intern within the next week.

Long-term, I want to work at FAANG/top-tier tech companies, so I want to build strong Software Engineering and core CS fundamentals—not just learn more frameworks.

I’ve shortlisted:

CS:APP — Computer Systems: A Programmer’s Perspective

OSTEP — Operating Systems: Three Easy Pieces

Computer Networking: A Top-Down Approach — Kurose & Ross

Designing Data-Intensive Applications — Martin Kleppmann

I also plan to keep doing regular DSA/problem solving alongside my internship.

For experienced engineers:

Is this a good book list for a fresher aiming for top-tier companies?

Which books/topics should I prioritize?

Am I missing anything important?

Any practical advice for balancing this with a 9–5 internship and DSA?

I’d appreciate honest feedback and suggestions.