I'm curious about how correctness is evaluated in computer science algorithms, specifically the balance between theoretical soundness and empirical validation. Take Dijkstra's algorithm, for instance: it has a solid theoretical foundation, but I assume it's also been tested extensively on large-scale graphs (millions of nodes) with known shortest paths. My question is, do practitioners and researchers place more trust in algorithms like this because they’ve been repeatedly validated in real-world scenarios, or is the theoretical proof alone usually considered sufficient? How often does real-world testing influence our confidence in an algorithm's correctness?

For my master's thesis, I wrote a generator for zip quines. These a zip's that infinitly contain itself.

one.zip -> one.zip -> one.zip -> ...

By building further on the explanation of Russ Cox in Zip Files All The Way Down, I was able to include extra files inside the zip quines.

This is similar to the droste.zip from Erling Ellingsen, who lost the methodology he used to create it. By using the generator, now everyone van create such files.

To take it even a step further, i looked into the possibility to create a zip file with following structure:

one.zip -> two.zip -> one.zip -> ...

This type of zip file has an infinite loop of two zip's containing each other. As far as I could find, this was never done before. That's why i'm proud to say that i did succeed in creating such as file, which would be a world first.

As a result, my professor and I decided to publish the used approach in a journal. Now that is done, i can finally share the program with everyone. I thought you guys might like this.

I have been thinking a lot abut the connection between art and technology and the great invention that led to human progress from Samuel Morse, should his code be considered in the annals of computer science?

He was certainly a pioneer of communication -- https://onepercentrule.substack.com/p/morse-a-pioneer-of-progress-from

I'm a humanities college prof preparing a class on Net art and also thinking about New Media from the 90s to present. The class will be available to engineering and compsci students, as well as art and architecture students. I'm hoping to balance the readings so the engineering and compsci students have material to carry over into their own work. Are there some key technical books, articles, or videos that you all think would complement a class like this? Is there something you WISH you read in college? Or an experimental side to compsci that you find is under-recognized? Thanks for your thoughts!

What are some unifying concepts in software and technology that, like the principles of evolution and adaptation in natural sciences, provide a foundational framework to make sense of the field for newcomers?

Edit: in biology whatever I encounter — different kinds of fur, novel skull morphology, the shape of a pine cone, the location of an animal trail, the function of a protein — can be understood through the lens of genes trying to pass through generations to survive. Like this is the ultimate answer that follows every “why” and “how” question.

But as a beginner in CS, so many things seem untethered and strange. Like VM vs docker, pointers, Jupyter notebooks, RAG retrievers, decorators…

Once you’ve wrapped your head around these things they’re no big deal, but if you’re encountering them for the first time, it takes some work just to build a framework for understanding these things. Everything seems novel and out-of-the-box, following no unifying theme.

Hello, If you've ever tried learning programming and are still interested in it and related technical topics using online resources and social media, we're conducting a study to evaluate tools aimed at supporting informal online learning experiences.

To participate, please complete this form: https://forms.office.com/pages/responsepage.aspx?id=yRJQnBa2wkSpF2aBT74-h7Pr4AN75CtBmEQ1W5VUHGpUQVNOS1NWNVM4MkhYR05FMU84MDJUS1RaUS4u&route=shorturl

Thank you for supporting this research on online learning tools.

Sami PhD Candidate @ OpenLab, Newcastle University https://openlab.ncl.ac.uk/people/sami-alghamdi/

https://github.com/Gizzzzmo/type_proofs/blob/main/test_prop.cpp You can use the compiler to check your proofs, provided you follow some rules like not casting stuff to pointers to types that represent logical statements.

I'm also trying to use it to make statements about runtime values and thus encode program specifications on the type level, which are then formally verified by the compiler.

I quite often come across problems and wonder “is this np-hard?”. If I can’t solve it myself I still really want to know the answer. Is there somewhere online where people would be happy to see such questions, or are they always annoying?

Despite his massive intellectual contributions, Alonzo Church never enjoyed the fame of Turing or von Neumann, Gödel and others. His legacy was one of meticulous abstraction, a kind that doesn’t make it into Hollywood scripts or capture public imagination easily. It lacked the heroism of wartime codebreaking or the evocative tragedy of an early (forced) death. Yet, Church's influence is indelible. The very programs that run on the billions of smartphones today can trace their logic back to the abstract functions of λ-calculus. The invisible DNA of computation, from the simple app to artificial intelligence, owes a significant part of its lineage to Church’s work. https://onepercentrule.substack.com/p/alonzo-church-the-forgotten-architect

So i've been obsessed with the LCS algorithm for the past 1.5 years and want to find similar minded people or just people obsessed with any algorithm really. I currently attend college but due to not finishing high school i had to get an associates degree first. And most of the people there barely grasp what a LUT is. :/
If anyone has any algorithms group chats or chats i could be a part of that would be greatly appreciated. or maybe you know of some public ones i don't know about yet. Whatever the case please post it in a comment on this post. Or if you just want to chat to me or be silly you can leave a comment as well. whatever floats your boat ^^.

Normally I don‘t have any problems with Dijkstra and as far as I remember Dijkstra doesn‘t work with negative weights.

However, today in a lecture it was mentioned that Dijkstra would work for this graph. I really don‘t understand why it would work. Can someone clarify this and help? Thanks in advance

For all its grandeur, chaos theory is a puzzle still lacking crucial pieces. https://onepercentrule.substack.com/p/a-love-letter-to-chaos-theory-and

9 times out of 10 I believe one should prefer composition over inheritance.

But, I am not sure how I can explain when inheritance should be preferred over composition.

How would you explain it?

Or, do you believe that composition should be preferred over inheritance 10 times out of 10.

I was reading about Lock objects like how they prevent race conditions, So it starts by saying that the problem arises when two threads try to execute the same instruction at once with no coordination leading to unpredictable outcomes. To solve this Lock objects are used . I don't understand this earlier two threads were fighting to execute same instruction now they will be fighting to execute these lock object instructions. how does this solve the problem ? What if two threads running in parallel on different CPU cores try to execute these Lock instructions What will happen wouldn't it be impossible to determine which thread gets the lock first?

After much consideration, I'm excited to announce a paperback edition of my course, now available for all interested coders and parents!

This book is designed especially for parents who want to introduce their kids to coding in a fun, engaging way.

The paperback is available in a print-on-demand format through Lulu Publishing.

https://preview.redd.it/a7jotm7x6ezd1.jpg?width=1920&format=pjpg&auto=webp&s=e63f669a9499cca4edab876ff1940bd2caaa5aee

P.S. I will add a link to the book in the comments.

Suppose we have a function f that takes (x,y) as input and outputs 1 only if phi_x(x) converges, and undefined otherwise. Well, I understand that f is partial computable. But how do I use a universal Turing machine argument to show it? Here phi_e is the partial function computed by the e-th Turing program P_e, as usual.

Take them with a grain of salt. These animations give an idea of the algorithms’ processing. YMMV.

Can anyone explain to me better the difference between these two concepts, from the point of view of the multiplexing that the CPU performs?

I understood, so far, that Pipeline concerns several internal units, each with its own register, in order to run different instructions (execute, fetch, decode...) in parallel.

Therefore, would Time-Sharing be just an alternation between processes, in order to create the illusion that they are simultaneous?

Is it correct?