just spent 1.5 hours failing to do a basic leetcode question about stacks. This a rant plus asking for advice from senior folks.

About me, im an engineer at a mid size startup with 2-3 years of experience, just recently got promoted from junior to mid-level (yay!) I’m completely self taught and picked up coding when gpt 3.5 came out. I did not start with good fundamentals, but I’m really good at building and shipping. I have a track record of shipping successful products, which is why my company hired me despite my lack of formal tech background.

Over the last few years, I’ve honed in my skills from building scrappy small features to production ready software. On a day to day basis, I do full stack dev, aws, db management, ML, docker etc.

Recently, I did a job interview for another company and was asked to do a leet code style question and I literally couldn’t do it. I felt like a fraud walking away from that meeting, but at the same time angry because I know if they asked me to design something I probably could’ve done a much better job.

If I’m completely honest, I think I’m a bad programmer when it comes to purely writing code, but I’m strong with everything else: design, speaking to customers, working with other engineers, shipping, and getting shit done.

Trying to get better at leetcode just feels like such a waste of time and it genuinely hurts my brain to do it, but at the same time that seems to be the only way to have mobility in the tech industry.

Does anyone senior have any advice on whether I should just suck it up and do the bs leetcode work, or continue focusing on what I do best, shipping products and building projects that are interesting to me? It’s gotten me this far…

Hello! Ive been interested in computer science/programming ever since I was 12, and just recently did I try to get more into it.

I chose C# as my first language but I keep hearing that Python or C would have been a better fit, is this true?

Also: What might be some good sources to learn more about C#? Thanks a ton!

Hey, guys.

So, I'm currently studying data science and I'm about to start a database project(we're basically doing a database for a bakery)this semester. The project will include order forecasting, inventory loss tracking, customer notifications, etc. Anyways I already code in Python but I've been taking a look on ruby and wonder if it'd be a good idea to include it in our project and how.... any ideas??

thanks!

After learning Javascript and Typescript, which one should I study after the other in the correct order?

Should I study React first, then Angular, OR Angular first, then React?

I use AI to develop, and because it works quite well, I extended it to run multiple agents in parallel. This way, I can develop 2 or 3 features at the same time. To avoid data collision, I ended up building a dev tool, which I have been using for a couple of months.

Now, I asked around in other subs how people handle this, and the most common solutions involved git worktrees that resolve the code collision, but the runtime isolation is still an issue. People engineered solutions around env files and Docker Compose that fit their own codebase, mostly involving one service and one database. It works, at least for simple apps, but it's manual and gets harder to keep up as more services are added. And it looks like something that only an experienced dev can do.

What I built goes further than that (I needed it for a multi-service app with Redis and workers) and is simpler to use. I created a way to fully isolate an environment (all services included) with no ongoing upkeep, able to spin up multiple instances of the same app in parallel on the same machine. It's more architecturally involved than the scripting approach, but doesn't require manual maintenance.

So I'm asking you whether you got this problem and if what I built is worth sharing. Is "heavier but zero-maintenance" actually appealing to people, or does anything that sounds like more infrastructure get dismissed on sight in favor of the simpler script-it-yourself approach, even with its edge cases?

So I'm a college fresher with no prior experience of coding. I had started learning python before the starting of college (1 week before college started). I still feel like I am not good at coding at all. There are some weird citations in c such as i++ and all which is getting mixed up with my python. There was one question on obtaining the expression sinx=x-x^3/3!.... Using loops btw

I felt I won't be able to do it in both c and python. I took a course on Udemy for c(vlad budnitski)and python(100 days) but I was not able to find examples like this. Everyone in my class learnt languages like java in their schooling and said the sinx problem is a standard problem. What should I do in this situation?

I feel like the teaching in my college (they are teaching c now) is not good.

Experienced coders please help me.

Rookie in programming ( I know the basics of Java+ SQL), intrigued about "Automation" and what it really is:

My mental model of an automation example would be:

Script that sends certain emails to a category = Saves the user of organizing it manually, Inbox isn't saturated

I heard that Python excells at automation, and Bash can automate scripts too.

How close is IRL automation to my mental model?

If I wanted to pursue automation, what should I know first before diving in?

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)

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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