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

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.

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.

Comments URL: https://news.ycombinator.com/item?id=48721903

Points: 1124

# Comments: 699

Source code: https://github.com/stared/tree-of-tree

I am grateful to Miroslav Šedivý, a Czechoslovak polyglot I met at PiterPy 2018, who sparked a curiosity that Polish 'zdrowy' (healthy) is from 'drewno' (wood) or 'drzewo' (tree), which are related to the English word 'tree'. A few weeks ago I saw Odd Pride's short on the tree of life, which prompted me to make an explorable explanation (https://p.migdal.pl/blog/2024/05/science-games-explorable-ex...), a medium close to my heart.

Comments URL: https://news.ycombinator.com/item?id=48671209

Points: 5

# Comments: 1

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.

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

Comments URL: https://news.ycombinator.com/item?id=48643725

Points: 2

# Comments: 0

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

Comments URL: https://news.ycombinator.com/item?id=48635982

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=48634156

Points: 46

# Comments: 66

I didn't know about these Typst restrictions. Silent fails are the worst, so if a constructor is necessary to prevent these, good it is there.

Thanks for explaining! (And for developing gribouille in the first place!)

Gribouille is not ggplot2, or other. Syntax is different. Superficial keyword similarity is (usually) a false friend. Reusing a keyword might be useful, but keeping an unnecessary construction is (in my view), a cargo cult.

Typst itself breaks with a lot of LaTeX stuff, and it is good that it does not pretend it is LaTeX-with-Rust, but has a fresh look.

A non-native UI has some issues, but also one clear advantage - it is easier to make a cross-system app with the same looks.

Comments URL: https://news.ycombinator.com/item?id=48597914

Points: 3

# Comments: 0

In any case, I'm curious whether aes is necessary, or whether it would suffice to drop this function entirely and just use keys in the mapping (similarly for labs). Or, more broadly, whether using patterns from other implementations of the Grammar of Graphics is a conscious decision, or some sort of legacy baggage.

But from what I see, it is the opposite - a lot (if not virtually all) progress in the last decade of deep learning was not because of a fundamental idea, but incremental, experimentally-verified practice. Even though I think there is good intuition for why ReLU is better than sigmoid (tl;dr: last layer is log(sigmoid) ~ ReLU, putting anything different inside kills the gradient), the original paper by Hinton himself was more or less "because it trains 3x faster".

Re-thinking fundamentals might help, but most "let's change the fundamentals" is rarely how it works. Even the most seminal papers, i.e. AlexNet and "Attention Is All You Need", are refinements of existing ideas, and show how they help.

Machine learning is an experimental science. Many mathematically cool ideas do not work. Many engineering ones do.

> I've tweeted before that one of the most important traits in a researcher is healthy paranoia. Be paranoid!

I have seen so many PhDs burned out to cinders; I don't think it is any more a good piece of advice than "depression is good for philosophers". Sure, be a relentless explorer.

> In short, holding on to ideas for too long can actually be counterproductive. Stay open-minded and refuse to let ego cloud your judgement.

Which I think is true.

Comments URL: https://news.ycombinator.com/item?id=48569887

Points: 2

# Comments: 0