A Ruby program that generates itself (through a 128-language quine loop)

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  • quine-relay

    An uroboros program with 100+ programming languages

  • This issue imploring Yusuke Endoh to write a paper about it is amusing:

    https://github.com/mame/quine-relay/issues/10

    > p-e-w:

    > This is more than a cool program – it's a computer science breakthrough! Apparently, you have discovered a method for constructing quines based on an arbitrary blueprint involving any amount of languages.

  • radiation-hardened-quine

    A robust quine program that works even after any one character is deleted.

  • I like the Radiation-hardened Quine: https://github.com/mame/radiation-hardened-quine

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • quine-howto

    how to write lots of quines

  • virgil

    A fast and lightweight native programming language

  • I hadn't written one until ~30 mins ago [1]. I cheated and looked at a Java quine (not particularly elegant, but easy to see what is going on.), but I wrote one for Virgil. Just think string substitution; a string with a hole in it and you substitute a copy of the string, quoted into the hole. Just one substitution suffices.

    [1] https://github.com/titzer/virgil/blob/master/apps/Quine/Quin...

  • nixpkgs

    Nix Packages collection & NixOS

  • There is an ongoing issue in Nixpkgs[0] to package this quine, more help would be appreciated!

    [0] https://github.com/NixOS/nixpkgs/issues/131492

  • quine

    Discontinued A quine in Go (by 62726164)

  • You're teasing, but I understand the question so I'll answer.

    I've had a good deal of joyous fun exploring quirky ideas with mathematics and software. It's old hat now, but back in 2011 I repurposed an algorithm primarily used for document classification by creating my own tokenization of features and stuffing it in there then running it through a ton of photographs, but forcing it to only 200 dimensions of freedom.

    Since the algorithm didn't have room to fully separate dimensions, it had to start grouping photos together along dimensions that had to account for multiple topics. I'll never forget bursting out laughing as I explored one particular dimension that was "motorcycles...... and photos of women taken from street level aimed at their scantily clad behinds."

    Collaborative filtering proved to me that some stereotypes really do have grounding in reality.

    As for the quine stuff? I actually had more fun repurposing[0] some of the ideas for a letter to my MP about the dangers of electronic voting back in 2011 when it seemed possible that Canada was going to allow it at the federal level. I chuckled more at literally sending self-eating code to a presumable less technical politician than I did at getting something to repeat itself.

    [0] https://github.com/zachaysan/darth_vader_wins_election

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