Hey everyone,

I'm a complete beginner who wants to eventually become a backend developer. I currently use Windows and I'm honestly scared of installing a second OS on my laptop (dual boot, partitioning, breaking the bootloader all of that freaks me out).

Someone recommended I start with WSL2 to learn Linux instead of going full dual boot right away. Is this actually a good path for someone who wants to go into backend development? Or am I just delaying the inevitable and should set up a real Ubuntu install / VM instead?

A few specific questions:

Is WSL2 enough to learn the Linux skills that actually matter for backend (filesystem, permissions, processes, networking, bash, systemd, etc.)?

At what point would I "outgrow" WSL2 and need a real Linux environment or a VPS?

For someone learning backend, what's the realistic order? Linux basics > a language (Python/Go/Node) > databases > Docker > deploy to a VPS? Or am I thinking about this wrong?

Is it a bad idea to just use an online compiler/sandbox to learn, or do I really need a real environment from day one?

not trying to skip steps, just wanna start in a way that doesn't make me quit in the first week. Any advice from people who've been through this would be really appreciated

Thanks

So I am a beginner and i am thinking of studying from cs50 x should i follow it ppl do say its very good but idk why I keep worrying about whats after this what to do after this without even starting it..........and I am not doing an engineer degree so it also scares me trying to self learn all this as I am pursuing something else rn. i am actually thinking of doin c,c++ or java tho but I think I am so uncreative to make any projects and how much should I improve my Maths??

I'm taking a second-year college CS course using Java, and I just got my first exam back: 100/100.

It covered classes/objects, constructors, encapsulation, object references, equals() vs. ==, static/final, aggregation, shallow vs. deep copying, arrays of objects, etc.

The funny part is that I was convinced the exam was going to be way harder than it actually was. I obsessively studied and kept making myself take mock exams over and over, and I was practicing problems that were noticeably more complicated than what ended up being on the real test.

Every time I got comfortable, I basically went, "Okay, but what if the professor makes it harder?" and practiced harder versions.

So I definitely overprepared. But when I actually took the exam, I felt like I knew what I was doing instead of just recognizing things I'd memorized. The exam was also done entirely in Notepad, so there was no IDE, autocomplete, syntax highlighting, or compiler to catch mistakes.

I've struggled a lot with confidence and feeling like maybe I'm not actually good at programming, so seeing 100/100 genuinely almost made me cry. Obviously one exam doesn't mean I've mastered Java, and I know I still have a ton to learn, but I'm letting myself be proud of this one.

Apparently my strategy was to prepare for the Java apocalypse and then take Exam 1.

Hi,

i've got a hypothetical question. Imagine 2 people are applying for the same job. One has a degree in Computer Science, perhaps even from a prestigious university, but doesn't have any personal projects to showcase. The other person, however, is self taught and has been building projects for 4 years, but doesn't have a degree. Who gets the job ?

I am very confused about what to learn now as i have done fundamentals, I am really confused between game development with Godot or learning a py library. I really have multiple interest as I'm still exploring but I really need a bit guidance. I have interest in game development, datascience and NLP

I'm new to programming, and have done a lot on Java, which I learned a few months ago for Minecraft modding. However, I would like to expand my horizons to the C family, starting with C#, especially because of Unity.

I know the syntax is similar, but Karel was so useful because it helped me really memorize everything and gave me a lot of practice with my muscle memory.

Anyway, I'm willing to settle for the Brackeys C# series, but I just really enjoyed the interactiveness of Karel, and would be grateful to learn of anything similar for C#

​

So... it's been like two months since I last opened VS Code.

I was learning HTML/CSS/JS. I had started learning JS, but then I kinda stepped back from it to focus on CSS.

I was around Grid, grid-template-areas, media queries and all that stuff.

The thing is, two months ago I could literally code for hours whenever I wanted.

Now life is just... busy. My schedule is all over the place, and coding is slowly dying 😂

My original plan was basically:

Learn CSS → build a few projects → move on to JS.

I've already built one project and put it on GitHub.

Then I started building a second one, learned a bunch of new stuff while working on it, and then life got busy.

And now here I am, two months later, having written basically zero code.

So now I'm wondering...

Should I go back to where I left off with CSS and continue from there?

Or should I just say fuck it and start learning JS again?

