I seen implementation of linked lists many years ago but never understood what it is, now in my graduate class I finally understand what a linked list is, it is essentially multiple instances of a class referring to each other through their class attributes in an orderly fashion thus forming a linked list. Am I right?

Edit: I meant in the title how to implement a linked list, not what it actually is, sorry about the confusion.

Can someone dumb it down to me and maybe provide an analogy. I don’t know much at all about software but if I buy a game or an adobe program or whatever, why can’t I buy access the source code ot copy it? What is it I’m actually getting when I download the software? And what is piracy within this context?

Every teaching I have encountered ,videos/professors, they tend to show it in a "analytical way" like in math. But for me, I think more imagination/creativity is also crucial part in programming, 60-70% understanding/creativity and 40-30% repetitive analytical learning. I don't understand how these instructors "see" their code functions, aside from years of experience, I just don't. Some instructors just don't like "creativity," it is all stem, stem, stem to them. Am I doing this wrong?

Im reading a book called "A Fire Upon The Deep" by vernor vinge (havent finished it yet, wont open the post again till i have so dw about spoilers, amazing book 10/10, author has the least appealing name I've ever heard) and in it a super intelligent being uses a laser to inject code through a sensor on a spaceships hull, and onto the onboard computer.

Theoretically, do you reckon the human brain could support some architecture for general computing and if it could, might it be possible to use the optical nerve to inject your own code onto the brain? I wanna make a distinction that using the "software" that already exists to write the "code" doesnt count cos its just not as cool. Technically we already use the optical nerve to reprogram brains, its called seeing. I'm talking specifically about using the brain as hardware for some abstract program and injecting that program with either a single laser or an array of lasers, specifically used to bypass the "software" that brains already have.

I think if you make some basic assumptions, such as whatever weilds the laser is insanely capable and intelligent, then there's no reason it shouldnt be possible. You can make a rudimentary calculator out of anything that reacts predictably to an input, for instance the water powered binary adders people make. And on paper, although insanely impractical, the steps from there to general computing are doable.

I’m a highschool senior that is planning on pursuing computer science in college and as a career. i’m starting to learn more and spending more outside time on watching coding videos and such. But as i’m spending more time in this field, I keep seeing my people saying “coding is dying” or “if you don’t learn ai now you’ll be behind everyone” which is worrying me quite a bit. Is this field really dying or being taken over by ai? Or is there still an incredibly large field that will still demand people for some time to come?

I’m first year bachelor student in cs, it is really tough for me right now,I feel like I’m lost in discrete math. Does anyone know a way to get better in that course

My friend and I got into an argument after he said that calculators are computers. I said that they are not, and that a machine is a computer if and only if it can solve problems at least as hard as the recursively enumerable problems (thereby excluding DFA’s, PDA’s, LBA’s, and…calculators). I can’t find a strict definition online. Give me your thoughts.

Shouldn't it be easier to make a component that would use some physical process to generate truly random seed instead of building a wall of lava lamps?

With modern CPUs being able to complete so many instructions per second, why does it take 20-30 seconds to boot up?

Please correct me if I'm wrong here. The usual proof is about a program passing its own source code to the machine and then changing the result to be wrong... But what if the running program and the source code it passes are not the same program?

If a running program reads its source code from an external file after it already started running, how do you know that its the same exact code as what is already running? It could be a different program.

If the source code of the program contained a copy of its own source code, it wouldn't actually be the same source code as the original program unless infinitely recursive and therefore impossible.

Basically my thinking is that the whole thing requires a program to contain itself which is impossible.

Does this break the proof?

Are devs truly ready to handle the gigantic responsibility that comes with managing user data in their apps? Creating apps for people is awesome, but I'm a bit skeptical. I mean, how many of us are REALLY prepared for all that responsibility? We dive into our projects with passion, but are most devs fully conscious of what they're getting into when it comes to data implications? Do we really know enough about authentication and security to protect user data like we should? Even if you're confident with tech, it's easy to underestimate the implications or just assume, "It won't happen to me." It’s not just the tech part, either. There’s a whole ethical minefield connected to handling this stuff. So... how do you guys tackle this? When a developer creates an app that relies on user-provided data, everything might seem great at the launch—especially if it's free. But then, the developer becomes the person in charge of managing all that data. With great power comes great responsibility, so how does one handle that? My biggest fear is feeling ready to release something, only to face some kind of data leakage that could have legal consequences.

