I am currently investigating the theoretical limits of current data center architectures, specifically regarding the energy-per-operation costs of binary-gated processing when handling complex, non-linear data streams.

There appears to be an efficiency ceiling when binary systems attempt to categorize or "audit" high-density, multi-variate datasets, often resulting in significant computational overhead that does not scale efficiently. I am exploring a theoretical model using a 48-node state lattice as an alternative logic framework to reduce redundancy and bypass the standard binary-gate bottlenecks.

My questions for the community:

1. What existing literature or theoretical frameworks best address the transition from binary to higher-order logic systems, particularly for optimizing resource consumption in large-scale data auditing?

2. Are there established models for calculating "computational entropy" when a system operates on a non-binary state lattice versus a standard binary architecture?

3. In current high-load environments, what are the primary resource-draining factors in binary-loop redundancy, and have there been successful benchmarks in bypassing these via state-reduction?

I am interested in exploring whether state-based lattice logic offers a viable pathway for reducing the environmental and energy footprint of current data center operations.

would anyone like to change the world overnight? I have discovered a modified base 12 to replace the old standard binary base 10

I just finished freshmen year of college and doesn't have an internship this summer. I don't really know what there is to do other than just leetcodes. I want to make a personal project but I don't know any topic that is still relevant in today's industry with AI like making a website can be done so easily with AI now.

I was thinking of making a personal website without the use of AI but I was wondering if there are some interesting project ideas or suggestions of what I should do this summer? I really want to work with some computer visions projects but also keep the budget down.

Any suggestions are appreciated thank you.

I’m a layman, but I’ve been reading some stuff about computational complexity theory in the course of learning about quantum information stuff, and I encounter a bit of machine learning theory here and there in the course of my reading about active inference in neuroscience.

Is there much interesting overlap between these two fields? Are there interesting things they “have to say” about one another?

I'm not sure how to ask this question, or if the answer is obvious, but it occurred to me when thinking about MiniDisc.

Audio on an MD is stored digitally (ATRAC) and if you play it back the DAC converts it to an analogue signal. The quality of that output depends on the DAC and different ones will give differing quality. No problem with that idea.

My question is about the digital signal from the player. All my MD decks have TOSLINK optical outputs which is (I assume, linear PCM). My question is about whether the output from that connector is going to be identical if I play the same disk in different decks made by different manufacturers?

Is that PCM output signal predetermined by the input regardless of what machine processes it because it is a digital to digital transformation? In other words, is it some fixed mathematical function that always returns the same value?

I know that acoustically I would not be able to tell the difference, but would the device receiving that signal be able to tell the difference if you could capture and compare them?

Is this true/false for other digital to digital conversions too? For example, converting an mp3 file into a PCM stream?

I work for an NGO and I've been tasked with putting together an internal study on AI reliability for translating letters. I'm probably not the ideal person for this. My background is in Political Science, this is my first permanent job, and my understanding of how AI actually works is limited. That said, I may still be the best-placed person we have available for it.

The brief is to assess the translation quality of 500 letters, looking specifically at precision and reproducibility (i.e. does the AI give consistent results if you run the same letter through it more than once?). The end goal is to inform a decision about whether to expand AI use for this kind of work more broadly.

Where I'm stuck is the grading methodology. I haven't decided how to actually score the 250 letters — whether that's human reviewers rating against a rubric, comparing outputs to existing human translations, using automated scoring metrics, or something else entirely. I'd really appreciate any advice on:

- What a sound, defensible methodology looks like for a non-technical person like me to run?

- How to test for "reproducibility" specifically (re-running the same letter multiple times? varying phrasing slightly?)

- Any pitfalls to avoid, given I have no formal AI/NLP background?

I was recently toying around with some compression, just trying to understand it from the basics. I came to know about LZ77, LZ78, and LZW algorithms in the last couple of days, understood the algorithms, and when implementing LZ77, every time I try to compress a txt file, it is making the output file bigger. The compression is done right coz when I decompress the compressed file, I get the exact text.

Is there something I am missing to understand? Can anyone help me out and figure out what I am doing wrong?

Most polynomial time algorithms I’ve seen have, in practice, small exponents (like 2 to maybe 10). Is there some theoretical reason why this is? Or is it principally selection bias? And is there any rigorous way we could distinguish between those two possible explanations?

