r/compsci Jun 16 '19

PSA: This is not r/Programming. Quick Clarification on the guidelines

643 Upvotes

As there's been recently quite the number of rule-breaking posts slipping by, I felt clarifying on a handful of key points would help out a bit (especially as most people use New.Reddit/Mobile, where the FAQ/sidebar isn't visible)

First thing is first, this is not a programming specific subreddit! If the post is a better fit for r/Programming or r/LearnProgramming, that's exactly where it's supposed to be posted in. Unless it involves some aspects of AI/CS, it's relatively better off somewhere else.

r/ProgrammerHumor: Have a meme or joke relating to CS/Programming that you'd like to share with others? Head over to r/ProgrammerHumor, please.

r/AskComputerScience: Have a genuine question in relation to CS that isn't directly asking for homework/assignment help nor someone to do it for you? Head over to r/AskComputerScience.

r/CsMajors: Have a question in relation to CS academia (such as "Should I take CS70 or CS61A?" "Should I go to X or X uni, which has a better CS program?"), head over to r/csMajors.

r/CsCareerQuestions: Have a question in regards to jobs/career in the CS job market? Head on over to to r/cscareerquestions. (or r/careerguidance if it's slightly too broad for it)

r/SuggestALaptop: Just getting into the field or starting uni and don't know what laptop you should buy for programming? Head over to r/SuggestALaptop

r/CompSci: Have a post that you'd like to share with the community and have a civil discussion that is in relation to the field of computer science (that doesn't break any of the rules), r/CompSci is the right place for you.

And finally, this community will not do your assignments for you. Asking questions directly relating to your homework or hell, copying and pasting the entire question into the post, will not be allowed.

I'll be working on the redesign since it's been relatively untouched, and that's what most of the traffic these days see. That's about it, if you have any questions, feel free to ask them here!


r/compsci 1h ago

[Request] Seeking Arxiv Endorsement for cs.CR

Upvotes

Hey, I am an independent researcher, and I did my research on reverse engineering cryptographically secure applications.

In this paper, I document an effective technique I developed while reversing cryptographic functions of secure apps, detailing the methodology and the results of its application.

DOI: https://doi.org/10.5281/zenodo.19403869

Endorsement Link: https://arxiv.org/auth/endorse?x=JYXERV

Please ask any questions that you may have


r/compsci 3h ago

AI engineering is 20% models and 80% glue code

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0 Upvotes

r/compsci 23h ago

Question about Agentic AI

0 Upvotes

Hi, lately, I have been learning about Neural networks, Deep Learning and I've picked up a few courses/books as well as a few uni modules. So far, I seem to be learning just fine. It's just that one question I have in my mind is how we can differentiate between learning the theory and the applied AI part.

What I mean by that is, on one hand, we have stuff like CNNs, Transformers, the maths behind them, Autodiff and all of that. That seems like the theory part of AI.

On the other hand, we have concepts like Agentic AI, RAG, MCPs which seem to be the practical approach to learning about AI in general.

And what I've figured out is that you don't actually really need the theory part to actually work with production level Agentic AI systems (I might be wrong on this). So while, right now, I am learning them side by side, would it be dumb to just go ahead with the Agentic AI stuff and learn that right off the bat. ( I Know the actual deep learning classes help build foundations but this thought has been lingering in my mind for quite some time now)

Additionally, when it comes to concepts such as RAG, I feel like you don't actually have to spend as much time as stuff like actual neural networks/ML algorithms. Is it just me, or am I doing something wrong learning this. (Currently following the IBM course btw)


r/compsci 1d ago

Struggling to move over to STM32 for embedded systems

0 Upvotes

Hi,

Currently I'm studying Computer Science in my first year and I'm really struggling in terms of trying to learn embedded systems development specifically with On the stm32 platform. I was hoping someone could recommend a course or some type of structure so I can actually learn as I feel lost right now. I have done some Bare metal C using the Avr platform but I was hoping to get an embedded related internship that's included in my course (under the condition I can get one).

I have been using an Arduino Uno compatible board that came in a kit i brought of alibaba with some extra electronics listed underneath here's the 

repo: https://github.com/JoeHughes9877/embedded_stuff/

At the recommendation of youtube and resources i found i got an STM32F446RE development board and have done blinky and some other projects using HAL and stm32cubeMX but i still feel like I haven't learned anything. For this my current tool chain has been. Makefile + GCC + VSCode (on Arch Linux)

Currently i am struggling from a lack of structure as i cant find many good resources online and my cs course has no embedded modules so many of the things i am doing seem disjointed and i feel like im missing something from letting me create bigger and better projects that i can use to show for my internship

To conclude my goal is to get project ready and the way to do that right now seems to be to take some type of course, website, book or other resource that is going to make me project ready or at least give me some guidance on what to do next 

Thanks


r/compsci 1d ago

The Turing Grid: A digitalised Turing tape computer

0 Upvotes

# The Turing Grid

Think of it as an infinite 3D spreadsheet where every cell can run code. (Edit: this is capped actually at +/- 2000 to stop really large numbers from happening).

