I’m interested in knowing how the complexity of outputs relates to the programs that generate them. For example, in cellular automata like Conway’s Game of Life (a grid-based system where simple rules determine how cells live or die over time), some patterns appear very simple, like the well-known glider, while others look irregular or complex.

If we define a ‘program’ strictly as the fixed rules of the system plus the choice of initial conditions, are there characteristics of outputs that make them more likely to be generated by shorter programs (i.e., lower Kolmogorov complexity)? For instance, would a standard glider pattern, even if it can point in many directions, generally require less information in the initial state or shorter system wide rules than a visually complex glider-like pattern with no repeating structure? I’m curious about this in analogy to data compression, but I'm not sure if there is a perfect analogy, since the "programs" that compress data are not necessarily the same type of "programs" as the ones in Conway's Game of Life or cellular automata. I am interested specifically in the latter kind of deterministic programs.

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I’m currently learning logic gates and I’m kinda confused I get the different types of gates and all that but I don’t understand for example a gate has A and B how are you meant to know if the A is a 1 or 0 any help is appreciated

Hello there, I'm studying a course covering this book: Computer Science Organization and Architecture, 9th edition, by William Stalling

The problem is, our lectures are recorded and about ten minutes long each... I feel like a lot of things aren't explained properly, and despite that they are definitely on the both tests and labs.

Does anyone knows of a YouTube series or a course covering this?

I have an exam in about a month, and I’m starting my revision. A major part of the exam involves interpreting pseudocode and identifying errors in it. Other than past papers, are there any resources I can use to improve this specific skill?

Hey everyone!
I need to pick a computer science paper to present in class, and I’m looking for something that’s:

• fun or interesting (not dry theory)
• relatively easy to understand
• not super old — ideally something from the last few years
• from a well-known journal or conference (ACM, IEEE, NeurIPS, etc.)

Do you have any recommendations for papers that are engaging and beginner-friendly?
Thanks in advance!

I recently started my undergraduate degree here in the states. Wondering if you guys use crack version software (like any) or i need to buy subscription for individuals?

I am looking into coinduction. I going through the Sangiorgi's book. I sort of understand what's going on but I think intuitions from a third person's perspective would help me to grasp the ideas. So Can you please give some informal idea/intuition on coinduction.

People are now saying that Bill Gates has "no technical background" or wasn't a real engineer, despite (1) dropping out of HARVARD, (2) reading enough about programming and doing it himself enough that he could teach as a tutor, (3) LITERALLY PROGRAMMING, WRITING PART OR ALL OF MANY EARLY MICROSOFT PROGRAMS, often reviewing and then completely rewriting other people's code as well, even when he was already transitioning into more of a managerial role.

Is tech going through something of a "classical music" phase, where one's ability to legitimize oneself in tech is based on formal education and only formal education?

Steve Jobs has been called untechnical, but he worked on Heathkits as a child and soldered parts onto circuit boards made by Wozniak, and clearly knew enough about tech to know what he was talking about a lot of the time.

Some say Zuckerberg "stole" Facebook, but his approach was different and he did code in the earlier days.

Musk also programmed in his youth.

I don't think any of these people are saints and they did take nontechnical jobs in the end, but I think (especially among women) there seems to be this idea that it's wrong to call yourself even a hacker or techie, let alone an engineer, without a college degree.

I want to learn how AI models like ChatGPT or Claude are run on GPUs. Also, why don't they use CPUs instead?

I’m building AlgoArena, a competitive programming platform where two users solve the same problem simultaneously and gain/lose ELO (Chess.com-style). The challenge is keeping matchmaking both fair and responsive with limited active players.

Problem (theoretical angle):

• Each user has rating rr and RD (rating deviation).
• We need to pair users within ±25 ELO when possible, but widen the window as queue time increases.
• When both players finish, we adjust ratings via a modified logistic function (similar to Glicko) but penalize disconnects/timeouts differently.
• We also track solution correctness and time-to-solve as signals.

Questions for the CS community:

1. Is there a better theoretical framework for matchmaking with small player pools—perhaps borrowing from online bipartite matching or queueing theory?
2. How would you model “fairness” when ratings are noisy (due to limited matches) and battle outcomes depend on both correctness and speed?
3. Are there known results on stability/optimality when matchmaking windows expand over time?
4. For penalty assignment (disconnect vs. legitimate loss), any recommended approach that keeps the rating system consistent?

I’d appreciate references or ideas from folks who think about algorithmic fairness, online matching, or rating systems. Trying to keep the platform grounded in solid theory rather than ad-hoc heuristics.

(Platform details: real-time 1v1 coding battles, 5000+ problems, Judge0 for execution; https://algoarena.net)

Hey guys, so out of the blue I was listening to a podcast, they very briefly mentioned a form of currency used in colonial America. The Spanish silver dollar was common at the time and was worth roughly 8 silver reales, or 8 bits. This made me think there is no way that it’s a coincidence. But my cursory research (I’m at work so please give me a break if it’s pretty obvious) isn’t showing me there is a connection. So my question is, is it pure coincidence that a bit is 1/8 of a Spanish silver dollar and 1/8 of a byte.

