Does anyone in here have any high school computer science kids? I would like to know what they are teaching these days. I did computer science class back in the 80’s. Teacher taught us BASIC and Pascal.

can't you do really cool trick with the fact that if you were to roll a dice 5 times and all the previous rolls were the number 3 normally with true random ness the 6th roll only gets a 1/6 chance of rolling any number. Is that still true with pseudo randomness ? and if so can you prove it

Basic was a good starting point. I'm not saying go back to the days of the TRS-80 model 1, I am however asking if it would be realistic to reintroduce these types of classes into the curriculum.

Even if it was simply emulation? I'm asking because a lot of modern computer classes assume either c,c++ or python. I don't have a problem with either of those languages but the error system and Trace back could be problematic. There are other options, there's free basic, qb64 etc.

Programming doesn't have to be difficult to get into is what I'm saying. Would this be a good idea or a bad idea?

Hi everyone,

I've been practicing Graphs and Dynamic Programming for the last six months, but the truth is that I still can't solve a single new problem completely on my own.

Everyone says that DSA is all about recognizing patterns, but I feel like I'm not actually learning those patterns. I've watched many tutorials and solved the questions explained in them. However, whenever I face a different problem that's based on the same pattern, I still can't figure out the approach myself.

This makes me wonder: am I just memorizing solutions instead of learning how to think?

Whenever I look at the solution, I understand the logic and why it works. But what's the point if I can only solve the problem after seeing the answer?

My usual process is to struggle with a problem for about an hour. If I still can't make progress, I use an AI tool to understand the solution. The problem is that this happens with almost every question. It makes me wonder how long I'll have to depend on AI before I can solve problems independently.

I'm feeling really frustrated. Has anyone been through the same situation? What strategy helped you develop problem-solving skills so that you could eventually solve questions on your own instead of relying on solutions?

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Hey everyone! 👋 As a final-year CS student specializing in data science, my team and I are looking to build a project. We're a bit light on computer network knowledge (think CCNA level), so we'd love some guidance! If you have expertise in this area, could you tell us if this project idea is feasible, what we should research, and the basic implementation needs?

Our project focuses on building an AI-powered predictive Content Delivery Network (CDN) that improves video streaming efficiency using intelligent networking and machine learning.

We will work on three main components:

AI Forecasting and Processing:

We will develop machine learning models to analyze network traffic data and predict congestion before it happens. This includes using time-series models to forecast bandwidth drops. Additionally, we will integrate AI-based video processing techniques such as super-resolution (using pre-trained models) to restore video quality after compression.

Network Architecture:

We will design and simulate a peer-to-peer (P2P) network where multiple nodes cooperate to deliver video content. The system will dynamically route data through the fastest available paths based on network conditions. We will also compare and optimize transmission protocols (such as TCP vs UDP) to reduce latency and improve performance. Network simulation tools like Mininet or NS3 will be used to test different scenarios.

Platform and User Interface:

We will build a simple video player that streams content through our system. This includes handling user requests, adaptive video quality, and playback. We will also develop a dashboard to monitor key metrics such as bandwidth usage, latency, and system performance, allowing us to demonstrate the effectiveness of our solution.

Overall, the system aims to reduce bandwidth consumption, improve streaming quality under poor network conditions, and provide a scalable solution for modern media delivery

Been learning a bit about basic foundational computer hardware’s interactions with instruction data. Like, machine language instructions.

More specifically, I came across this whole rabbithole about data compression. Theoretically, there shouldn’t be a limit to how much we can compress data; accepting that quality may be lost… etc, etc.. Also at some point it will probably cost more energy to decode super heavy compressed data than is relatively necessary.

Right, so unrelated, a little while back, I was looking into the concept of protein folding and how instructions are encoded into proteins relating to biology.

My question is: hypothetically, theoretically, could we “fold” binary machine language instructions like nature does with proteins? Would it even be practical?

Can anyone provide any resources related?

(If relevant: Kindly, I won’t click links. If it’s a paper, tell me the name and author please.) thanks.

I'm trying to abstract a biological problem into a more general computational problem, as I'm interested in the underlying methodology used in this fields, to ideally translate back to biology.

