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@netzschleuder@social.skewed.de
2025-12-11 02:00:04

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.

@cheryanne@aus.social
2026-01-05 18:08:47

Make Me Data Literate
features Dr Linda McIver interviewing fascinating people who work with Data, asking the question: What is the one thing you wish everyone knew about data...
Great Australian Pods Podcast Directory: greataustralianpods.com/make-m

Make Me Data Literate Screenshot of the podcast listing on the Great Australian Pods website
@netzschleuder@social.skewed.de
2025-11-07 20:00:03

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.

@grumpybozo@toad.social
2025-12-02 22:58:37

I never could have been literate in Chinese. Not that I could not read those 2 characters as different, but I’d never be able to write characters with that fine a distinction with any sort of reliability. American cursive was bad enough. mastodon.social/@mcc/115651531

mcc (@mcc@mastodon.social)
Current source of Mandarin learning frustration: 时 and 肘 are not only different but entirely unrelated. Yes, I know one of those radicals is sun and one is moon. I wasted an alarming amount of time entering characters in the dictionary app before figuring it out however

@arXiv_csSE_bot@mastoxiv.page
2025-10-13 09:54:00

Literate Tracing
Matthew Sotoudeh
arxiv.org/abs/2510.09073 arxiv.org/pdf/2510.09073

Literate Tracing
As computer systems grow ever larger and more complex, a crucial task in software development is for one person (the system expert) to communicate to another (the system novice) how a certain program works. This paper reports on the author's experiences with a paradigm for program documentation that we call literate tracing. A literate trace explains a software system using annotated, concrete execution traces of the system. Literate traces complement both in-code comments (which often lack glo…

@netzschleuder@social.skewed.de
2025-11-06 22:00:04

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.

@netzschleuder@social.skewed.de
2025-12-06 09:00:04

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.

@patrikja@functional.cafe
2025-10-26 13:17:25

In the paper "Agda-ventures with PolyP" Jeremy Gibbons (@…) and I revisit PolyP in a literate Agda setting — combining executable code, theory, and reflection on three decades of generic programming. It is part of a Festschrift gifted to Johan Jeuring at the academic celebration of his 60th birthday.
📖 Blog post:

Implementaiton of pmap
Agda implementaiton of pmap, fmap, cata in Agda. (Screenshot from the talk.)
@Jyoti@mas.to
2025-12-19 14:04:47

I gruntled. :ablobmeltsoblove:

Jeopardy answer: "A literate spider on a farm sends messages to her non-Japanese friend who is obsessed with Japanese culture"
Jeopardy contestant gets the right question, which is "What is 'Charlotte's Weeb?'"
@wraithe@mastodon.social
2025-11-23 15:10:54

Yea I can’t imagine why anyone thought this dipshit was defending rape…I mean aside from the over half a dozen posts where he defended rape as “not immoral”, literally said “No. In fact, the word "rape"…didn't even exist until the 1800s.” and arguing that being “owned”* wasn’t “horrific”
Complete mystery why people went after him, must be some weird BlueSky thing. 😂
JFC

Bluesky screenshot: The Louvre of Bluesky @thelouvreof.bsky.social horrible day to be literate i Possible Bluesky screenshot with an "i"@liawithani.bsky.social • 1h child rape was also horrific in 1776, hope this helps ••• Mugsy's RapSheet @mugsysrapsheet.bsky.social Follow Actually, no. There were no laws against having sex with child slaves in 1776. "Horrific" or no, it wasn't "immoral" in Jefferson's time. Would he have any less of a chance of being elected president in 2024? #PedoDon Nov…
Bluesky screenshot Mugsy's RapSheet @mugsysrapsheet.bs... • 17h Simply being "owned" isn't "horrific" (all wives were "owned"), or do you not believe providing "safe haven" was a form of protection? By that standard, the Van Daan family that hid the family of Anne Frank were subjecting them to "horrific mistreatment."
BlueSky Screenshot Mugsy's RapSheet @mugsysrapsheet.bs... • 17h Simply being "owned" isn't "horrific" (all wives were "owned"), or do you not believe providing "safe haven" was a form of protection? By that standard, the Van Daan family that hid the family of Anne Frank were subjecting them to "horrific mistreatment."
lol he blocked me so here he is crying on Mastodon: joined "BlueSky" (against my better judgement) last week so I could contact people/services that aren't on Masto. I made the mistake of responding to a post attacking Thomas Jefferson for failing to live up to a moral standard we clearly haven't even achieved in 2025, and the knives came out. Every self-important child misrepresented my claim, accused me of defending slavery & child rape , and bombed me with 400 posts in one hour. BlueSky = R…
@chris@mstdn.chrisalemany.ca
2025-11-12 18:42:22

The emails and docs from Epstein also seem to indicate that when it comes to being a paedophile and millionaire “financier”, there is no requirement to be a literate person able to write a coherent sentence...
#Trump #Epstein #criminals
masto.ai/@Nonilex/115537044865

@lukascbossert@mastodon.social
2025-10-21 14:16:56

Resilient technologies aren’t retro—they’re ROOT: Robust, Open, Ongoing, Time-tested. In RDM, text-first small, composable tools beat opaque stacks. Emacs/Org(-babel) for literate workflows & provenance; Makefiles declare rebuilds; CLI atoms—curl, sed, awk, grep, diff, tar, rsync, cron, SQLite—keep steps inspectable, portable, rebuildable. DOI:

“ROOT badge: Robust • Open • Ongoing • Time-tested.”
@netzschleuder@social.skewed.de
2026-01-04 09:00:04

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.

@arXiv_csLO_bot@mastoxiv.page
2025-10-15 08:57:32

Substitution Without Copy and Paste
Thorsten Altenkirch (University of Nottingham), Nathaniel Burke (Imperial College London), Philip Wadler (University of Edinburgh)
arxiv.org/abs/2510.12304

Substitution Without Copy and Paste
Defining substitution for a language with binders like the simply typed $λ$-calculus requires repetition, defining substitution and renaming separately. To verify the categorical properties of this calculus, we must repeat the same argument many times. We present a lightweight method that avoids repetition and that gives rise to a simply typed category with families (CwF) isomorphic to the initial simply typed CwF. Our paper is a literate Agda script.

@netzschleuder@social.skewed.de
2025-12-28 11:00:04

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.

@netzschleuder@social.skewed.de
2025-10-30 21:00:04

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.

@netzschleuder@social.skewed.de
2025-12-20 20:00:03

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.

@netzschleuder@social.skewed.de
2025-10-18 07:00:04

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.

@netzschleuder@social.skewed.de
2025-12-14 22:00:04

unicodelang: Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.
This network has 868 nodes and 1255 edges.
Tags: Informational, Relatedness, Weighted

unicodelang: Languages spoken by country (2015). 868 nodes, 1255 edges. https://networks.skewed.de/net/unicodelang

unicodelang — Languages spoken by country (2015)
A bipartite network of languages and the countries in which they are spoken, as estimated by Unicode. Edges are weighted by the proportion of the given country's population that is literate in a particular language.