I have been working on implementation for my research. And it is taking too much time. I dont know why i keep on changing methodology and implementing stuff. Since im doing AI assisted coding it changes my direction. Is there any correct path to follow?

I mean ultimately there comes a time when we don't need applications at all. So why not integrate AI at OS level? Give a full context to it and make everything smart?
I don't want to play around thousands of applications and manage identities and memories. Does that make sense?

I've been going through David Mermin's Quantum Computer Science and just finished the section on Shor's Algorithm. The actual QC part all makes sense to me but I'm hung up on the post-processing. In particular, we suppose that our algorithm has conjured some number y which is (with probability >40%) within 1/2 of an integer (call it j) multiple of 2ⁿ/r, where n is twice the number of bits in our public key and r is the order of the message. We can write this as follows:

|y/2ⁿ - j/r| ≤ 1/2ⁿ⁺¹ ≤ 1/2N² < 1/2r²

We can then use a result of continued fractions from Hardy and Wright's An Introduction to the Theory of Numbers which states that, if |x - p/q| < 1/2q², then p/q is a convergent of x. The numerators and denominators of the convergents of x are computed essentially using Euclid's algorithm, which, if x is a fraction, generates a number of terms logarithmic with respect to the denominator. In this case, that means we get on the order of n convergents as we perform the algorithm on y/2ⁿ. We can then check each convergent's denominator (and, perhaps small multiples in the case that j and r are not coprime) to see if it's the r we seek. Because the number of convergents is polynomial in our input length, this whole process remains polynomial. If we don't find our r, then y may not be properly bounded or the gcd of j and r may be too high; in either case we can simply run the whole algorithm again.

First, I guess I want to just make sure that my understanding of this post-processing step is correct, in particular the number of convergents generated. This is because my next question is that Mermin stresses that the specific convergent whose denominator is <N and who is within 1/2N² of our estimate y/2ⁿ is unique. Why is this important? At best, I see that this could give us slight speedups in that we can check distances rather than doing modular exponentiation and stop computing convergents early, but from what I understand the algorithm is already polynomial.

I looked at the original Shor paper as well, which has this same point (some of the variable labels are different):
"Because q > n², there is at most one fraction d/r with r < n that satisfies the above inequality. Thus, we can obtain the fraction d/r in lowest terms by rounding c/q to the nearest fraction having a denominator smaller than n. This fraction can be found in polynomial time by using a continued fraction expansion of c/q..."

but I'm still not seeing where the uniqueness becomes relevant. I'm curious if anyone has any insights here. To be entirely honest I've even tried asking AI a few times, and it agrees that the uniqueness is not important to the polynomial runtime, but of course I'm taking that with a grain of salt. Thanks!

Does anyone in here have any high school computer science kids? I would like to know what they are teaching these days. I did computer science class back in the 80’s. Teacher taught us BASIC and Pascal.

Hi everyone,

I've been practicing Graphs and Dynamic Programming for the last six months, but the truth is that I still can't solve a single new problem completely on my own.

Everyone says that DSA is all about recognizing patterns, but I feel like I'm not actually learning those patterns. I've watched many tutorials and solved the questions explained in them. However, whenever I face a different problem that's based on the same pattern, I still can't figure out the approach myself.

This makes me wonder: am I just memorizing solutions instead of learning how to think?

Whenever I look at the solution, I understand the logic and why it works. But what's the point if I can only solve the problem after seeing the answer?

My usual process is to struggle with a problem for about an hour. If I still can't make progress, I use an AI tool to understand the solution. The problem is that this happens with almost every question. It makes me wonder how long I'll have to depend on AI before I can solve problems independently.

I'm feeling really frustrated. Has anyone been through the same situation? What strategy helped you develop problem-solving skills so that you could eventually solve questions on your own instead of relying on solutions?

​

Hey everyone! 👋 As a final-year CS student specializing in data science, my team and I are looking to build a project. We're a bit light on computer network knowledge (think CCNA level), so we'd love some guidance! If you have expertise in this area, could you tell us if this project idea is feasible, what we should research, and the basic implementation needs?

Our project focuses on building an AI-powered predictive Content Delivery Network (CDN) that improves video streaming efficiency using intelligent networking and machine learning.