The core challenge is that I want to modify a system while preserving a desired behaviour in one context, but allowing that behaviour to change in other contexts. The difficulty is that I don't know which internal parts of the system are responsible for preserving the desired behaviour.

A simplified example:

• We have a system (algorithm, function, circuit, program, etc.).
• The system operates within different contexts/environments.
• In Context A, it must produce a desired output.
• In Contexts B, C, D..., it should not produce that output.
• The context may interact with or modify any part of the system.
• We are free to modify the system itself.
• The system can be enormous in complexity, but ideally is optimized for minimimum required complexity that might scale with the number of contexts.
• The contexts can also be enormous in complexity and interact with the system in many ways.
• Some internal components are essential for producing the desired output in Context A.
• Other components are free to change.
• The problem is that we do not know which components are essential and which are not.
• We can only evaluate the system by observing its behaviour in each context.

Are there existing computational methods that tackle this type of problem?

With AI tools getting better every month, a lot of students are using them to build projects much faster.

If you’re majoring in computer science, do you think relying heavily on AI to generate code is a smart way to learn, or does it end up hurting your fundamentals in the long run?

Where would you draw the line between using AI as a productivity tool and becoming too dependent on it?

Hi everyone,

I'm an 18-year-old founder currently researching a startup idea, and before I spend months building it I'd really like to understand whether this solves a real problem or whether I'm completely wrong.

The vision isn't to replace researchers with AI or build another ChatGPT wrapper.

The idea is to build a development platform specifically for computational work (quant finance, scientific computing, optimization, simulation, eventually quantum computing).

Think of it as four pieces working together:

• IDE – where you write your code
• AI Assistant – understands mathematical and computational problems (not just autocomplete)
• Runtime – analyzes the workload, suggests optimizations, and prepares it for execution
• Hardware Layer – executes the workload on the most appropriate hardware (local CPU, GPU, cloud GPU, and eventually quantum hardware)

The goal isn't to hide everything behind AI.

It's the opposite.

I want developers to keep writing normal Python/Qiskit/CUDA-Q/etc., but remove the headache of figuring out:

• Which algorithm should I use?
• Is this workload GPU-friendly?
• Should I run locally or on cloud hardware?
• Is there any advantage to quantum for this specific problem?
• What's the cheapest way to run this?
• Why is my implementation slow?

For example, imagine a quant researcher writing a portfolio optimization algorithm.

Instead of manually benchmarking different hardware and execution strategies, the runtime could say:

>"This is a convex optimization problem. GPU is estimated to be 12× faster than CPU. Quantum offers no advantage for this workload. Estimated cloud cost: $1.87."

Or, for another workload:

>"This problem can be reformulated as a QUBO. A hybrid quantum-classical workflow may reduce execution time."

The developer still has complete control—the platform just provides recommendations and execution options.

My questions

1. Is this solving a problem you actually have?
2. What is the biggest bottleneck in your computational workflow today?
3. Would you trust a runtime to recommend execution strategies if it explained why it made each recommendation?
4. Am I missing something fundamental that makes this a bad idea?
5. If you could wave a magic wand and improve one thing about your current workflow, what would it be?

I'm not looking for validation—I'd honestly prefer someone tells me why this won't work before I spend a year building it.

Any criticism is appreciated.

Im a cs student in quite a top university. I have had experience in AI before chatgpt became popular.

I was actually teaching AI courses. But it seems like now theres so much buzzwords that i feel like im falling behind.

Ie agentic ai, vibecoding or whatever.

Any good, respected material that is extensive for me to catch up?

I know about forward propagation, neural networks. Linear algebra and calculus. But im more interested now with application, perhaps some theory too.

I wanna build an application, with a good infra that has agentic ai to help run. But I feel lost.

Im also tasked to teach AI to middle aged adults, and they have complained that mine is too technical and isnt useful. Its also a non profit, and im teaching other non profits and the middle age adults too who arent that tech savvy but is trying to be.

Please help, human coders

Hello! I am not sure if this is the appropriate subreddit to ask about this, but I just need some advice.

