Mauve 👁💜 @mauve@mastodon.mauve.moe

Some architectural #conceptart I did for #scavengersreign (2). #Blender

#art #anime #mastoart

How bad are the thousands of new stochastically-generated websites?
Last night I wanted to roast some hazelnuts, and I could not remember the temperature I used last time. So I searched on DuckDuckGo. Every website that I could find was machine-generated with different temps listed. One site had three separate methods listed that were essentially differently worded versions of the same thing. With different temperatures.

So I pulled my copy of Rodale’s Basic Natural Foods Cookbook off the shelf and looked it up there.

I think it may be time to download an archive copy of the 2022 Wikipedia before we lose all of our reference material. It was nice having all the world’s knowledge at my fingertips for a couple of decades, but that time seems to be past.

[ Edit: Since others have mentioned the possibility, I should mention that some of these sites may have been SEO-generated/altered and not generated by an LLM. However, even if that is the case, the fact that the sites are as bad as and indistinguishable from LLM-generated sites means to me they are just as bad and just as likely to be have only a loose resemblance to reality. There are many ways to be a fancy stochastic parrot. ]

Ugh 😤 ip command is the most significant change in Linux since they deprecated the ifconfig, a standard command on unix-like systems such as macOS, freebsd, and others. Check out my ip command guide https://www.cyberciti.biz/faq/linux-ip-command-examples-usage-syntax/ for more info.

This is going to get interesting.

From Tom the Dancing Bug // @rubenbolling

https://boingboing.net/2024/01/01/meet-tom-the-dancing-bugs-newest-character-mickey-mouse.html

@ShadowJonathan see also

@llewelly @Gurre @futurebird
Don’t accept the chauvinistic tradition that labels our era the age of mammals. This is the age of arthropods.They outnumber us by any criterion – by species, by individuals, by prospects for evolutionary continuation.

@Gurre

OMG it's smaller than an amoeba!

I'm screaming!!

They lay their eggs inside of the eggs of 1-2mm long crop pests.

And... read the article to see what the males are like... they are even smaller somehow, but it's ... disturbing.

https://blogs.dal.ca/openthink/a-tiny-wasps-big-brain-survival-strategy/

Photo by Alexey Polilov, 2012

Finding a new job is not going to be easy, after six years of corporate bullshit I just can't anymore. If you need a very experienced Kotlin/Android dev, but don't do any AI/Blockchain/Tracking/Ad stuff let me know.

infosec best practice in 2023: do not use or install any software

The more you know, the more you wish you didn't.

Checking in on whether #bluesky / #atproto has become any more like a communication medium, and... nope. almost unchanged since i looked at it last in June. Bluesky is a spectator platform where a small number of accounts receive most of the visibility and smaller accounts are effectively invisible. The introduction of new feed algorithms (to the degree that happened, there aren't really many that I can find in wide use) did not change that. This is a non-normative analysis: in some cases, it is good to have a medium that promotes some very small number of posts and accounts, eg. to surface singular events, etc.

From a 25h sample of the firehose...
- 600k posts, 2.4m likes, 250k boosts, 350k follows
- 40% of posts receive 0 likes, 70% receive <= 1
- accounts in the 99th percentile of likes received 44% of likes, accounts in the 95th percentile received 74%
- 40% of posts were from accounts within the top 95th percentile of accounts by likes received.
- the maximum number of likes for a post by an account not in the top 95% is 32.

The first plot below shows the cumulative sum of likes received on the y axis against each account in the sample on the x axis - this includes accounts that didnt' post during the sample (but would still have posts that could be liked, so this also shows the extreme recency bias). The second plot is a hockeystick showing the number of likes (*not* cumulative sum) received on the y axis per post on the x axis.

For background, the default algorithm only cares about likes, boosts don't matter, which is why i am calculating things by likes here - they are the primary algorithmic signal.

These are the same calculations that I did back in June, but this time i'm leaving the firehose open to do a longer sample to be able to parse momentary virality from persistent effects.

edit: more on "where is the fedi comparison" and "why is it like this" https://neuromatch.social/@jonny/111660754706547194

Do you know anyone looking for a job that wants to travel and problem solve? Looking for someone with a software dev background who is ready to do more people facing work. This is not a full time coding job, more like 30% coding 70% whiteboarding, teaching, debugging, pair programming.