@netzschleuder@social.skewed.de2025-12-27 05:01:03
discogs_genre: Discogs artist affiliations
Two bipartite networks of the affiliations between musical artists and either musical genres or musical "styles," as given in the discogs.com database. An edge indicates that a given artist was involved in a production of a particular genre or style. The date of this snapshot is uncertain.
This network has 1754838 nodes and 19033891 edges.
Tags: Informational, Relatedness, Unweighted
@heiseonline@social.heise.de2025-11-24 14:04:00
KI-Update kompakt: KI-Wettrennen, Nano Banana Pro, Figure 02, KI-Gamer
Das "KI-Update" liefert drei mal pro Woche eine Zusammenfassung der wichtigsten KI-Entwicklungen.
https://www.
@kexpmusicbot@mastodonapp.uk2025-10-28 16:03:42
πΊπ¦ #NowPlaying on KEXP's #MorningShow
Aqueduct:
π΅ Growing Up With G'N'R
#Aqueduct
https://aqueduct.bandcamp.com/track/growing-up-with-gnr-2
https://open.spotify.com/track/4QBAPwQmD0BfJanGKYfRds
@benb@osintua.eu2025-12-27 08:32:12
General Staff: Russia has lost 1,203,310 troops in Ukraine since 2022: https://benborges.xyz/2025/12/27/general-staff-russia-has-lost.html
@mxp@mastodon.acm.orgβ¬2025-12-27 16:53:41
Some people now argue that LLMs are useless. I disagree; they can be very useful if you take them as what they are: models of language that generate text on the basis of some given text. As such, they can be useful for a wide range of text-related tasks, including assisting with writing. And the more formulaic the genre, the better they work obviously. This is part of the reason why they are so popular with students, and in academia more generally.
β’
βͺ@mxp@mastodon.acm.orgβ¬2025-12-27 16:53:41
Some people now argue that LLMs are useless. I disagree; they can be very useful if you take them as what they are: models of language that generate text on the basis of some given text. As such, they can be useful for a wide range of text-related tasks, including assisting with writing. And the more formulaic the genre, the better they work obviously. This is part of the reason why they are so popular with students, and in academia more generally.
β’
@netzschleuder@social.skewed.de2025-12-24 01:00:07
dbpedia_genre: DBpedia work-genre network
A bipartite network of the affiliations between artists and their works on one side and genre classifications on the other, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 266717 nodes and 463497 edges.
Tags: Informational, Relatedness, Unweighted
@netzschleuder@social.skewed.de2025-12-22 08:00:08
dbpedia_genre: DBpedia work-genre network
A bipartite network of the affiliations between artists and their works on one side and genre classifications on the other, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 266717 nodes and 463497 edges.
Tags: Informational, Relatedness, Unweighted
@netzschleuder@social.skewed.de2025-10-19 17:00:08
dbpedia_genre: DBpedia work-genre network
A bipartite network of the affiliations between artists and their works on one side and genre classifications on the other, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 266717 nodes and 463497 edges.
Tags: Informational, Relatedness, Unweighted
@netzschleuder@social.skewed.de2025-12-22 17:01:02
discogs_genre: Discogs artist affiliations
Two bipartite networks of the affiliations between musical artists and either musical genres or musical "styles," as given in the discogs.com database. An edge indicates that a given artist was involved in a production of a particular genre or style. The date of this snapshot is uncertain.
This network has 1754838 nodes and 19033891 edges.
Tags: Informational, Relatedness, Unweighted