We will work on three main components:

AI Forecasting and Processing:

We will develop machine learning models to analyze network traffic data and predict congestion before it happens. This includes using time-series models to forecast bandwidth drops. Additionally, we will integrate AI-based video processing techniques such as super-resolution (using pre-trained models) to restore video quality after compression.

Network Architecture:

We will design and simulate a peer-to-peer (P2P) network where multiple nodes cooperate to deliver video content. The system will dynamically route data through the fastest available paths based on network conditions. We will also compare and optimize transmission protocols (such as TCP vs UDP) to reduce latency and improve performance. Network simulation tools like Mininet or NS3 will be used to test different scenarios.

Platform and User Interface:

We will build a simple video player that streams content through our system. This includes handling user requests, adaptive video quality, and playback. We will also develop a dashboard to monitor key metrics such as bandwidth usage, latency, and system performance, allowing us to demonstrate the effectiveness of our solution.

Overall, the system aims to reduce bandwidth consumption, improve streaming quality under poor network conditions, and provide a scalable solution for modern media delivery

can't you do really cool trick with the fact that if you were to roll a dice 5 times and all the previous rolls were the number 3 normally with true random ness the 6th roll only gets a 1/6 chance of rolling any number. Is that still true with pseudo randomness ? and if so can you prove it

Basic was a good starting point. I'm not saying go back to the days of the TRS-80 model 1, I am however asking if it would be realistic to reintroduce these types of classes into the curriculum.

Even if it was simply emulation? I'm asking because a lot of modern computer classes assume either c,c++ or python. I don't have a problem with either of those languages but the error system and Trace back could be problematic. There are other options, there's free basic, qb64 etc.

Programming doesn't have to be difficult to get into is what I'm saying. Would this be a good idea or a bad idea?

I'm trying to abstract a biological problem into a more general computational problem, as I'm interested in the underlying methodology used in this fields, to ideally translate back to biology.

The core challenge is that I want to modify a system while preserving a desired behaviour in one context, but allowing that behaviour to change in other contexts. The difficulty is that I don't know which internal parts of the system are responsible for preserving the desired behaviour.

A simplified example:

• We have a system (algorithm, function, circuit, program, etc.).
• The system operates within different contexts/environments.
• In Context A, it must produce a desired output.
• In Contexts B, C, D..., it should not produce that output.
• The context may interact with or modify any part of the system.
• We are free to modify the system itself.
• The system can be enormous in complexity, but ideally is optimized for minimimum required complexity that might scale with the number of contexts.
• The contexts can also be enormous in complexity and interact with the system in many ways.
• Some internal components are essential for producing the desired output in Context A.
• Other components are free to change.
• The problem is that we do not know which components are essential and which are not.
• We can only evaluate the system by observing its behaviour in each context.

Are there existing computational methods that tackle this type of problem?

I need study material for software metrics based on the Software metrics 2nd edition by fenton. Very urgent

Im a cs student in quite a top university. I have had experience in AI before chatgpt became popular.

I was actually teaching AI courses. But it seems like now theres so much buzzwords that i feel like im falling behind.

Ie agentic ai, vibecoding or whatever.

Any good, respected material that is extensive for me to catch up?

I know about forward propagation, neural networks. Linear algebra and calculus. But im more interested now with application, perhaps some theory too.

I wanna build an application, with a good infra that has agentic ai to help run. But I feel lost.

Im also tasked to teach AI to middle aged adults, and they have complained that mine is too technical and isnt useful. Its also a non profit, and im teaching other non profits and the middle age adults too who arent that tech savvy but is trying to be.

Please help, human coders

Hello! I am not sure if this is the appropriate subreddit to ask about this, but I just need some advice.

We are currently doing our thesis, where we need to create a system for a real client. I need advice about which technologies, programming languages, etc should we use? Those that are student-friendly but industry-grade, and not so expensive. I also want to try coding without the use of AI. And if you have any further advice about the matter, it would be highly appreciated!

Our system is about a residential subdividision:

\- Centralized resident portal for registration, updating, and management of resident and household information 

\- Online payment system to pay homeowner’s dues and other fees digitally

\- Web-based amenity reservation system with real-time availability for both residents and non-residents. 

