BTW Maybe I am fully in the wrong here, and I know I am not a computer engineer or a programmer or something, but I do know some basic stuff, so sorry if its seems really basic as a question.
So, basicaly I have this cctv camera, and to use it I need to use its app and I can only do things that are in the app, and this made me wonder: Why can't I make it do whatever I want, especially since its mine and I am admin. For example, like in the movies when hackers displays random images or other stuff. I was thinking that because that camera in reality is what?, just electronical components, motherboards, transistors,resistors etc that passes electricity and a lens in that case. btw my question isnt really about the camera its just an example and its more about every electronical devices. So to go back to the camera, its just electrons passing in a certain ''rythm'' so I should be able to control it like I want no? So I wanted to know can programmers do absolutely what they want with the physical components inside, or they can't also or maybe you would need to like write in binarie to control the on/offs or use a special program to do so?

EDIT: I just wanted to thank everyone that answered here since i got actually way more answers than i though (its my first time) and I get it now so a BIG THANK YOU to everyone

Is there a legitimate reason or is it just because people thought it looked better?

Why are we still pretending "AI" using LLMs or any other model based purely on probability and statistics could ever be anything remotely resembling intelligence? Can we just call it what it is: programmers that are too lazy to come up with a heuristically based solution or executives that are too cheap to invest in a proper solution? The AI pundits are making a preposterous claim that a machine can be intelligent, so the burden of proof should be on them to show it's even possible. Where's the math to show that anything outside of probability and statistics can come out of anything other than probability and statistics? Do people do probability and statistics in their head all the time on large data sets that could never possibly fit into their head at any point in their life, is that intelligence? So doesn't what we do as people in our heads, regardless of how anyone is possibly eventually to describe or understand, have to include something besides probability and statistics? So why, then, aren't we requiring these AI pundits to show us what kinds of concepts can appear mathematically out of thin air using only mathematical concepts used in LLMs?

The "Turing test" is a load of bunk in the first place. Intelligence is not predicated purely on behavior. If you read a book, sit there silently, contemplate on what the author was trying to say, piece it together with the themes and the narratives of the novel, and synthesize those ideas that occur to with other lessons from your own life, isn't that intelligence, even before you speak or communicate so much as an iota of any of those thoughts to anyone? Why, then, does the Turing test, and all artificial "intelligence" so-called academia center around this mode of thought? Where is the academic literature supporting "artificial intelligence" that discusses how this is irrelevant somehow?

And why is it that any conversation with an AI pundit that supposedly knows what they're talking about, if pressed, will retreat to religiously minded thinking? Religiously minded thinking can be great for religions, don't me wrong, but it doesn't belong in academia, where there needs to be room for rhetoric. Why, then, can no AI pundit come up with any better argument than "but you can't prove it's not intelligent". This is the same as saying that you can't prove their religion false - again, fine for religions as they are religions, but this AI crap is supposedly based in academia. More burden of proof for the preposterous and supposedly academic claims that ChatGPT and its ilk are based on, the supposed "artificial intelligence" that can be found, discovered, or created somehow from nothing more than engineering software, based on a pattern of on high and low signals on a wire that semantically form our ones and zeroes rather than the actual electrical impulses that run through our brains in the form of synapse impulses. Where then is the academic literature supporting how our intelligence must surely run on a pattern of simplified response to the electric signals rather than what is actually clearly running through our brains?

My question is more vibe coding oriented if yk what I mean.

Edit: I'm talking about the values of certifications, are they more valued now that mostly anybody can play with AI?

I've noticed that lately when people are talking about display drivers they mean video card drivers, like NVidia Geforce drivers. Yet display drivers still exist, you can see your screen in Windows Device Manager for example. For me it says "Generic PnP Monitor".

So the question is: are there different display drivers in modern world? Or are all monitors standartisized to the point we have the same interface for all of them?

I was thinking about how even a lot of household devices did not have a dedicated instruction for floating-point division, or even integer division, until recently. Could the P solution to an NP-complete problem require an instruction on a chip that is beyond the scope of anything commercially available or even conceived, and can researchers then pose the question: "Why can't NP-complete problems be solved in P without this function?"

