So I'm in the second year of my CS degree and doing DSA, now we have to understand Tower Of Hanoi, I studied the game, studied the trace diagrams, looked up multiple tutorials for it, I get what the program but I don't at the same time. I get how it arrives at the minimum but I think if you gave me a problem to solve via recursion I'd be stumped, I understand how this specific of recursion works but I don't know how I would...make it? If you asked me to create a Hanoi algorithm or any recrusive algorithm recursively I won't be able to but at the same time I understand somewhat how they work?

I'm kinda smart for a lot of things and thought AI was one of these thing but apparently it evolved too fast and I lost the line of understanding it almost automatically and now am a bit lost on the AI train.

So here it goes: what exactly is a model? is a LLM a type of AI or one of the stuff AI can do? what exactly is a token? how can AI run locally if it needs those tokens? what is an agent/how does it works? what is a harness? why the agent needs a harness?

I've been researching this subjects but only came across stuff that looks too advanced for my level to understand.

Thanks :)

i get the decision tree argument for why any comparison sort needs log2(n!) comparisons, which is about n log n. but then counting or radix sort does it in linear time and people just say it's cheating because it isn't comparing.

what i can't figure out is whether that's a real escape or just hiding the cost somewhere. like radix sort is O(d*n) but d is basically log of the max value, so for n distinct keys isn't it back to n log n anyway?

PhDs don't necessarily correlate to intelligence but it is used for a short hand for being highly knowledgeable. Humans with Phds, didn't need terabytes or even hundreds of gigabytes to get their degrees even if it is one field, so why does AI need that much data? Humans with PhDs don't need to read millions of books and they manage just fine. Even simpler models from a few years ago needed a lot data for their capabilities relative to humans.

I got a question wrong in an exam that asked which algorithmic search has a time complexity equal to O(b^m) and two of the options were Depth First Search and Breadth First search. I foolishly picked BFS when I can see clearly DFS is a correct answer. But I'm not sure my logic for picking BFS is actually wrong in this instance. Yes BFS has a time complexity of O(b^d) in all cases where d is the shallowest goal node but in the worst case scenario for BFS wouldn't d=m by necessity? As BFS checks level by level the worst case scenario for BFS is when d=m? So it would have a time complexity O(b^m) = O(b^d) where the worst case specifically is proportional to the total depth of the tree?

Hamacher's "Computer Organisation" book introduces the concept of distributed arbitration when talking of which DMA controller would currently have access to the bus. It uses a 4-bit circuit and demonstrates the system with two devices A and B with ID 5 (0101) and ID 6 (0110). The arbitration code is a bitwise OR of all device IDs (0111 at this point). The arbitration lines are all default LOW (they assert zero when no line is driving it HIGH)

Ideally, we start from the MSB of both IDs and whenever a bit from a device disagrees with the arbitration code, it stops driving that bit and all bits after that. In our case, Device A would become 01XX and the arbitration code becomes 0110 and therefore ID 6 wins the race.

My issue is about synchronization. Do we NEED some sort of synchronization method(where each device checks a specific bit at the same time) to guarantee correctness of this circuit? I have been trying to look for edge cases when differences in synchronisation lead to a wrong device winning the race.

For example, in out previous example. if device B checked its bits before device A had a chance, device B would update its signal to 011X. This makes the arbitration code become 0111. Then device A looks at this arbitration code and updates its signals to 01XX. This changes the arbitration signal to 0110, and ID 6 wins the race. How does one go about proving correctness regardless of synchronisation when generalising this to some N number of devices and some B number of ID bits?

I do not have much experience in electronics, but as a more general question, is there a way to tell when a system (hardware especially) needs synchronisation in general. For software, it feels very intuitive, we assume no guarantees about order of execution and use locks whenever needed, but hardware feels a lot more trial-and-error.

I bought my current laptop about 2 years ago, around when LLMs were getting major press for the first time. A big thing in the marketing of the laptops I was looking at was that the intel CPUs had "AI integration". I didn't think much of it at the time, but i've since taken a computer organization course, and it's led me to think back on this phrasing.

It seems to me, from what I know about CPU design, that LLMs are too complex of algorithms to be integrated into something like branch prediction, and perceptrons and neural nets in control flow prediction aren't a new technology by any means so it would be odd to advertise that as some kind of new "AI". I'm wondering if it could refer to some kind of coprocessor designed to more efficiently do math relevant to LLMs, or if there really is some kind of new predictive architecture intel is utilizing in their chips?