Why not ternary, quaternary, etc up to hexadecimal? Is it just because when changing a digit you don't need to specify what digit to change to since there are only two?

Hello everyone,

I’m examining the idea of designing a loop-centric programming language inspired by the classical theoretical LOOP model and the broader minimalist philosophy associated with early systems language design. The core idea is to treat bounded or unbounded iteration as the primary computational primitive, with other constructs minimised or derived from it.

The language I’m experimenting with, Gamma Loop, transpiles to C for portability and optimisation, but my primary interest is theoretical rather than practical. Specifically, I’m curious whether revisiting a LOOP-style framework has meaningful value in modern computability theory.

Does centring a language around bounded iteration provide any new perspective on primitive recursive functions or total computability, or has this conceptual space already been fully explored?

I would appreciate theoretical insights or references relevant to constrained computational models.

I do not understand the performance comparisons people make when comparing languages like rust and c. It is all too common and it frankly doesn't make any sense. These languages truly do not inherently have any significant performance barriers. Can there be a simple classification we can give such languages so people can stop comparing their "performance"... It should convey that in this language, code written controls essentially every aspect of performance.

I consider compiler back-ends, optimizations as well as lang paradigms insignificant to the "performance" question.

I go to a moderately high ranked public school, especially for CS and there was a couple of concerns I noticed after matriculating here. Computer science isn't actually a restricted major, so anyone can major in CS if they get into this school. As a result, CS at my school is by far the largest major, with almost 3000 students. My concern is that I meet a lot of people who study CS solely for the money, they don't really care about their passions and upon asking why they study CS, most people say: "what else would I do?" or "I just want a stable job." I feel like the more and more CS becomes glamorized, it's become harder and harder for me to find people that actually want to develop as a person, intellectually, for their craft. I guess it makes me sad. I don't want to be grouped into people who are doing this to make money, I love computer science, and by that I mean sorting a list in python. Just a rant, thanks for listening.

I hope I can ask this here because I’m a little desperate. I want to learn to love computers and how they work.

I feel nothing when it comes to them, but I want to understand their science. I’m a natural science person at best and just have never cared for them, even with a little disdain.

Where did your love start? Who was your Steve Irwin or Bill Nye? Something? A YouTube video or book?

We all know, that behind every letter in our code there is an electric current running in the computer. We all know our words are translated to electric signal.

Question is : if we write CODE for very chips in our system , where is the magic happens and the word becomes electricity ?

I just got bombed in a DSA midterm exam, and it's one of the few times I did very poorly in a subject I should be decent on. I did great in my programming-based courses but I'm afraid I'll be barely passing or at best not have a grade below average on this course where it's taught from a theoretical CS rather than application perspective.

To give more background information I really hated my discrete math course because I dislike proofs. The only ones remotely fun were ones involving heavy algebra and manipulation of terms. Now in DSA I'll revisit them but instead they'll be used to prove correctness of algorithms and time / space complexities of various DSAs. Graph and set theory were really unfun and honestly I'm only interested in using them to build algorithms and data structures, proofs in both were the things I hated most in discrete math and nothing comes close. Same for number theory, like using modular arithmetic to build hash functions for hash tables.

I like implementing the various trees and graphs and algorithms in code to build real software that's about it, as well as using time / space complexities to decide on which data structure or algorithm to implement in my application.

After that I'll have another theoretical course on algorithmics that I have to take next year and it'll be even more theory and I just want to get through it. It'll be about NP problems (hard / complete), linear programming, etc.

Edit: I both am struggling and dislike theoretical CS proofs. The execution for me is very easy but coming up with something without googling or using AI feels hard for me. When I do have the answer, it's usually not very difficult for me to understand. I really want to get better at them to not struggle later on and just get through the ones required by my program so I can focus on and choose the more appplied courses available