Since when Zuckerberg is an AI expert?

I am puzzled (and irritated) why there is „rotate left” without „rotate right”. Does any of you know why?

There was "It’s Not Enough to Be Right – You Also Have to Be Kind" https://news.ycombinator.com/item?id=21490714

But I think the core part is WHY we want to be right? To prove something to others, or to ourselves? To feel better? As a compulsion? As a gambler's fallacy? Many motivations are less lofty that we dare to admit.

I wasted way to much time arguing online. It was mostly wasted time, and wasted emotions. I mean, I also had many eye-opening and enlightening discussions, but these rarely were fights.

Nice!

If you want to play a hyperbolic minesweeper, Hyperrogue features that https://hyperrogue.fandom.com/wiki/Minefield

Yes, it gets really hot really fast.

As much as I was tempted to use it on longer projects, I had some reservations about whether it would put too much strain on my MacBook.

All experiments with Qwen 3.6 required no more than 48GB Apple Silicon. I believe you can go even further with more aggressive quantizations - one can go down even further.

In any cases, from the economic point of view, running models on laptops make little sense. Even at the pure cost of energy consumption, it might be hard to beat pricing at tokens generated at scale.

At the same time, it is a breaktrough, that will change the game. Previously such vibe coding on consumer device was not hard or costly - it was impossible.

See also Gribouille: A Grammar of Graphics for Typst, discussed here a week ago https://news.ycombinator.com/item?id=48541062.

Violin plots have an interesting reputation (https://xkcd.com/1967/, https://www.reddit.com/r/labrats/comments/91ex4u/is_it_just_..., https://jabde.com/2022/12/22/banned-violin-plots/).

For showing distributions, I much prefer strip plots (https://seaborn.pydata.org/generated/seaborn.stripplot.html), perhaps with opacity, or swarm plots (https://seaborn.pydata.org/generated/seaborn.swarmplot.html) - no averaging with an unknown kernel, no hiding distributions behind a box plot, and the data is directly visible. We also directly see whether it is based on 5, 100, or many more points.

When using histograms, binning is usually more straightforward than kernels. And in any case, the mirror reflection of a histogram is not needed.

Another alternative is raincloud plot (depending on data).

I didn't know it has a name. But I have been using similar thing, https://github.com/harbor-framework/terminal-bench-science/p...

Interesting. Btw I agree with some of the comments. The framing/language of the question is a bit weird.

A year ago, added R to the pipeline (with multiple complications) just to use ggplot2 - even though Python was the main tech.

https://quesma.com/blog/sandboxing-ai-generated-code-why-we-...

Good, that ggplot2 can run inside in WASM, vide https://github.com/QuesmaOrg/webr-ggplot-playground

A big part of the motivation was that something like this...

  $ which ggplot
  ggplot () {
          if [[ "$1" == "-f" ]]
          then
                  shift
                  rush run --library tidyverse "$(cat "$1")" -
          else
                  rush run --library tidyverse "$@" -
          fi
  }

  $ echo "one,two,three\n1,2,3\n4,5,6\n,7,8,9" | ggplot 'ggplot(df, aes(one, two)) + geom_col() + theme_minimal()' | imgcat

...is just very slow. Booting R just to run ggplot2 was not cutting it compared to a custom DSL written in Rust!

BTW, that "R on the command line" tool was inspired by:

https://datascienceatthecommandline.com

For malware detection, many models are biased for or against detecting a threat (likely a thing that can be adjusted with a prompt).

I suggest tasks cannot be guessed (find, not tell). And 2d charts, both for ROC and pricing, vide https://quesma.com/benchmarks/binaryaudit/