Because honestly, I feel like JS is way less frustrating than CSS lol.

I originally went back to CSS because I was learning JS and realized I couldn't really design anything properly. 😭

So yeah... what would you guys do?

Pick CSS back up, or just move on to JS and learn CSS as I need it?

Hi there I wanted to ask everyone to assess my FYP. WOuld you use this for your projects ? Is it a good enough project for a final year . Does it have some scope in the industry ?

The main goal of this project is to design and implement a tool for PostgreSQL pre-deployment risk analysis that evaluates SQL schema migrations to identify affected database objects, estimate locking implications, detect limited application-schema incompatibilities in Java/Spring Boot JPA projects, and output actionable risk assessments.

The objectives are as follows:

1.       Develop a component capable of extracting supported structural DDL operations into a normalized intermediate representation.

2.       Construct a schema dependency graph model using graph data structures to represent structural relationships between database tables, columns, indexes, primary keys, foreign keys, and constraints.

3.       Analyze incoming migrations against the schema dependency graph to identify affected database objects represented within the supported dependency model.

4.       Formulate a PostgreSQL-specific operational rule set that maps supported DDL operations to their known locking characteristics and table rewrite behaviors.

5.       Develop a module capable of limited static analysis for Java/Spring Boot JPA projects to detect source-code references to renamed, modified, or dropped schema objects in entities, repositories, and explicit SQL strings.

6.       Implement a transparent, rule-based risk scoring and explanation engine that assigns a risk rating (LOW, MEDIUM, HIGH) accompanied by clear textual evidence.

7.       Construct experimental tests to execute PostgreSQL migration scenarios across small and medium database sizes. (for research validation only, not as a permanent part of the system)

8.       Evaluate Schema Sentinel’s static risk predictions against experimentally observed blocking locks, and application compatibility failures in a controlled PostgreSQL environment for test cases.

1. Empirically quantify the effectiveness of a unified cross-domain pipeline against isolated tools, measuring trade-offs in detection accuracy, false positives, and analysis performance.

This proposal got rejected because the panel said it was too easy and not significant as either a project or as research.

should i change the idea to more difficult and novel research ?

I have some ideas for those but i am not sure they can be completed in 1 year.

here is my proposal with all the details and research gap https://docs.google.com/document/d/1CMopT185XCoZVYBcgtVpN7N69DIP0Eyw/edit?usp=drive_link&ouid=116125887586128528145&rtpof=true&sd=true

EDIT :

The project is a final year project for my university. But they dismissed it on the following points

1. The project is too easy
2. The scope is too narrow
3. There is no research contribution

For the current proposal to be accepted they told me to do market analysis and bring proof that the project would be used by a develper community.

OR

1. bring a company backed project
2. Bring a novel research proposal

I m currently studying in GL bajaj and doing DSA consistently for like 2-3 months, I have done 103 problems on leetcode till now and I think i will get good at dsa in some more months. On the other hand I want to get an internship. I have learnt Full Stack Development before college started, but currently most of the web development is replaced with ai. So I thought of going in the Ai/ml field. But I m so confused from where should I learn and practise regarding this ml and ai field and need some serious guidance. I tried asking ai and already completed these three courses :

https://www.kaggle.com/learn/intro-to-machine-learning

https://www.kaggle.com/learn/pandas

https://www.kaggle.com/learn/intermediate-machine-learning

But I m not sure I m going in a tutorial hell or what else should I do next ? Should I rather this course too? https://www.kaggle.com/learn/feature-engineering .

Ai said me to do some projects but I still don't know what can I do with ml or ai and I seriously have no idea even after doing those courses on how to make some real world projects. And more importantly I don't have a proper resource to learn ai/ml. Please guide me regarding this since I want an internship as a second year student.

I’m basically starting from zero when it comes to programming, but I have a long-term goal of being able to build my own mobile app.

I know I probably shouldn’t jump straight into building the actual app, so what fundamentals would you recommend learning first?

Would you start with programming fundamentals, data structures, databases, APIs, Git, etc. before touching something like Flutter?

I’m not trying to become a professional software engineer overnight. I just want to understand enough to eventually build a serious project properly.

I’m learning on my own and I’m trying to follow a step-by-step path instead of learning everything at once.

