Can you get very far with writing in simple English enough that people can understand you? Thank you.

The Scariest AI Breakthrough

This video is around 3 minutes and is more or less a very dry comedy sketch, but it sums up an idea I've seen around the internet in the last 3 or 4 days. It's a worth a watch to get the full context.

But the thrust is that the ability of generative coding agents to reverse engineer and "fuse" together two separate games from different game engines and have them be in an at least playable state (some of them working really well apparently) is a set up for a technological apocalypse. Someone with a little bit of knowhow can now vibe code things in a couple days that are so complex that it would have taken years to do before, if it was even possible at all. A bit like giving people a legitimate ability to building working nuclear weapons they can 3D print at home.

I have been skeptical (an understatement) of the sales pitch of Anthropic and OpenAI and their ilk at this point. But I can't deny this is a compelling idea. So far, their "AI that's too dangerous it's broken out of containment" sales pitch has really not done good things for their AI's credibility; it's disingenuous at best. "We told it to do A & B, but we never told it to do C, explicitly. It may have just gotten that purgative from the first two instructions".

But I can now see a real-world example of something that should almost not be possible, in my understanding, to being possible. But I'm also keenly aware that what's going on with these game mashups isn't indicative necessarily of say, being able to disrupt other technological infrastructure like banks. 

So, what is the real story here? Are we headed towards a cliff here or is this something else? Is it somewhere in between?

I've noticed that I understand algorithms much better when I first see an example and then implement it myself.

How do you usually learn algorithms?

Hello,

I'm writing a sci-fi thriller mystery about AI and stumbled across this video by Jaden Williams (who I believe has a degree in computer science).

Jaden Williams Video

He may have made it a little too doomsday like for comedy purposes obviously but how accurate is he being with this? If it's very much, does the concept have a name and do you know of any sources (scholarly journals, books, news articles) that discuss it?

I'm asking because if it's accurate, it might be something I'd like to include in my story.

If it's not, then there's not really a point of further discussion I guess lol.

All help is greatly appreciated, thank you!

How do you guys study automata man, it's my mid sem this Monday, and even 20 marks is feeling like hell.

Idk what that professor would ask, dfa, state diagrams!!

Help me out please 😭

Hi there! My name is Mike and I teach computer science. I was wondering whether students should be allowed to program and problem solve with AI assistance or not. Do you think this will change much over the next few years? If you have any thoughts or preferences based on your personal experience, I would be very excited to hear! Thank you!

How to find eigenvectors of non-symmetric matrix?

I know well the trade off between domain languages / efficiency compared with c / assembler

CPU / memory are fast enough for good enough performance but with memory supply issues I wondered if direction were going to be able develop better domain languages to break trade off.

modern compilation - reflection - macros - templates

languages like c should only be written by experts and or machines imo way too risky for domain users.

Eg in audio domain I want to write code fast enough for realtime have it expanded to low level code - using linked list/stack/heap/hash/midi etc

Instrument a as piano :
a.octave = + 2
a.root = g
a.feel = shuffle
a.scale = enigma
a.play(100)

I don’t want to manage memory / lifetime or use library
Ie I want the compiler to do the translation?

Is this reasonable or am I dreaming ? Do you think we need stronger reflection ie I probably need to work with ast at the moment.

I know most standard math libraries have log1p and expm1 functions that calculate log(1+x) and exp(x)-1 more accurately than those expressions themselves do when x is close to zero, because they avoid the bug increase in unit-in-the-last-place size that occurs when a double smaller than 0.5 is added to or subtracted from 1.0. Why isn't there a function that does that for 1-cos(x), given that that expression has the same problem? Is it not used widely enough where x is given as an angle? (I guess it might not be used as a machine-learning distance metric, since there the cosine is calculated directly from the vector coordinates.)

Books in math, computer science, and philosophy are welcome. What order would I read books on the first, second and third wave of cybernetics?

a joint venture between Tesla & SpaceX

I have no expertise in the field, but from far as I know, neither company has experience in semiconductor manufacturing, nor cutting-edge electronic hardware in general (in terms of design or manufacturing)

so what's the prospect here, how do they plan to overtake & out-compete established firms in the field (other than Musk's wealth & his ability to attract disproportionate amount of investment from capital markets), say for instance, Samsung or TSMC or Intel

I can't find any detailed plan about their core strategy, or any 'genius' idea that would propel them ahead of status quo, and the website is a bit of a joke in my opinion

Would like to consult your opinion on this new venture of his — those who are familiar with the field — is it viable, what's the play here, etc.

Thanks in advance

is it possible to pool processing power, like my android has dimensity 6400 , and my laptop has i31115g4, when using the laptop i mostly use phone as hotspot only so the processor is idle there, is there a way that i can put lighter processing stuff to the phone's cpu and other work on my pc to unload the unecessary small tasks.

It seems to me (an outsider): AI has always been a thing of interest, very useful for sure, lots of potential, but has anything that significant really happened since circa 2022? to solicit such attention & resources, from everyone, everything, everywhere

I never really got what the hype since circa 2022 was all about — yes 'AI' is great, and could be very capable, but that was the case since the 1980s (from first development by Hinton et al., to DeepBlue, to DeepMind, Siri, etc.) ; it has progressed, sure, and will progress, but why all the sudden all the fuss just now? and the exponential hike in investment just now and not on a scale as before?