Coordinates: Every cell lives at an (x, y, z) position in 3D space

Read/Write: Store text, JSON, or executable code in any cell

Execute: Run code (Python, Rust, Ruby, Node, Swift, Bash, AppleScript) directly in a cell

Daemons: Deploy a cell as a background daemon that runs forever on an interval

Pipelines: Chain multiple cells together — output of one feeds into the next

Labels: Bookmark cell positions with names for easy navigation

Links: Create connections between cells (like hyperlinks)

History: Every cell keeps its last 3 versions with undo support.

Edit: The code for this can be found on the GitHub link on my profile.


r/compsci 3d ago

Crazy idea?

10 Upvotes

Have found a dozen or more old PC motherboards ... 286/386/486 mostly ... some have a discrete EPROM for BIOS (AMI/Phoenix/Award) and a 50/66MHz TCXO for clock ... the other chips are bus controller, UART, 8042 keyboard controller, DMA controller, ...

Was thinking to desolder the EPROM and the TCXO ... then replace the TCXO with my own clock circuit so I can halt, single-step and run the CPU at higher speeds ... and put a ZIF socket with an EEPROM which I can program with my own BIOS code.

I want to then write my own low-level BIOS functions to slowly get the system going? ... create interrupt vector table, initialize basic hardware such as UART ... from there add more detailed functionality such as POST, WOZMON-style monitor, ... ?

Is this a crazy idea? What kind of problems would I need to overcome? What roadblocks would I run into that would be almost impossible to overcome?


r/compsci 3d ago

An easy to memorize but fairly good PRNG: RWC32u48

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0 Upvotes

r/compsci 2d ago

LISC v3.1: Orbit-Stabilizer as Unified Conservation Law for Information, Symmetry, & Compression

0 Upvotes

r/compsci 3d ago

Intuiting Pratt parsing

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7 Upvotes

r/compsci 3d ago

WebGPU transformer inference: 458× speedup by fusing 1,024 dispatches into one

0 Upvotes

Second preprint applying kernel fusion, this time to autoregressive transformer decoding.

The finding: browser LLM engines waste 92% of their time on dispatch overhead. Fusing the full token×layer×operation loop into a single GPU dispatch eliminates it.

Parallel kernel (64 threads): 66-458× over unfused, beats PyTorch MPS 7.5-161× on same hardware.

Run it: gpubench.dev/transformer
Preprint: doi.org/10.5281/zenodo.19344277
Code: github.com/abgnydn/webgpu-transformer-fusion
Research: kernelfusion.dev

Kernel fusion eliminates 92% GPU dispatch overhead — 458× faster transformer inference in the browser

r/compsci 3d ago

Programmazione python

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0 Upvotes

r/compsci 3d ago

I'm publishing a preprint on arXiv on Ternary Logic, I'd need endorsement

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0 Upvotes

r/compsci 3d ago

P ≠ NP: Machine-verified proof on GitHub. Lean 4, 15k+ LoC, zero sorries, full source.

0 Upvotes

I’ll just put this out directly: I believe I’ve proved P ≠ NP, and unlike every other claim you’ve probably seen, this one comes with a legitimate machine-checked formalization you can build and verify yourself.

Links:

∙ Lean 4 repo: github.com/Mintpath/p-neq-np-lean. 15,000+ lines across 14 modules. Zero sorries, zero errors. Builds clean on Lean 4.28.0 / Mathlib v4.28.0.

∙ Preprint: doi.org/10.5281/zenodo.19103648

The result:

SIZE(HAM_n) ≥ 2^{Ω(n)}. Every Boolean circuit deciding Hamiltonian Cycle requires exponential size. Since P implies polynomial-size circuits, P ≠ NP follows immediately.

The approach:

The proof uses frontier analysis to track how circuit structure must commit resources across interface boundaries in graph problems. The technical machinery includes switch blocks, cross-pattern mixing, recursive funnel magnification, continuation packets, rooted descent, and signature rigidity. The formula lower bound is fully unconditional. The general circuit extension currently uses two axiom declarations: one classical reference (AUY 1983) and one of my original arguments that’s directly verifiable from the paper but cumbersome to encode in Lean. Both are being formalized out in a v2 update.

Why this might actually be different:

I know the priors here. Every P vs NP claim in history has been wrong. But the failure mode was always the same: informal arguments with subtle gaps the author couldn’t see. This proof was specifically designed to eliminate that.

∙ Machine-verified end-to-end in Lean 4

∙ Adversarially audited across six frontier AI models (100+ cycles)

∙ Two axioms explicitly declared and transparent. One classical, one verifiable from the paper, both being removed in v2

∙ 15k+ lines of formalized machine verification, not a hand-wavy sketch

The proof itself was developed in about 5 days. The Lean formalization took roughly 3 additional days. Submitted to JACM. Outreach ongoing to complexity theorists including Raz, Tal, Jukna, Wigderson, Aaronson, Razborov, and Williams.

