I'm asking this honestly, as someone with no computer science background.

I just watched a video called TempleOS in 100 Seconds. Most of the comments recognize Terry Davis' genius, even with his schizophrenia and severe mental health struggles.

How would you explain to an ordinary person how significant what he was able to accomplish really was, especially doing it all on his own?

I've been wondering about how when you toss a coin or roll a die it's supposed to be random, but it's not truly random because of physics and things like that. I was curious whether that same thing applies to a computer. What does it actually do to choose a number?

I mean, this ought to be doable, right? Pixel patterns that the eye can't see but machines can pick up, essentially fingerprinting an image or video as coming from a specific AI model. I'm not really sure how you'd keep those pixel patterns from being reversed with a computer program, but I think this could go a long way toward labeling AI-generated content.

P.S. The Wikipedia page on printer dots, in case anyone isn't familiar with them.

If nobody had ever invented the Turing machine or Lambda Calculus, would engineers have still invented computers as we know them?

I have been looking into how we handle probability estimation for high-cardinality categorical features, and standard implementations still heavily rely on fixed smoothing rules like Laplace or Lidstone. These approaches completely ignore feature cardinality, sample size, and class imbalance, which inevitably introduces a non-vanishing bias when you throw them at modern tabular data with lots of unique categories.

While reading through recent work on this topic (found at arxiv.org/abs/2608.11162v1), the authors propose a hierarchical empirical-Bayes approach for Naive Bayes (HEB-NB). Instead of a fixed concentration parameter, they learn a Dirichlet prior data-adaptively via Type-II maximum likelihood. This lets the model share information across classes while keeping closed-form inference intact, and they even extend it to structural relaxations like average one-dependence estimators (HEB-AODE).

Theoretical vs practical trade-offs

What caught my attention is that they actually establish non-asymptotic l1 error bounds and show solid log-loss reductions (up to 22 percent on certain UCI and OpenML benchmarks) alongside much better calibration metrics. However, looking closely at the formulation, learning the Dirichlet concentration via Type-II maximum likelihood adds optimization overhead during training compared to standard closed-form Laplace smoothing. For extremely wide datasets with millions of sparse features, I am wondering how much of a training bottleneck that empirical Bayes optimization introduces in practice.

Curious if anyone here has run into similar issues when trying to move away from fixed-parameter smoothing in probabilistic classifiers, or if you have benchmarked empirical Bayes overhead against simpler heuristic alternatives on large-scale categorical data?

Hey everyone!

I'm a BCA (Hons.) student, and I have to develop an individual project as part of my course.

I'm currently struggling to find a good project idea, so I thought I'd ask here!

I'm looking for ideas for a website or mobile app that can solve a genuine real-world problem. It could be something that makes people's everyday lives easier, solves an issue in society, or addresses a problem that people don't usually pay much attention to.

I'm open to pretty much any domain – AI/ML, cybersecurity, education, healthcare, transportation, agriculture, data analytics, or anything else.

I don't really want to build another basic attendance system, expense tracker or e-commerce website. I'd love to work on something useful, a little different and interesting to develop.

It doesn't have to be a super complicated idea either. It just needs to solve an actual problem and be something I can realistically build on my own.

If you've ever come across a problem and thought, "There should be an app or website to solve this!", I'd love to hear about it!

Feel free to share your own project ideas, problems you've noticed in everyday life, or even something you wish existed.

Any suggestions would be really appreciated. Thanks! :)

Hope it isnt too much of a weird question. I'm new to this.

Have a nice day :)

These days everywhere you go you only hear "omg AI is gonna replace/kill/save us all!!!" and stuff like that. But when I talk to the people who actually do computer science they dont seems all that amazed by AI. They actually seem rather annoyed with all the hype about it. So whats going on?

Obviously its a good tool that can do a lot of things, but is it really all that great or is it just a lot of hype for just another good invention?

Keep in mind that I am a layman when it comes to computer science (a bit of experience with R is all), but feel free to get technical with me.

Edit: to be more specific about my question: What do you consider to be the greatest strengths and weaknesses of AI? What is it really great at and what might be problems with it (except power and water usage)?

Hey everyone

I'm a Computer Science student right now, and I've been wondering a lot about how much of what I really use comes from outside of college.

Don't get me wrong, the theory side is strong (algorithms, data structures, and so on), and I genuinely see why it matters.

But for practical skills like:

* putting together real projects
* using current stacks
* handling bugs in production
* getting how things work in practice

…nearly everything I've picked up has come from:

* personal side projects
* stuff I found online
* simply working it out as I go

It often feels like college shows you how things ought to work
while the real world shows you how things really fall apart.

Is a lack of a comment citing a relatively cliched bit of code considered plagiarism in the big world? What are the attitudes to autocomplete? Is Alberta Tech right about linked lists?

Hi!

If number of operations are more than CPU threads and not completely divisible by number of threads available on CPU, should I split remaining operations into already created threads or creating a new thread just for remaining operations is completely fine?

Does one method give an advantage over the other?

Thanks!

Hey folks,

Building an isolated chat microservice consumed by other services in our ecosystem. Auth/users are handled externally by a dedicated Identity service, so our service only handles private topics/rooms, participant access, message history, and an embeddable <iframe> widget.