I also wouldn't be surprised if it's all just marketing to cache in on "AI" as the new hot buzzword.

What is the difference between decomposition by business capabilities and decomposition by subdomain?
i mean what is domain in this? i am unable to differentiate between these two techniques (by business capabilities vs by subdomain)

I have a Computer Networks course this semester and I am finding it difficult to understand. It is not that the concepts are difficult, I am just finding it hard to make a mental model of where every single thing fits. it is not like i am failing the course. Till here i have been able to handle the course. but what is the point of getting good grades when you don't even understand where each thing fits ?
From the very bottom layer to the top level, where DNS comes (where it is stored), how IP's even works, UDP, TCP, QUIC, tables, DNS chain, masks, subnets or even IP's. I know what they are, but i don't know where they are and how they interact.

Can anyone please suggest some resources, more like an animated video where i can visualize it ? Cisco Packet tracer didn't help much.

every intro course says dict lookup is O(1) average, but you have to hash the key first and hashing a string means going through every character, so isn't it really O(length of key)?

is the O(1) just assuming keys are a fixed size, or is there some other reason we ignore that. kinda confused how people are comfortable calling it constant

I'm reading the pkware documentation on the ZIP file format and I get how file data is structured, but I don't see where it says how the ZIP file saves directories.

Edit: Nvm I'm stupid. A file's relative path is stored in the file name section of the file header.

I own a domain-specific dataset, and I depend on horizontal teams to deliver platform capabilities like search, alerts, notifications, dashboards, and whatnot.

The data itself is structured data that we're buying from a vendor, and the integration work is something I own. It's a pretty large dataset, about 150 million records.

I need to work with our horizontal search team to make this dataset searchable. My responsibility is to provide the requirements, define the user experience, write user stories, and work with the search PM and their tech lead to discuss technical trade-offs.

Before I go into these meetings, I'm trying to figure out what I should know or put together beforehand. More specifically, what search-related technical knowledge should I gain so that I can have meaningful discussions with the team and understand the trade-offs they're making?

I know I can ask ChatGPT o rClaude to help me learn these concepts, but I'd also like to hear from search PMs or other PMs who have worked with similar dependencies.

How did you get up to speed on search and its underlying systems? What topics or concepts, like precision, recall, indexing, ranking, etc., should I familiarize myself with before engaging with the horizontal search team?

I read once that Xerox was working on a way to do that, but I don't know how much that was just hype.

I just saw a headline titled “Anthropic warns self-replicating Al 'worms' could spread across smart devices by end of 2026”. I read a bit more into it and I don’t understand why they won’t just stop? Like if they’re the one creating it then why don’t they stop?

I know programming, obviously, however I don't know much about the operating system. Most people can make apps within an environment, but most of us don't know anything beyond that

I want to deeply understand how the computer I'm using works, even just the software part

If you go on a virtual tour of your computer, like browsing the file system, or even looking at the plain old desktop, there's so many endless things in front of you, that most people have no idea how to make

How do I make a Windows desktop app? Why does it run on Windows? Same for MacOS

How do tab sizes work?

What is the desktop "screen" and why can I click and drag my mouse to highlight the icons? Where are those icons stored?

I can code a website, but I don't know a clue about how the browser works

How do I make a file system? I mean, isn't it just a large data structure, and I know data structures, so I'm sure I can learn it

Why does my task bar pop up when I drag my mouse all the way to the bottom of the screen?

What does it mean to turn on a computer?

Why / how does my physical mouse control my cursor? Is there a way I can "hack" my mouse? How do I code the firmware in my mouse?

How does the OS accept and eject USBs?

How do you allow your OS to connect to wifi?

What really is a .txt, .pdf, .rar .zip? Why can some OS's open them, some cannot?

Stuff like that

Can most of you point to any part of the screen and know how it works?

I found this flyer in a stairwell where I live. It has binary on it and a date, but every translator I’ve tried to enter it into has just given me “Can you r.” I assume it says something like “Can you read this,” but I don’t know. Here’s what I could transcribe and the flyer: 01000011 01100001 01101110 00100000 01111001 01101111 01110101 00100000 01110010

I was studying data structures and algorithms and I wondered, If we have asymptotically positive functions f(n) and g(n) and increasing asymptotically positive function h(n) such that f(n)=O(g(n)) is it safe to assume h(f(n))=O(h(g(n))) ?