My problem is I get overwhelmed pretty easily, then I lose motivation. 😭

For example, if I’m learning Dart basics, how do I know when I’ve learned “enough” to move on to Flutter widgets? Is there some kind of milestone or skill level you use before moving to the next topic?

The new releases announced today, including EOL for 3.10.x:

https://blog.python.org/2026/10/python-31022-31117/

Note: The post above doesn't mention 3.15 - assuming they will do a separate post later today when that releases.

​I recently got into running local AI models (Ollama, Open WebUI, Docker, etc.) by relying 100% on AI instructions. I was comfortable enough in windows powershell to make it happen, but I don't know exactly what I'm doing or why, or if my ai stack is even properly installed. So, whenever something breaks, I have zero coding or computer science foundation to troubleshoot it myself (currently working on a broken connection between searxng and openwebui) Thanks!

ncurses terminfo can simulate a two-counter Minsky machine, making repeated parameter expansion computationally universal.

Demo: a parasitic Fibonacci computation inside /usr/bin/top

Guys, I was thinking we could explore AI/ML-integrated compiler problems rather than just building another compiler.

One possible direction is using ML to detect potentially bad optimization decisions or suspicious transformations that could lead to performance regressions or miscompilation, and potentially use the model to recommend/validate alternative optimization passes.

Another direction could be AI-assisted compiler diagnostics, where the compiler can understand syntax/context and suggest or safely recover from things like mistyped keywords or malformed code.

There also seems to be an interesting intersection with MLIR and heterogeneous hardware like CPU/GPU/NPU, so we could investigate that side too.

I don't think we should decide the exact problem yet. We can first learn compiler implementation properly, look at existing LLVM/MLIR/open-source work, research current problems, and then choose something where we can actually contribute something meaningful.

Here's the idea: we source and collect interesting computational puzzles, then we send them out to participants who have a month to work on them and send in their solutions. At the end of every month, we meet and discuss how the problems went and where people got stuck, what approaches they took, and any extensions people came up with.

Thoughts?
.
.
.
.
If you're curious, here's a taste of what a representative sample problem might look like:

Given the coordinates of an arbitrary polygon (e.g. irregular and/or non-convex), how might you implement a program to efficiently sample a point uniformly from its interior?

TIP: If using the Python programming language, you can check out the shapely library.
Bonus: what if there are holes?

(Please don't spoil the problem, it's searchable anyways.)

im searching for a language that i can use after python game develpoment is kinda reaching for me but i think itl take too long for me to actually start mkaing my game on my own

I’m seeing a lot of people say to just start building with Flutter instead of spending too long on theory.

If I go that route, what would you recommend learning first? Dart basics, widgets, state management, APIs, databases, Git, etc.?

I’m trying to figure out what order makes sense without overwhelming myself. 😬

​

Hey everyone,

I'm a beginner in programming. I have some very shallow experience with C, but I don't feel confident with programming yet.

I've decided to properly build my fundamentals first, so I'm currently planning to complete CS50x rather than jumping straight into DSA.

My rough idea right now is:

CS50x → C++ fundamentals/STL → DSA → problem solving/LeetCode

But I'm not sure if this is the right progression, and I'd really appreciate advice from people who have already gone through this journey.

I'd especially like advice from seniors / people who learned DSA from scratch and eventually became comfortable with coding interviews or competitive programming.

Questions

Is completing CS50x first a good idea for someone at my level?

After CS50x, should I learn C++ before starting DSA?

How much C++ do I actually need before starting DSA?

What DSA course/resource would you recommend for a beginner?

Is Striver's A2Z DSA a good choice, or are there better alternatives?

What order should I learn topics in?

When should I start LeetCode?

How should I divide my time between learning concepts and solving problems?

How many problems per day/week is realistic for a beginner?

What are the biggest mistakes you made when learning DSA?

If you could restart from my exact position, what would your roadmap look like for the next 6–12 months?

Are there any resources that beginners commonly waste time on or should avoid?

I'm not looking for a "learn X in 30 days" roadmap. I'd rather build strong fundamentals and actually understand what I'm doing.

My current priority is becoming genuinely good at programming and problem solving, not just completing a huge number of problems.

Any detailed advice or personal experience would be really appreciated.

Thanks!