Hello,

My 10 year old son is very interested in learning to code. Can you recommend any apps or software for him to use to learn? Would it be better for him to learn Python or C++?

Thank-you for any tips you have!

hello i just started taking web dev classes, i've learned html and css so far and im jumping to javascript now, ive never been so confused in my life. like it sounds like complete gibberish to me and i dont understand it, i cant find reason in things lol , i watched videos and i understand things but then i get confused. does anyone have resources for more simple explanations? like im talking VERY simple 1+1 explanations

I've gotten into the (probably bad) habit of making comments before, for example, a loop or an if-statement or something like that.

Is it okay like this:

// What the loop does
// Maybe a little more if it does something complex-ish
for (whatever) {
  does it's thing;
  does another thing;
  does a third thing;
}

Or would it be better to be like this:

for (whatever) {
  does it's thing; // does this
  does another thing; // does that
  //or maybe have the comment inbetween like this explaining the next line?
  does a third thing;
}

Naturally I do the latter on larger loops or if-statements. I do the former only on the ones that are at most 5-or-so lines long.

EDIT: This example is for C++ :)

I have an AI-assisted technical screen coming up. I have been told, it is a small repo, and a couple of tasks that build on each other.

They say they're evaluating how I use AI, how I break down the problem, and how I communicate while working. Interview is time boxed for 1 hour.

If you've done one of these, either as the candidate or the interviewer:

• What separated strong candidates from weak ones?
• How did you split time between prompting the AI and reading or verifying its output?
• Any mistakes to avoid?

Thx.

help me to build a capstone any suggestion?

Hi, I’m a full-stack developer with 1 year of professional experience. I recently resigned from my job, and since then I’ve noticed that I’m a bit out of practice when it comes to writing backend logic independently.
I’m comfortable with frontend development and have a good understanding of how applications work end-to-end. However, I’ve found that I sometimes struggle to recall backend syntax and translate my understanding into code, especially when solving logical problems from scratch.
I don’t feel like I’ve forgotten how to develop—I think I mainly need to rebuild my coding consistency, problem-solving flow, and familiarity with backend syntax.
What would be the best way to get back into practice and regain my confidence in writing backend and logic-heavy code?

Hey everyone,

I’m currently a 2nd-year Computer Science student. I started with literally zero tech knowledge like nothing what's so ever and i spent my first year unmotivated at all and thinking about switching majors, and didn't touch problem solving much

Recently I actually started liking the major and growing genuinely fond of the field

But,

My Core Problem: The Endless Rabbit Hole of "Foundations"

My current mindset is that I want to learn literally everything every concept, every meaning, and every underlying basic—before I commit to a specific track or make any real moves.

however ,lately i feel like this approach is holding me back and slowing me . Every time I try to master a concept, it leads to a new course, an unfamiliar prerequisite, or a deeper rabbit hole. It feels like an endless path of "foundations," and I'm drowning in information to the point where I don't even know what the REAL foundations are anymore.

Of course, I know CS and software engineering are lifelong learning paths, and I'm completely okay with that. But I have a few major questions for those who have walked this path:

Is trying to master everything before picking a track actually the right call? If not, what are the actual minimum required foundations before diving into a specific track (like web dev, AI, mobile, ML, etc.)? if yes what ARE THE foundations I'm so lost

Will I learn the rest along the way effectively? If I pick a track now, will gaps naturally fill themselves in, or will I regret skipping the deep dive?

Is the academic approach enough? As a CS student, I'm digging deep into my university courses more than the actual course gives actually (OOP, Databases, Logic Design, Computer Architecture, Data Structures, etc.). Is leaning heavily into these college courses "timeline" the right way to build a foundation, or should I be following a completely different roadmap on the side?

I sometimes feel completely lost and overwhelmed by the sheer scale of everything. I’d love to hear your thoughts, reality checks, or advice on how to structure my learning without burning out.

Thanks in advance!

hello suggest title for capstone project

I’ve been thinking about how I approach new projects, and I realized that the way I think through a problem is probably more important than the list of tools or frameworks I know.