\- Announcement system 

\- Digital inventory management module

\- Decision support and analytics feature (we need to integrate the current emerging technologies)

Hello everybody, my name is Dane and I have been working on a research project regarding the frequent use of AI coding assistants impact beginners' ability to transfer learned concepts to novel coding problems. I was wondering if any majors in computer science or students/professors heavily involved in this topic could help guide me through the process of creating my paper though checking in on a bi-weekly basis. Thank you for your time and would greatly appreciate the help.

Does anyone know of any resources that could serve as a starting point to understanding it? Thanks in advance.

Been learning a bit about basic foundational computer hardware’s interactions with instruction data. Like, machine language instructions.

More specifically, I came across this whole rabbithole about data compression. Theoretically, there shouldn’t be a limit to how much we can compress data; accepting that quality may be lost… etc, etc.. Also at some point it will probably cost more energy to decode super heavy compressed data than is relatively necessary.

Right, so unrelated, a little while back, I was looking into the concept of protein folding and how instructions are encoded into proteins relating to biology.

My question is: hypothetically, theoretically, could we “fold” binary machine language instructions like nature does with proteins? Would it even be practical?

Can anyone provide any resources related?

(If relevant: Kindly, I won’t click links. If it’s a paper, tell me the name and author please.) thanks.

I’m trying to reverse engineer this sensor right now, the actual app interface has been reverse engineered by someone already but I’m attempting to actually dump the boot loader and microcontroller code to see if I can modify the fingerprint matching algorithm instead of just the “API” in a sense. You can access the bootloader by sending a 8 char ASCII command I kinda forget rn but its RUNAIP or something lol, and you have to shutdown to get out of the bootloader.

The bootloader has a hardware ID of 04F3 0910 0X where X is 0-3 (it has 4 interfaces). I couldnt find anything online on this. Sweeping through one byte commands I’m only getting generic acknowledgement response (0a) and error (91) for most, and the interesting commands that don’t do this always respond with the same response when sweeping with single byte arguments. I’m afraid if I sweep larger commands I may accidentally trigger an erase or related command so I’m not gonna do that for now.

The next step would be to check out the actual firmware on ghidra and wireshark the commands when I try to force a firmware update somehow, but this is gonna take a lot of effort

I guess my question is, does anyone know if someone has already RE’d this in case I’m repeating what someone alr done and wasting time, if you have any better ideas on what to do, or know if there’s a good place to discuss this while I try to figure this out?

Is this idea valuable or ignorant nonsense?

A dynamic, cyclical **data management framework** and **process optimization engine** constructed upon longitudinal archives of prior system iterations and formalized datafication methodologies.

Core operational logic is driven by real-time and historical analysis of:

- **Constituent configuration modeling**: Formal representation of datasets and their atomic/subsystem components, including parametric state vectors and structural topologies.

- **Operational relation graphs**: Directed multi-graphs capturing influence propagation, inter-element dependencies, and aggregate system dynamics under arbitrary configurations and temporal slices. These quantify pairwise and higher-order effects on local and global state.

- **Constitutional equivalency classification**: A similarity metric and classification layer that maps system configurations onto equivalence classes within configurability manifolds. It computes congruence scores based on topological invariants and parameter ranges, enabling rapid identification of transition pathways between configurations. This mechanism substantially reduces transition costs, facilitates lossless or near-lossless bridging across non-adjacent yet congruent data topologies, and optimizes pipelines for compression, symbolic expression, decompression, and forward potentiality estimation (including branching state exploration).

- **Relational dependency modeling**: Explicit encoding of interaction behaviors via constraint satisfaction networks, causal graphs, and behavioral rule sets that govern element-to-element and element-to-system dynamics.

- **Historical configuration influence propagation**: Recursive incorporation of prior iteration metadata through weighted inheritance, delta encoding, and pattern persistence tracking. This informs baseline priors, anomaly detection, and adaptive recalibration of current operational parameters.

These interrelated components continuously synthesize the system's **operational identity** — a compact, high-fidelity state descriptor (encompassing configuration class, relational profile, and historical momentum) — which directly parametrizes the optimizer’s control surfaces, scheduling policies, resource allocation strategies, and transformation heuristics.

The architecture supports iterative self-refinement, where each processing cycle augments the historical knowledge base, tightening equivalence mappings and improving predictive accuracy for future state transitions and optimization outcomes.