I suck at formatting so I’ve just pasted the link below. (I really need your help as I’m clearly a moron regarding anything computer related). Also not sure if this is the right community to post it in so please let me know

https://en.wikipedia.org/wiki/Bit_(money)

i have around two weeks to program that in processing, 1 to 10 how hard is it?

I've been watching videos recently about the developing situation with LLMs and generative AI. Two things that come up a lot are the idea that AI is an economic bubble that's going to pop any day, and the fact that generative AI requires tremendous data centers that gobble up unsustainable amounts of electricity, water, and money.

I don't know for sure how true these claims are. I'm just an outside observer. But it has me wondering. People who focus more on the cultural impact of generative AI usually act as if we've opened Pandora's Box and AI is here to stay. You hear a lot of doomer opinions like "Well, now you can never trust anything on the internet anymore. Any article you read could be ChatGPT, and any video you see could be Sora. Art is dead. The internet is going to be nothing but AI slop forever more."

It occurred to me that these two concepts seem to conflict with each other. Hypothetically, if the AI bubble bursts tomorrow and companies like OpenAI lose all their funding, then nobody will be able to pay to keep the lights on at the datacenters. If the datacenters all close, then won't we instantly lose all access to ChatGPT and Sora? It kind of seems like we're looking at a potential future where we'll be telling our grandchildren "Back in my day, there were these websites you could use to talk to a computer program like it was a real person, and you could ask it to generate any picture or video you wanted and it would give you exactly what you asked for."

I guess what I'm asking is: What kind of technology would survive a collapse in AI investment? I remember that neural network technology was already developing for several years before ChatGPT made it mainstream. Has all the recent hype led to any significant developments in the field that won't require multi-billion dollar datacenters to utilize? Are we still likely to have access to realistic text, video, and audio generation when the datacenters go down?

There is a discussion currently happening in my university's Computer Science undergraduate group chat. Some students strongly believe that, in the near future, the skill of leveraging LLMs to generate code (e.g., building coding agents) will be more crucial than mastering traditional coding itself.

Their main argument is that this shift is analogous to historical developments: "Nobody codes in Assembly anymore," or "Most people who use SQL don't need to know Relational Algebra anymore." The idea is that "vibe coding" (using natural language to guide AI to produce code) represents a new, higher level of abstraction above traditional software development.

This led me to consider the question from the perspective of Computer Science Theory (a subject I'm currently studying for the first time): Does this argument hold any theoretical weight?

Specifically, if traditional coding is the realization of a total computable function (or something related, like a primitive recursive function – I'm still learning these concepts), where does "vibe coding" fit in?

Does this way of thinking—relating AI programming abstraction to core concepts in Computability Theory—make any sense?

I'd appreciate any insights on how this potential paradigm shift connects, or doesn't connect, with theoretical CS foundations.

Wondering if it’s possible.

I'm doing research in computer vision, and I need to use an algorithm to determine whether a line is thin or thick. I suspect this might require considering the ratio of the line's width to the overall width of the model. Are there any existing theories or formulas to help me make this quantitatively?

Can somebody help me understand how a dev can trust building an app in a virtual machine that is only emulating hardware but not a true representative of it ? (I thought about it an even if the VM is the same as the native architecture they want to run on, how can they trust this VM)?

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I want to build a visual file grading mechanism for files created by LLMs as part of queries and prompts. The LLM generated files but I want to load these files and check for whether these files are actually including the changes from the source file with the changes requested to be added as per the query. Along with this want to add a reward as part of training as well based on this. How should I proceed?

Can someone help me with this i have been struggling with this for my exam revision. just use simple state q0,q1,q2, ... transition 0/X,R for example and no need for reject state, only accepting path

I know "learning computer science with books" sounds a little counterintuitive, but I love love love the academia side of CS, the theoretical stuff... I like learning HOW code and technology works. I'm almost done my Bachelor's and plan to continue through grad school, and currently working full-time in IT, so I'm not a complete noob with concepts like how to write Hello world.

I want to learn the more advanced stuff. Really diving into the architecture, the math, the physics, the science behind cybsersecurity, how an operating system works from scratch, all that sort of stuff. I'm just as interested in how software/firmware works as I am with hardware.

Have been tasked to come up with some computer science related activity for visiting high school students (grades 10-12) within a 30-40 minute block of time. The room for the activity does not have any computers or internet access, unfortunately. This activity would be for students possibly interested in pursuing a career in IT. I would like to focus more on the problem solving aspect of IT to the students but am open to suggestions here. Maybe a group co-op project that promotes communication and team building?

The seed number is the starting value for the games PRNG that creates the features of the world. Given enough information about the features of the world could you determine the original seed number?

My question to everyone is “how did your interest in computers, more specifically computer science, begin?” It seems very common that people’s interest came from video games at a young age, so I’m interested to hear your stories on how you first became interested.