Hello, everyone, I am just ending my first year at uni as A computer science student and I want to invest in a laptop as my main machine. My use for the laptop would be of course reading pdfs and the usual when it comes to studying, But also I would want to program the uni's projects but also start mine too. Also because I will choose a career path in Networking and in the fourth year of cyber security I want the laptop to be capable of handling a couple of VMs so I am planning to have a bunch of RAM. The thing is that every laptop I've searched has a Huge problem. For example, The xps13 9310 battery 2-3hours M2 air To expensive in Europe starts at 1500€ the base model The Lenovo laptops don't have enough RAM and the Hp elite book 855 g8 the new Ryzen CPU has a problem when it comes to cooling and fans on Linux because I would like to dual boot the machine. I know that I can't have it all but when you want to spend 1500€ on a laptop I think you can be picky. If anyone can help with any suggestions I would really appreciate it!

My Candidates are

1 Dell XPS 13 9310 i7 1185g7/32gb/1tb SSD

2 HP EliteBook 855 G8 15.6" IPS FHD (Ryzen 7-5800U/32GB/1TB SSD

3 Lenovo IdeaPad Slim 7 Pro 14IHU5 14" IPS Touchscreen (i7-11370H/16GB/1TB SSD

4 Lenovo Yoga 7 14ACN6 14" IPS FHD Touchscreen (Ryzen 7-5800U/16GB/1TB SSD

5 Lenovo ThinkPad E15 Gen 3 (AMD) 15.6" IPS FHD (Ryzen 7-5700U/16GB/1TB SSD

6 Lenovo ThinkPad E14 Gen 3 (AMD) 14" IPS FHD (Ryzen 7-5700U/16GB/1TB

7 Dell Inspiron 5625 16" (Ryzen 7-5825U/32GB/512GB SSD

8 Macbook air m1/m2 16gb/512gb ssd

P.S I spend my whole day in uni with classes and later I study in the library so I want a laptop with a reliable battery and somewhat lightweight so that is why I did not choose a gaming laptop.

I’ve been thinking about the way we frame software development / computer science, and I wonder if our discipline has been mislabeled.

Right now, software engineer is the most common job title, and software engineering is often used as a synonym for the entire discipline of software development. But this framing feels a bit off. In traditional engineering fields (civil, electrical, mechanical), the word “engineering” is grounded in the physical: materials, stress, limits of nature. Software, by contrast, does not face physical constraints; it is logic, symbols, and abstraction.

If we zoom out, programming looks much closer to applied mathematics. Writing software is specifying formal systems, manipulating symbols, and reasoning about correctness. The fact that it executes on machines is almost incidental; the underlying work is mathematical. In that sense, it makes more sense to see software development and computer science as a technical and applied branch of mathematics.

The terms software engineer and software architect then become useful analogies rather than literal mappings to physical engineering or architecture. Much like financial engineering or mathematical engineering, they borrow the prestige and process-implying metaphors of engineering, but they do not imply we are pouring concrete or bending steel. They are metaphors for rigor, system design, and discipline.

This framing seems cleaner to me:

Discipline → Applied mathematics (specialized for computation)

Job titles → Engineers, architects, etc., used as analogies for roles, not definitions of the field

Curious to hear what others think. Does this make more sense than lumping all of software under “engineering”? Or is there a reason the engineering metaphor is still the better fit?

Maximum price of around $1,5K Thanks in advance!

Do you think a CS degree is still a good option in the age of AI? Are there any specific areas of CS that might see a decrease in demand due to automation, or conversely, any areas that might see an increase in demand due to the rise of AI ( Human-Computer Interaction for example) ?

Do you think that a strong understanding of CS principles will become increasingly essential for tech jobs in the future? Or do you believe that new technologies and tools will continue to simplify and automate these aspects of the job?

one fine day i was commenting on reddit and i wrote a couple of punctuation symbols expecting them to be displayed but instead... they were caught by the markdown software. this is bad. this means there are strings i cannot display as i wished. "use a backslash to escape them!". yeah but then what if we want to pass the string to several nested bash-like programs? ever found yourself putting over 9000 backslashes in a command line? how can i empower an editor to have markdown functionalities WITHOUT reserving any punctuation?

a very not human readable idea: we write using only odd string positions and we fill the even positions with _. a ☭ in a even position functions as delimiter (like a double quote) for the beginning of a new formatting mode declaration, which ends when the same mode is declared again by the same pattern (kind of XML tags). example:

hello-world

... becomes...

-h-e-l-l-o--☭i☭t☭a☭l☭i☭c-w-o-r-l-d☭i☭t☭a☭l☭i☭c

it's like a way to attach metadata to a file where needed without reserving escape sequences/characters.

an even less human readable would be to put two space delimited numbers ahead of each string. the first would be the length of the description of the current format mode, the second would be a counter of words before the next format mode change. example:

19 17 one two three ... ok nineteen? one two ... club seventeen

19 53 1 2 3 ... 19 1 ... 53

i don't even know how to call what i'm attempting to so, and surely i don't want to resort writing with lambda calculus like combinators for the sake of minimality.

any idea?

