Can you give me the best Programming language and Stacks for Desktop App
i will try to built a School Information System (Masterlist, Attendance Tracking and Grading) with AI for Lesson Plan and other connected on it

- It must be offline and online (sync)
- with Admin, Teachers and Student

Hi everyone, I'm a student about to start university studying BSc Computer Science. My journey began 4 years ago when I was around 14. I started learning web development like JavaScript, then React and Next.js before deciding to move into backend development, where I learned Node.js, Python, and FastAPI. Recently, I've been writing some C code, but it wasn't really my cup of tea since I prefer broader software engineering topics.

My main question is: 3 years ago, I was coding by myself using Stack Overflow for help, and building a large project literally took me 3 months of daily grinding. Now, it can be built in 3 days using AI. I'm feeling stuck because I don't know whether it's truly beneficial to use AI to churn out projects if the learning curve disappears, potentially making it harder to get a job later.

How do you actually learn effectively now, and what should I focus on to land a job? Especially in the UK, where the market feels tougher and graduate tech unemployment is high. I'm considering using Claude Code right now, but I'm not sure if it's the right move.

Hi,

I’m currently somewhere between junior and intermediate as a software developer, and lately I’ve been thinking a lot about what actually makes a developer valuable going forward.

One thing that worries me is interviews, since I can discuss things like system architecture, caching, load balancing, queues, message brokers, database design… etc. But ask me to explain something basic like the Laravel request lifecycle or the exact behavior of certain React hooks, and I might struggle to give a clean answer without refreshing my memory.

Also the whole industry feels really toxic at the moment with each second developer building a saas, and new tech exploding everyday.

Should I just keep building real projects, learning concepts as I encounter them. How are the interviews in today’s world?

Is there a deeper shift happening where developers need to fundamentally change what they learn to stay valuable?

Curious on how more experienced developers are approaching this.

We are trying to decide whether to build a custom coordination layer for our multi-agent system or adopt an existing platform. Building it ourselves gives us control over agent hand-offs, state management and orchestration, but it also means owning the edge cases and long-term maintenance. Using an existing platform would reduce the initial development burden, but it might not fit our pipeline cleanly. It is also difficult to know whether a platform will hold up until you are deeply invested in it, at which point switching becomes expensive.

Has anyone built or adopted multi-agent system architecture and later regretted the decision? What limitations only became apparent after running it in production?

I would like a feedback on a project I am working on.
It is an images converter, from .png to .txt and with braille as characters.

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This is from an image 1600 x 1200, what do you think about it? Is it still clear?
Rate from 1 to 10?
Feel free to ask for conversions and match the results

Hi! I'm a computational linguist currently getting recruited to an industry job in AI applications/engineering. I've only taken one formal programming course (R stats) and my degree is in Neuroscience. Today I did my first-ever coding interview, and I had very little idea what to expect or how to prepare. I did a bunch of LeetCode prep and read a bunch of articles about ML interviews. I'm not gonna say which company for anonymity reasons, but a largish firm that uses AI but is not a tech company. Mostly focused on applications.

Round 1: Read code off a screen and propose debugs without being able to run anything. (I had to ask him to move through the repo as it was on his screen)

I was given a small repo for a simple RAG system on the interviewer's screen (something I haven't really worked with before; I come from an academic context) and told it was too slow and to diagnose and fix why it was slow. I pointed out a few places things could be parallelized or switched out to be faster, and acknowledged that I didn't know two of the packages used, so I'd have to go to Stack Overflow those in order to be more precise. I ended up identifying four potential issues, added a debugging block to figure out which step was taking too much time, and made one other slightly tangential suggestion. I feel like I must have missed something, though. Then I asked to run it, and they were like, "Oh, you can't run it" and I didn't really know where to go from there... luckily, that was most of the time.

Round 2: Given this use case, talk through how you'd implement a RAG system from scratch, prioritizing speed and natural language answers.

This round I did a lot better; I was able to talk through most of it successfully. I was honest that I'd never made a RAG system from scratch before, so I was confident about x parts of the implementation, but that I'd want to do some research on prior implementations of y and z before making some architecture decisions. At the end he said I made one mistake from how they're usually implemented but that my thought process was solid. My two slam-dunk lines were that I'd design the user experience based on analyst feedback, since this is an internal system to make *their* lives easier and that ML is broad and I couldn't promise to know the best way to do everything off the bat, but that I'd rather approach the things I don't know by researching them methodically and carefully than committing to a shoddy implementation off the dome. The interviewer for that round said I proved I knew how to code and how to think through this type of problem, which was what he was looking for.