Is it always the case or it just works with some specific functions?

another question I have is if we have f(n)=O(h(g(n))) can we assume f(n)=h(O(g(n))) ?

I'm studying DBMS and I'm confused about the difference between partitioning and sharding.

From what I understand, partitioning means dividing a large table into smaller parts. For example, splitting a table of 100k rows into 50k, 30k and 20k rows.

Now, if I store these three parts on three different servers, is it called sharding or just partitioning? Since partitioning isn't necessarily restricted to one server, the terms seem to overlap.

Also, if sharding is a type of partitioning, why is sharding defined as dividing data into smaller parts again? Wouldn't the data already be divided during partitioning, with sharding simply distributing those parts across multiple servers?

Basically, is partitioning the general concept and sharding the same concept specifically used in distributed systems? Or am I missing something?

While reviewing some nonparametric manifold learning methods for high-dimensional feature spaces, I came across a recent paper (http://arxiv.org/abs/2608.05112v1) that re-examines the convergence properties of the Subspace Constrained Mean Shift (SCMS) algorithm.

For a long time, the common assumption in the literature has been that SCMS trajectories converge directly to the classical "static ridge" defined by the density gradient and the eigenvectors of the density's Hessian matrix. However, this paper demonstrates that this assumption doesn't hold in general because the static definition completely ignores the rotation of the trailing eigenspace along the continuous flow of the underlying vector field.

To address this gap, the authors introduce a dynamical systems perspective, defining a new structure called the "stable ridge" using the Jacobian of the projected density gradient. They prove that this stable ridge is actually the true theoretical convergence target of SCMS, rather than the traditional static definition.

They also propose a generalized framework using a constant step size, which achieves uniform R-linear convergence, and show that the original formulation suffers from polynomial-time computational bottlenecks due to implicitly coupling the step size to the smoothing bandwidth via the Mean Shift operator.

That said, one limitation I noticed while reading through the proofs is that the analysis assumes certain smoothness conditions on the underlying density estimator that might be difficult to verify or guarantee in practice with sparse, noisy, high-dimensional empirical datasets.

Curious if anyone here has run into similar convergence or subspace rotation issues when applying mean shift or gradient-based ridge estimation techniques to complex real-world manifolds?

Parallelism i understand that Performing multiple tasks parallely at the same time

but what exactly asynchronous and concurrency means?

Overall unemployment rate of recent graduates is also 5.7%

Source: NY Fed recent labor market outcomes by major and unemployment.

I understand that deleting a file usually just removes its pointer or metadata, leaving the actual data there until it gets overwritten. But since the pointer is just data too, why can't the computer just wipe the actual file right away?

​

Which one is the best from your perspective? For me, those options are somehow resistant in this AI context.

Here it is: https://doi.org/10.1109/ASSIC70037.2026.11717448

I don't have access to the paper because of the paywall, and this isn't my paper. I was just searching all over the internet for papers about P=NP and came across this one. What caught my attention the most was that it was published at a peer-reviewed IEEE conference.

Might be the wrong subreddit, but I wanted to start a discussion about a possible revival in lower-spec PCs now that AI is cannibalizing modern consumer computing.

The joy of PCs for me is offline creative/coding software where the latest and greatest specs aren't important, and I'm wondering how feasible it would be to go back in time to specs that are low-end enough that no supply chain disruption could mess up the price of entry. Would we really have to go all the way back to music trackers and pixel art with something like Commodore, or is there a happy in-between from somewhere in the 2000s or 2010s where we can keep music DAWs or even Blender and video editing? (Like would making PCs that are 4 GB of RAM still be eventually too expensive? 2 GB? 1 GB? PC makers right now are raising their prices instead of cutting back on specs)

Obviously the world hasn't already ran out of used modern computers on eBay that work fine, but let's say those do run out. How much downgrading can we get out of making new consumer PCs with less specs and more efficient software instead of relying on the current norms like 16 GB RAM or 512 GB of storage?

I don't really have an answer I'm looking for here, I'm just wondering if anyone else has been thinking about this too. I refuse to live in a world where ALL computing is solely done on someone else's servers. I just want the joys of offline PCs to keep living. Thanks!

EDIT: General consensus seems to be that existing hardware that's at the level of DDR3 RAM and below won't be running out anytime soon. Which is good because people also seem to agree there is probably no substitute to the existing supply chain without going back to specs and computer parts as low-tech as the 80s (if most of the tooling from that era even still exists)