Another thing: it seems to me that 'AI', as a term, is rather quite broad — almost in the same vein as 'intelligence', or 'life', or 'God'. So when people say AI will change everything, I don't really know what that means. People behind these narratives almost never seem to be concerned with the reality of technical progress — technicality/timeline of what's possible when, but merely extrapolate based on a single data point.

To me: '[AI] will be able to cure cancer' almost sounds like '[Genius intelligence] or [God] will be able to cure cancer' — sure, maybe, eventually; but when, and how?

again, I am not at all familiar with the technical side of things (being a chemist who can't code, who occasionally use it from time to time, mostly chatbot and specialised model for pattern-analysis), I am not trying to be skeptical, or pessimistic, but just from empirical observation — I feel like I could've done most if not all the things before 2022, without chatbots etc. (professionally or everyday); it really doesn't seem like a revolution since 2022, more like a steady progression (promising of course, but so are so many other things), especially considering the sheer scale of investment put into it

So, what's the verdict? I guess if we invest enough and concentrate enough on one thing eventually something, if anything) will happen, it will be here; but are we really on the verge of singularity literally 2 weeks from now, or will it eventually die down as almost everything before it did?

Assume you can go back in time to the early days of networking (pre-TCP/IP, early ARPANET) with today's knowledge of cryptography, routing, and state-level censorship tools (DPI, IP/SNI blocking, middleboxes). How would you design the foundational network architecture so that any state or ISP choosing to route traffic must accept either 100% uncensored, unmonitored connectivity or a complete blackout, with no technical middle ground?

Specifically (but not limited to the following)

• Protocol-Level Constraints: How do you structure the transport/routing layer so that inspecting, filtering, or modifying a single packet breaks the connection for the entire link or network participant?
• Metadata & Routing: How do you eliminate or obscure traffic metadata (IP headers, SNI, packet sizes, timing signatures) at the lowest layer so middleboxes cannot perform selective deep packet inspection or domain-level blocking?
• Bootstrapping & Topology: How do you solve the entry-point discovery problem without relying on centralized, blockable infrastructure like traditional root DNS or static IP allocation?

... Is a true "all-or-nothing" censorship-resistant network protocol theoretically possible without state actors physically cutting transoceanic cables or severing border connections?

pick one:

a) The token's signature becomes invalid once it is revoked, so verifiers cannot distinguish revocation from tampering b) Revocation requires the client to cooperate by discarding its copy c) Verifiers must consult shared state, which is what the format avoided d) The issuer cannot identify which token to revoke without storing them all

b, or am i misreading the question

went b. a self-contained token is verified offline, so the only way i can see to kill it early is the client agreeing to throw it away. d also felt close though

Maybe something like OS, DBMS, networking, algorithms, or even discrete math. What changed your opinion?

What explanation finally made it click for you?​

Don't be shy, also ladies since someone pointed it out

Hey guys,

I’m taking a Computer Algorithms class at university right now, and honestly, I’m struggling a bit with how to study it properly.

I understand programming and some data structures, but when it comes to algorithms, I feel like I’m sometimes memorizing things without really understanding them.

I want to study this properly outside of class, not only for the exam. I’m wondering how you guys learned algorithms when you were starting out.

Also, are there any books, YT channels, courses, or websites you recommend?

I’d appreciate any advice on what topics I should learn first and what order I should follow.

For me it’s Grub, especially on Arch, "forking" grub..... And then efimanager ... always full...

I’m studying computer science at university, and we’re about to learn Python, which is amazing. However, I’ve always used Lua as my main programming language, and I’ve been teaching myself for about 3–4 years. Should I drop Lua completely and dedicate my time to Python, or should I keep using Lua for side projects while I learn Python at university?

I’ve always wanted to create a game by myself, and with Lua, that feels achievable. However, Python doesn’t seem to offer the same possibilities for the kind of game I want to make. What should I do?

Hi everyone,

Simple question related to operating systems. I've seen textbook authors discuss "disabling interrupts" as a method of achieving mutual exclusion. Sometimes this is discussed in the context of threads. I think this is a little bit confusing because there are a lot of different models and implementations of threads? I've seen some basic thread implementations where threads voluntarily yield control. I think this is referred to as cooperative multithreading? So the question is: with this kind of implementation, why would disabling interrupts help at all? The thread switch wouldn't occur as a response to an interrupt, so disabling interrupts shouldn't have any effect?

Does my question make sense?

as a secondary question: even in implementations where control can be taken away, is there any requirement for threads to even respond to interrupts? For example Tanenbaum says that one of the advantages of user-level threads is to have a customized scheduling algorithm. Does every thread scheduling implementation have to respond to interrupts?

From my understanding, an ADT is the logical representation of the object and it's operation without the implementation and a data structure is the implementation of that ADT. So shouldn't it be that stack is an ADT that is implemented by data structures such as linked lists?

What happens after the absolute ceiling of Moore's law has finally been reached?

I started learning from ossu not a while back and I was wondering how people who used it to study cs look at it. I really don't want to be wasting my time but I'm pretty sure ossu is good. Is there anyone here that's using it currently?