I’m looking for advice from people with serious experience in data compression, information theory, technical diligence, or IP strategy.

I started building a deterministic CPU-based AI architecture a few years ago because mainstream probabilistic models did not give me the guarantees I needed and were too GPU-dependent for my goals. During development, it became clear that part of the architecture had compression implications. That led me into deeper research around information theory, Kolmogorov complexity, the pigeonhole principle, and compression benchmarks.

I believe I have developed a novel compression-related architecture that is not a conventional entropy encoder and not part of the usual LZ/Huffman/arithmetic/ANS/PPM/BWT family. I am intentionally not describing the mechanism, transformation structure, or internal method publicly because I am still working through patent protection and international novelty risk.

The problem is validation.

A public prize like the Hutter Prize would require source disclosure, but the source would expose the core mechanism. That same mechanism is also foundational to a broader deterministic AI system I am building. I do not want to create public prior art against myself or hand the method to larger companies before the IP position is protected.

I am looking for guidance on the safest credible path to private validation.

Specifically:

1. How can a novel compression claim be evaluated privately without public source release?

2. Are there reputable researchers, labs, attorneys, or technical diligence groups that handle this kind of review under NDA?

3. Are there alternatives to public-code prizes for validating compression systems?

4. What should I avoid saying publicly before patents are filed?

5. Are there funding paths specifically for patent protection and private hard-tech validation?

I understand that extraordinary compression claims are usually met with skepticism, and rightly so. I am not asking anyone to accept the claim from a post. I am asking how to get the work reviewed and protected without accidentally disclosing the core invention.

The broader project includes deterministic AI and low-cost information infrastructure, but the immediate proof surface is compression because compression is measurable.

Any serious guidance on IP-safe validation paths would be appreciated.

Hey guys, I'm studying a major in computer science and I looking for a new computer.

I was looking for options and I can't decide between an ultrabook or a gaming laptop. I have to mention that I have like a desktop gaming PC, so I was leaning towards an ultrabook, but I don't know if that's a good choice.

The ultrabook that I had in mind was the Razer Stealth 13. You guys got any opinions or recommendations?

PD. Sorry for the grammar :)

Why isn't Microsoft Windows absolutely perfect yet? They have had enough time to probably rewrite the entire OS a couple of times. How could something like Vista even happen? Why is my Mac so ridiculously easy to use and stable? Is there something fundamentally different about the system architectures (DOS vs UNIX), that makes Windows irredeemably more buggy? Does Microsoft actually delete and rewrite code or do they just keep piling on new code? Why does Mac take up so much fewer hardware resources?

There are apparently around 50 million lines of code for Windows 10 - if that's converted to binary, how many single digits would there be?

I’m in my last semester of CS and I’m trying really hard to figure out the point of the programming language class.

It’s taught from the “modern programming languages “ book.

The first chapters are about “grammar” and further in the book it discusses ML (not machine learning a language called ML).

I had some strange classes in my academic career but none of them did I think they were pointless.

I don’t see a point to any of this. I don’t see a point to studying grammar theory or a language that is not used anywhere in the industry.

Am I being short sighted?

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.

Now I'm no field expert, just a junior dev, but I'm skeptical about the outlook most people have for this field.

Right now companies like Wix take up x% of the market for basic web pages. In the future, could those services not expand to more complex websites or apps? Even if those sites are sub-optimal in performance, won't computers and internet speeds be fast enough that it won't really matter?

I've already experienced being pushed out of a "safe" niche because there were enough developers readily available that the job went to whoever would take the shittiest deal. Do we really expect the future to still be top-dollar-for-top-skill? I can envision a future of /mostly/ bottom-dollar-for-good-enough, especially as "good-enough" gets easier and easier with high level languages and frameworks.

What do you think? Could Web Development crash?

Im just tired of not understanding tutorials of any language other than python, english is not my first language so the accent makes it difficult for me to understand what people say

I have been working as a software dev for a while now and been mainly working in JS. Apart from my work i enjoy doing my own projects. I do it on an old G50-45 Lenovo laptop with latest Ubuntu dual booted with Win10. Currently i am financially stable and can afford a MacBook. I my country MacBook costs almost twice the amount of two wheeler. I can afford it right now and plan on buying it for sole purpose of programming. Should i get it? It it worth the money? I can code right now on my laptop but because of battery problems its more like a desktop than a laptop.

Man I thought I was going to be safe...