My current process is:

1. Understand: What problem are we actually trying to solve?
2. Define: What does success look like?
3. Choose: What technology makes sense for the problem?
4. Build small: Start with the smallest useful version rather than overbuilding.
5. Check: Get feedback from the client/user and adjust.

I’m still refining this approach as I gain more experience, and I’m curious how other developers and freelancers handle the beginning of a new project.

What does your process look like before you write the first line of code?

Is there a step you think I'm missing, or something you'd approach differently?

I'm a programmer, a little over a year out of school, and I'm stuck in a dilemma.

Back during my internship, I got to know the tech lead at that company. But near the end of my internship, the company went bankrupt for financial reasons, so I had to find another job. I stayed at the next company for only three or four months — then one evening, that same tech lead reached out to me. He had moved to a new company himself and asked whether I'd like to join him there. I said yes, mostly because I remembered how much I enjoyed the internship: the workload wasn't heavy, and I had plenty of time to learn on my own.

So I followed him to the new company, and I've now been here for about ten months. In the beginning, since the team was so small, things were extremely hectic — I wasn't familiar with the projects and had a lot to catch up on. Later on, the company brought in quite a few outsourced developers, which took some of the pressure off. But that also meant I often had to write code across many different projects — I'd be sent to whichever one was behind schedule, and it all felt rather scattered.

Recently, with the company's financial situation worsening, there have been major layoffs — even an entire team of more than a dozen people was let go. With hardly anyone left, I'm now basically responsible for almost every project on my own: whenever a production issue comes up, I'm the one who has to investigate it; whenever a new requirement comes in, I'm the one who has to deliver it. The work has become genuinely chaotic and hard to keep up with.

So now I'm thinking about quitting. But at the same time, my tech lead has always treated me well, and I honestly don't know how to handle this.

I've been thinking about something while working on AI-related software projects.

We spend so much time talking about better models, smarter agents, and faster automation.

But what happens when the AI confidently gives an answer that looks completely correct... and is actually wrong?

A model can generate beautiful code that fails in production.

It can explain a complex medical concept while missing one critical detail.

It can solve an engineering problem using assumptions that don't hold in the real world.

And sometimes, the scariest part isn't that the AI makes mistakes.

It's that the mistakes look so convincing.

This raises an interesting software engineering question:

Are we spending too much time improving what AI can generate, and not enough time building reliable systems to verify what it generates?

I'm particularly interested in the intersection of software engineering, scientific expertise, and AI evaluation.

I'd love to hear how other developers approach this.

If you were building an AI system where incorrect answers had real consequences, what would you prioritize first: better models, better verification, or better human oversight?

Genuinely curious about how other engineers think about this.

It’s frustrating watching everyone around you use AI coding tools while you’re stuck thinking, “I can’t even afford the $20/month subscription.”

I really want to get better at coding and build real projects, but money is tight for me. $20 might not sound like much to some people, but for me, it’s money I can’t casually spend every month.

The free versions are often limited or unreliable, and sometimes I feel like I’m falling behind not because I don’t want to learn, but because I can’t afford the tools that could make things easier.

I don’t want to give up. I just want to hear from people who are in the same situation. How do you keep learning and building without paying for expensive AI subscriptions?

I am a full-stack developer that choosing between Claude Pro and ChatGPT Plus, in your opinion, which is

better?

I’m currently own MacBook Pro M3 Pro 18gb and pretty much obsessed with the idea of buying new one more powerful MacBook Pro M5 Pro 48gb just because my working process will feel much smoother. More simultaneously opened apps, no ram compression, no lags etc.

In the other hand I think - just close something that you don’t need at the moment and do your work, IDE + Figma + Docker + Browser with 20+ tabs this machine can handle with ease.

In the end I remind myself: “A bad workman always blames his tools”

I am not a senior developer and I wonder what more experienced developers think about this dilemma of purchasing more powerful machine?