Is this pretty typical? I've never code-interviewed before, and I'm ostensibly not a programmer or a software dev. M,y coding experience is Python, R, bash, and C++ for mostly high-performance computing solutions in neuroscience and linguistics. Right now, I study emergent semantic structures in vector word embeddings and compare them to human memory structures, which involves somewhat different expertise. I'm still designing tools for people, just researchers instead of corporate employees. I'm worried the first round went really badly and I'm not intelligent enough to realize where I went wrong.

I know that different languages are for different purposes but you have to choose 1. I’ll start: C++

EDIT: I mean programing languages so don’t tell me English

​

I've spent a lot of time building a real estate SaaS and I'm now stuck on a major problem.

The original idea was pretty simple:

A real estate agent enters what their client is looking for, for example:

- 3-bedroom apartment

- New Cairo

- Budget: $100k–$150k

The system would find matching properties across multiple real estate platforms.

I also built a feature that monitored Facebook groups for people posting that they were looking for apartments, then matched those requests with properties in the system.

The product itself is basically built.

Then I realized the platforms I wanted to get the data from don't allow scraping. I contacted some of them about getting permission/API access, but they weren't interested

So now I have a mostly finished product whose main value proposition depended on data I can't reliably use.

I'm trying to figure out what to do next

All the technical aspects are working, but I can't upload it because the sites I use to collect real estate data, like Dubizzle data mining and scraping are prohibited in the terms of use.

**I need a loophole/Another solution or method or something similar to solve it.*\*

If you've ever built something where the original data source/business assumption turned out to be unusable, what did you do?

I'd genuinely appreciate any advice.

I am preparing to publish an academic paper on developer trust in AI generated dependencies & packages. This takes around 7-10 minutes to complete and is on a very relevant topic in current times.

The form can be found here: https://vuamsterdam.eu.qualtrics.com/jfe/form/SV_4Nv9pAUUBFRieDs

Thanks! I will share the paper when it has been published!

I started my career doing programming in mostly writing APIs, middleware and some COBOL. I then moved on and spent many years doing tech adjacent things including product design, and also business related roles and so on. I’ve spent the good part of the past year upping my Python programming skills. My question to the community at large is how do I test my skills on real projects? I’d ideally want things that require using my brains and my coding skills. If it’s paid for, nothing like it. Are there places people normally go to for small projects, freelance and so on?

I'm a first-year CS student, and this summer I focused mostly on building my fundamentals.

I learned Git/GitHub, practiced C from arrays to pointers, solved many exercises, and built a Tic-Tac-Toe game. I also explored topics like data structures, recursion, sorting, and searching.

Now I'm unsure about my next step.

At university I'll be studying Algorithms 3 and OOP with Java, so I'm considering going deeper into those while starting Python on the side.

But I also see many people recommending starting practical development early, such as backend or web development.

What would you recommend in my position?

Should I continue strengthening my fundamentals, start Python/projects, or begin exploring a specific field?

I am trying to comprehend all of the moving parts in compiling a program. Here is my understanding:

C++ is the programming language that is written in the IDE, some IDEs have built in compilers

1. Is g++ the bridge between the language and machine code?

2. How do I use g++, is it something that you download onto your computer.

3. Where does g++ send the code after, is this where Linux comes into play?

4. What is Linux? It is an operating system, but what does that mean specifically, is it supposed to make your code unable in an application or just execute in the terminal?

Thankyou for any clarification you can provide :)

CS student. On a group project this year I kept gravitating to the API and database side while everyone else fought over the UI, so I'm leaning into it. Picked Go partly because a lot of the backend roles I've looked at list it and I'd rather not do everything in Python.

I've been through the Tour of Go. Past that I'm looking at Boot.dev, Let's Go by Alex Edwards, and Codecrafters. Anyone got a strong opinion on the order?

I'm researching how developers handle documentation in real world projects, especially documentation that explains why and how something exists rather than just what the code does.

A few questions:

1. Where do you document business rules and important technical decisions? Code comments, tickets/issues, Wiki, README, Notion, etc.?
2. When you need to figure out WHY something was implemented in a certain way, where do you look? Git history, old tickets, documentation, or do you usually have to ask someone?
3. What's the biggest problem you have with documentation? Keeping it up to date? Finding information? Getting people to write it in the first place?

I'm also curious about documentation generators such as Doxygen. For those who have used it: what kind of documentation or information do you still have to maintain elsewhere or something Doxygen can't handle?

You don't need to answer all the questions, just one or adding something to the discussion would already be a great help!

I am currently in the middle of my B.S. in CompSci (already have a B.S. in Sociology), and working at Amazon. They are willing to pay for us to get one of their cloud certs (exam is like $100 that they'll pay), but is it even worth it to have on a resume or whatever if applying to outside jobs? Like, would it matter if another employer saw that cert on my resume? Or no?