Stuck choosing between two stacks:

1. Node.js + Prisma + Socket.io

   • Pros: Native room management (socket.join, io.to().emit), simple single-language codebase for both backend and the iframe widget.

   • Cons: Generating interactive API docs for other teams isn't as seamless out of the box.

2. Python + FastAPI + SQLAlchemy

   • Pros: Automatic OpenAPI/Swagger docs (/docs) out of the box makes sharing API contracts effortless.

   • Cons: Bare-bones WebSockets mean building a custom ConnectionManager for rooms/disconnects, plus writing JS for the iframe anyway.

Is rolling custom WebSocket room logic in FastAPI a pain compared to Socket.io's battle-tested rooms, or is FastAPI’s clean OpenAPI generation worth the plumbing?

Would love to hear how you'd approach this!

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,

I am a sci-fi writer who's doing a near future novel so I'm trying to keep it accurate. The scenario is the government wants to preserve a section of servers in a very powerful AI, but a computer virus is spreading through the code as a 'No one can have it' failsafe.

The idea is that if they cryogenically freeze the servers, they will have more time for computer engineers to counter the virus at a better setting. In my head, they'd avoid unplugging it because they really want the AI and don't want to damage it with a hard shutdown like that.

I'm basing this off of MythBusters freezing a bomb, so it doesn't explode. In that though, it froze the chemical reaction in the battery, don't know if it actually froze the wires. The first test they did was on just the C4 and wire. It delayed it but the explosion still went off under a second.

Does freezing electronics stop electricity from flowing through?

Is this just completely ridiculous? Does unplugging it just do all of this without issue?

May be more of a /nostupidquestions ask, so I'll really appreciate the in-depth answers!

Thank you!

P.S. I put the parentheses in the title because my browser kept defining freezing in the technological sense not the physical.

I just started learning in computer science and could not understand these three, I know unicode is for describing(?) languages other than english but how is it differ from UTF-8 and how UTF-8 differ from ASCII ?

For context, Discord is going to get a rewrite in Rust: https://www.techspot.com/news/114462-discord-rebuilding-desktop-app-rust-fix-memory-problem.html
This is apparently ment to reduce working memory consumption.

The question essencially boils down to: Can't Electron not just... not do that? Like, is it dammed to always consume multiple gigabyte worth of RAM? And whats the difference between it and "Native" code? (I know that Java isnt native code, but thats about it)

I am more of a hobbist than anything else to be honest, but I have some experience with java and even made some small discord bots in it. Dont be affraid to go into deep details with this, I enjoy that very much.

My book has this concept titled "Trace Data retrieval", I'm confused on what exactly they mean, how does it really connect to our data structure, as in how does it make our work easier?
Cannot upload images so here's the exact text:
2.7. Trace data retrieval in Array, List and Queue:
Tracing data retrieval means following the step-by-step path of how an algorithm finds or accesses data.
Rather than focusing solely on the final result, attention is given to the entire process, including how the data is stored, how it moves within the structure, and the steps involved in accessing or removing elements from data structures such as arrays, linked lists, or queues. This approach provides a clearer understanding of the operations performed.
Even though the Array, Linked List, and Queue data structures are all linear, the effort needed to find an item can be very different. It depends on how the data is stored and organized internally; some allow quick access, while others may require checking items one by one. Tracing makes it easier.
-----
It then goes onto showing a trace table for an array by implementing a basic for loop.

Anonymous (first-time) artifact reviewer for a conference here. I've been contacted by Alexander Chevalier, a PhD student from France, for asking me about a feedback about a paper called "Hyde: Search-Based Hypertesting of Observational Determinism in Java Programs" (https://alexander-chevalier.github.io/paper_final_alexander.pdf ). Does anybody know what does observational determinism refer here? I mean, I know the whole logic behind tracing but I dunno about testing.

Example given: a valid test might be correct even if we do not keep the observational determinism. Observational determinism is a security property, rather than a functional correctness property. How can this be applied for testing?!

The paper can be found at: https://alexander-chevalier.github.io/paper_final_alexander.pdf

i get that 0.1 is a repeating fraction in binary, like 1/3 is in decimal, so it has to get rounded. what i don't get is why the rounding errors feel so random. 0.1 + 0.2 gives 0.30000000000000004 but 0.1 + 0.3 gives exactly 0.4, and i can't see the pattern.

also is there a number system or representation where 0.1 is exact? i know about decimal types and storing everything as integer cents, but is there something that handles it in hardware, or is it always a software workaround that's way slower?

Hello,

i have a simple issue - some xml input, some xml output, and need to generate a program that takes the input and results in output. Was hoping there are preexisting thtorials. I know this idea isn’t new; my issue is ive never done this particular task before.

Hopefully it’s non-fiction but not necessarily needed because fiction can inspire also! Especially like you know the iPad was invented in Star Trek. The Next Generation.

Potentially a stupid question, but I’ve had a lecture about CIDR notation and subnet masks and while I kind of understand how you chose a subnet mask and the amount of hosts, I don’t understand how you choose an IP address.

I was told not to use 192.168.x.x throughout and then shown a few examples using 10.x.x.x and 172.x.x.x but how do I decide which IP to use for each subnet within a network and why are these 3 IP values the most common?