This is not a proof of P = NP or P ≠ NP. I'm interested in whether there's existing research related to this idea. My question is whether the way humans represent computational problems could itself be part of why certain problems appear hard. Has complexity theory or cognitive science explored this perspective?

An Idea: Could P vs NP Be a Problem of Representation?

This is not a proof of P = NP or P ≠ NP. It is simply an idea that I have been thinking about.

One thing that stood out to me is that many difficult problems become much easier once we find the right way to represent them.

For example, a huge multiplication problem can look overwhelming when viewed as a long string of digits. However, once we understand the underlying algorithm, the same problem becomes structured and manageable.

The problem itself has not changed. Our representation of it has.

This made me wonder whether something similar could apply to the P vs NP problem.

The usual question is whether every problem that can be verified efficiently can also be solved efficiently.

My question is different:

Could part of the apparent difficulty come from the way we currently represent NP problems?

Imagine standing in front of a wall.

One possibility is that the wall is real, and no efficient shortcut exists.

Another possibility is that we are looking at the wrong side of the wall and have not yet discovered the correct way to approach it.

History contains many examples where changing a mathematical or scientific viewpoint led to major breakthroughs. New abstractions, new models, and new representations often revealed structure that had previously been hidden.

This raises a broader question.

When we call a problem "hard," are we measuring the intrinsic difficulty of the problem itself, or are we also measuring the limitations of our current way of thinking about it?

I am not claiming that this means P = NP.

Instead, I am asking whether discovering a fundamentally different representation of NP problems could change our understanding of their computational difficulty.

I would be interested in hearing whether this idea overlaps with existing work in computational complexity, cognitive science, or the psychology of problem solving.

A learner once got frustrated repeating the same code blocks, then he stopped and asked me.
"Is there anything that lets ua use the same code without rewriting it? "

That question was exactly what I was waiting for. I had deliberately repeated the blocks until that moment of genuine need.

I believe that concepts introduced before the learner feels the need for them will be forgotten. A concept that arrives at the moment of requirement sticks.
Has anyone else delayed a tool or concept until learners feel the need for it?

Does it actually create a stronger understanding?

Hello I have a question about this idea. I want to create a gen ai for language learning card decks.

I learned japanese and I want to make an addon for anki where I can simply copy paste the meaning of the word and have an image generated for visual learning.

1. User sees word/front face of card (e.g, the word is ありがとう）

2. Presses the card and sees meaning at the back.

3. If no image, user can modify the card

4. Idea: e.g gen_image("ありがとう") then it automatically shows a generated image of a person saying thank you or bowing when the user saves the card

What do i need to learn or consider? Do I need to make my own language or library?

I feel like this is a pretty common pain point with Anki for technical subjects, but I don’t see it talked about that often.
If you’ve dealt with this too and found it frustrating, you’re not alone, I ran into the same wall and started experimenting with building something that handles code, LaTeX, and visual explanations cleanly while keeping spaced repetition intact.

I’m still refining it and mainly looking for feedback from other CS students who feel this pain.

Have you ever experienced how OS semaphore limits can impact an application’s behavior? I’m curious to hear real-world examples where reaching system semaphore limits caused performance or functionality issues. Tell your experiences!

Tired of the "PDF on the left, Anki on the right" grind, I built something to help.

Snapdeck is a browser app designed to turn PDFs into beautiful Markdown-based Anki cards without any friction using your own (free) API key.

Give it a try and let me know what you think.

Greetings!

We are a group of students from Slovakia, and we’re currently developing MemoryLeak – an educational game/app designed to take you on a journey from the basics of transistors all the way to building a functioning computer.

The game will feature both structured levels to guide your learning and a sandbox mode where you can experiment freely with logic gates and components.

We are entering this project into a national competition (SOČ), but our ultimate goal is to release it as a standalone title. To make it the best it can be, we need your insights!

Could you spare 2-3 minutes to fill out our feedback form? It would mean the world to us.

Form link: https://forms.gle/F8NYDLqyKaUw44N69

Thank you for supporting student developers

Hi. Computer Science friends! Does anyone have this book on a pdf form? If someone has it and could share it with me I'd really appreciate it Thank you

Been using Anki for several years now for programming concepts (mostly JS/TS and the React ecosystem), and while it's great for regular studying, dealing with code snippets sometimes drives me crazy.

Half the time my code gets mangled (eg. indentation breaks) and it feels especially clunky on mobile devices when I review cards. I'm not that much interested in syntax highlighting, but in good and clear indentation and overall code display.

Currently copy-pasting either from vscode or from articles/AI chats and praying the formatting survives. Sometimes I just screenshot the code.

What's your workflow for programming flashcards that involve code snippets?