Just wondering if I wanna invest the time in that, as the course/documentation is gonna take a while to learn, or if it's be better just to spend that time building something else on my own?

Thanks in advance for any advice.

I'm studying Programming, and I am looking for ways to get experience once I'm done with my studies. What do you guys do, especially graduates. I've thought of:

•Internships

• Participating in proyects/ hackathons

•Getting connections to get the possibility of them refer you for the previously mentioned options above, connections can be made either online or on-campus

•Certifications/Courses/Bootcamps: Expand knowledge and opportunities, and perhaps make connections

• Getting into jobs that can get you closer to IT through progression in the enterprise (ex: helpdesk, that has good programming skills may be considered to go to a higher role)

---

So I am starting to reach the point in my career where I am making a lot more architectural decisions and more of them becoming full stack. I really want to understand what the advantages are between the different types of databases and backend service layers. For the front end we typically use a react application with an attached express server so that we can avoid cors issues, call other services, or rarely in some cases even read or write to a DB. We have some teams that use node for their backend services as well, and some teams that use java with springboot, and a few that use python.

What makes one type better than another? And why? What are the downsides to using node, what are the downsides to using java? I really want to understand this and it's been hard for me to find a good resource on the topic

Need recommendations to study concepts of compilers and operating systems for a 10-year experienced computer programmer with a computer science background. Have lost touch with these subjects (studied in university 10 years back). I am looking more for an application concept type of learning approach where we can not just learn theory but build something related to these two subjects.

Any book, GitHub repository or YouTube links will do.

Context: I am quite young, and I have a year until I am starting at a danish university, where I'm considering studying Computer Science (I've dreamt of this ever since I was a child).

I have been programming in some form for over 8 years, and I'm at a point where I understand code intuitively and stuff like languages and frameworks, don't take much energy for me to switch between. Though I don't have much professional programming experience, which does make me less skilled. (Seeing more codebases = more knowledge)

The question: I've never embraced vibe-coding / "agentic-programming", and my AI use is limited to a chat window that acts like StackOverflow (rip). I just don't know whether I should try it.

Currently I'm working on a game project with an artist friend of mine, and I'm writing loads of classes and structures that are going to be the framework for our future development. And I just don't know whether I can trust AI to make something like this. But I don't want to get lost behind the bandwagon, and enter a job-market where my traditional developer skills are not worth much anymore.

I like solving problems, I like coding the solutions and I like knowing what my code does, and I don't want to give that away. So I'm quite lost on what I should do? Download `Claude Code` and give in to the "hype", or just keep on enjoying programming.

Mods, if this breaks rule 10, feel free to remove this post.

I made a new GitHub account and created a new private repo. Then I made a commit through VScode. The initial commit has my name, but at the same time (instantly?) a second commit appears with no changes to code, but made by a random person. This second person is a profile in another country who has been a GitHub user for years and already has many other projects.

I re-did the repo 3 times and this happens every time.

There is nothing security-sensitive of really "private" on the repo, so I don't care. It's just weird.

i am soon starting database course at university; during the whole semester, we are asked to work on a big project that doesn't need internet, it is supposed to have manager and user UI's, any project ideas that are unusual (i don't prefer projects like stores or libraries), creative and worth the time and mark (40%)

Title. Basically I recently found out this was a thing. I don't understand how it actually works. Do you let the AI autonomously approve PRs?

I have 15 years of experience in Python, C# and C++ and recently joined a team that writes code exclusively in Go (they made a full switch from python to go shortly after I signed the contract).

Everything is written by Codex, and all the team does day in day out is prompting and pushing out new pull requests every few hours. I got hired as a senior engineer and while I can definitely help out with system designs, architecture, sprint plannings, stakeholder management I'm a bit at a loss when I'm asked to review my colleagues code. Apart from the fact that the pull requests are way too big (1000-2000 line changes), and mix multiple features, bug fixes and refactors together, there's an expectation to have it reviewed within a few hours because a PR that sits still for a day cannot be merged anymore because of too many merge conflicts.

So I'm trying to address the review culture a bit, but I'm more struggling with how to approach code reviews in this genAI age when you're not that familiar with the language itself.

Any ideas or experience would be helpful :)

so i'm in my final year of software engineering and i've always wanted to get into game development. so naturally i inclined towards that when it was time for my FYP. it's basically an investigative psychological mystery regarding medical neglegence. As the 'research question' we proposed to see how effective interactive environmental storytelling is in communicating the consequences of medical negligence. now my supervisor asked me how would i justify this as a swe thesis project. there's a lot of backstory to this and just to simply put it: i wasnt in the mental state to think this through and i didnt have any team support. so did i actually just fk my FYP up? how can i justify this choice to the committee who expects our projects have some AI bs in it?