We are currently doing our thesis, where we need to create a system for a real client. I need advice about which technologies, programming languages, etc should we use? Those that are student-friendly but industry-grade, and not so expensive. I also want to try coding without the use of AI. And if you have any further advice about the matter, it would be highly appreciated!

Our system is about a residential subdividision:

\- Centralized resident portal for registration, updating, and management of resident and household information 

\- Online payment system to pay homeowner’s dues and other fees digitally

\- Web-based amenity reservation system with real-time availability for both residents and non-residents. 

\- Announcement system 

\- Digital inventory management module

\- Decision support and analytics feature (we need to integrate the current emerging technologies)

Prompt injection is easy to talk about as “bad text in, bad answer out.”

It gets more interesting when the model can take actions. Then the failure is not just the generated text. It might be a tool call, a permission mistake, or untrusted data changing the goal.

If you were modeling this cleanly, would you treat it more like input validation, confused deputy, capability security, or something else?

This is moreso a question for the computer scientists interested in philosophy, but I've noticed that the Turing test is very philosophically naïve for its time. I do know computers were very evidently in a very archaic state, likewise with parallel fields in philosophy (Norbert Wiener, for example, had introduced cybernetics relatively recently). Despite this, Turing still relies upon questions long destabilized by phenomenologists, semioticians, and linguists decades prior. to this day, questions regarding the Turing test will often revolve around more Cartesian concerns, as if the field of computer science could have its questions answered while philosophically remaining in the 17th Century. I will make it entirely clear that my intentions are not the strawman Turing, perhaps a bit of a summary would suffice.

Hello everyone, CS 1st year undergrad here. My algorithms professor is so bad I don't understand what he teach at all. he gave a time complexity assignment ( some problems are at the end of this post ) this week but I couldn't do it myself. I tried to read few algorithm books but I wasn't able to find a good book or other learning resource to learn analyzing these kind of algorithms. If you guys could recommend a book or some any other study material, It would be helpful. thank you !

1. FOR i FROM 0 TO n-1
       FOR j FROM 0 TO i
           PRINT i, j

2. FOR i FROM 0 TO n-1
       FOR j FROM 0 TO m-1
           FOR k FROM 0 TO p-1
               PRINT i, j, k



3. FOR i FROM 0 TO n-1
       FOR j FROM 0 TO i
           IF (i % 2 == 0)
               PRINT i, j
           ELSE
               FOR k FROM 0 TO j
                   PRINT i, j, k



4. i = 1
   WHILE i < n
       FOR j FROM 0 TO i
           PRINT i, j
       i = i * 2



5. FOR i FROM 1 TO n
       FOR j FROM 1 TO i * i
           IF (j % i == 0)
               FOR k FROM 0 TO j
                   PRINT i, j, k

6. FOR i FROM 0 TO n-1
       k = i
       WHILE k < n
           PRINT i, k
           k = k + i + 1

Why can't quantum computers solve the Halting problem?

And, what would hypercomputation look like.

1. Are LLMs essentially large memorization machines that are trained to learn patterns from massive datasets?
2. Is the math and reasoning they perform just the result of patterns they have picked up during training, which they then use to answer questions?
3. If LLMs are identifying patterns, could they potentially discover patterns that humans have missed?
4. I remember seeing research where an LLM was trained only on data up to around the 1940s, with no access to later discoveries, and was then tested to see whether it could independently rediscover ideas like Einstein’s relativity. Is this a real line of research, and what does it tell us?
5. Could LLMs find meaningful patterns in randomly generated text or data, or would they just impose patterns where none actually exist?
6. Is true randomness possible, or will some kind of pattern always appear when we analyze enough data and Can LLMs help us find that patterns faster.

What is it

Hi, I’m curious about what operating systems people are using and roughly what percentage. I want to make a post and share it in several IT communities. What OS do you use? If possible, please include your distro and version. Thank you!

I'm trying to understand the formating for graphing out transducers and I'm not sure where looping back to existings states is acceptable or not.

The homework example involves converting 3 digit binary numbers to gray code. The model answer given is depicted more like a binary tree that loops back from the end points back to the root. Making use of 6 total states.

Where as the answer I came up with only uses two states and transitions that allow states to loop back to themselves. Is this not acceptable? Since it seems to be the convention with other finite automata.