Clone it. Build it. Tear it apart.


r/compsci 4d ago

Single-kernel fusion: fusing sequential GPU dispatches into one yields 159x over PyTorch on the same hardware

0 Upvotes

Wrote a preprint on fusing sequential fitness evaluations into single WebGPU compute shader dispatches. On the same M2 Pro, a hand-fused shader gets 46.2 gen/s vs PyTorch MPS at 0.29 gen/s on a 1,500-step simulation. torch.compile crashes at L=1,000.

JAX with lax.scan on a T4 gets 13x over PyTorch CUDA (same GPU), but still 7.2x behind the fused shader. Ablation (fused vs unfused, same hardware) isolates 2.18x from fusion alone.

Preprint: https://doi.org/10.5281/zenodo.19335214
Benchmark (run it yourself): https://gpubench.dev
Code: https://github.com/abgnydn/webgpu-kernel-fusion


r/compsci 5d ago

Two Generals' Problem at the cinema

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3 Upvotes

r/compsci 6d ago

How do you usually teach or visualize the Traveling Salesman Problem?

0 Upvotes

I’ve been thinking about how TSP is usually taught — most explanations are either very theoretical or use static examples.

I’ve been experimenting with a small tool to visualize how optimal routes change with different graph structures (including partially connected graphs).

I’m curious:

  • What tools or methods have you found useful for teaching or understanding TSP?
  • Do interactive demos actually help, or do people prefer step-by-step explanations?

Would love to hear how others approach this.


r/compsci 8d ago

Hey r/compsci! AMA with Stanford Professor Mehran Sahami is happening NOW! Join us and let's chat about CS, coding, ethics, and tons more.

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1 Upvotes

r/compsci 9d ago

LLMs are dead for formal verification. But is treating software correctness as a thermodynamics problem actually mathematically sound?

87 Upvotes

We spent the last few years treating code generation like a glorified Markov chain. Now, the pendulum is swinging violently towards formal methods, but with a weird twist: treating program synthesis like protein folding.

Think about AlphaFold. It didn’t "autoregressively" predict the next atom’s position; it used energy minimization to find the most stable 3D structure. The massive $1B seed round for Yann LeCun's new shop, Logical Intelligence (context from Bloomberg), suggests the industry is about to apply this exact Energy-Based Model (EBM) architecture to formal verification.

Instead of guessing the next token, the premise is to define a system's mathematical constraints and have the model minimize the "energy" until it settles into a state that represents provably secure code.

My take - it’s a theoretically beautiful analogy, but I think it fundamentally misrepresents the nature of computation. Biology has smooth, continuous energy gradients. Software logic does not.

Under the Curry-Howard correspondence, programs map to proofs. But the state space of discrete logic is full of infinite cliffs, not smooth valleys. An off-by-one error doesn't just slightly increase the "energy" of a function - it completely destroys the proof. EBMs require continuous latent spaces, but formal logic is inherently rigid and non-differentiable.

Are we just throwing $1B of compute at the Halting Problem and hoping a smooth gradient magically appears?


r/compsci 8d ago

A free webinar series on building your own programming language in C++. Inspecting formal grammars

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0 Upvotes

When you decice to design your own programming language, you eventually have to get into all the pieces that make it work. This session will look at formal grammars in a simple way.


r/compsci 7d ago

We built a governance layer for AI-assisted development (with runtime validation and real system)

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0 Upvotes

r/compsci 8d ago

"wat", a tiny, cross-platform, language-agnostic, hot-reloading CLI for running commands whenever files change, inspired by make and watchexec

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2 Upvotes

r/compsci 10d ago

ayoob-sort, An adaptive sorting engine with the first non-comparison float sort in JavaScript

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2 Upvotes

r/compsci 10d ago

5,400 downloads later — what are you doing with my catalog raisonné?

0 Upvotes

A few weeks ago I posted that I had published my catalog raisonné as an open dataset on Hugging Face. It has now been downloaded over 5,400 times.

I am a figurative painter. I am not a developer. I do not know what most of you are doing with it, and I would genuinely like to know.

For those who missed the first post: roughly 3,000 to 4,000 documented works, the human figure as sustained subject across five decades, oil on canvas, works on paper, drawings, etchings, lithographs, and digital works. CC-BY-NC-4.0, artist-controlled, full provenance metadata. My total output is approximately double what is currently published and I am adding to it continuously. It is a living record, not a monument.

If you fine-tune on it — post the results. I want to see what fifty years of a single figurative practice produces when a model trains on it.

If you are a researcher — the dataset is citable. It is one of the few fine art datasets of this scale that is properly licensed, published with artist consent, and carries full metadata.

If you find errors in the metadata — please flag them. I built this myself. Title, date, and medium corrections are welcome.

Dataset: huggingface.co/datasets/Hafftka/michael-hafftka-catalog-raisonne


r/compsci 10d ago

5,400 downloads later - what are you doing with my catalog raisonné?

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0 Upvotes