Amaze, amaze, amaze!

Loved the Project Hail Mary quote from one of the mission controllers. :)

This bright spot in world news has been good for my mental health and general motivation. Thank you NASA!

You didn’t even look at what the tool does, did you?

> If the issue is that people write bad floating-point expressions, a code-writing tutorial would be a better solution.

Yeah you are just criticizing this without even looking at it. Shame.

> You didn’t even look at what the tool does, did you?

On the contrary, I did exactly that. It proactively intervenes where mathematical knowledge would be a better remedy overall. It shields programmers from their ignorance.

If floating-point code is correctly written, it can't possibly serve a useful purpose.

> Yeah you are just criticizing this without even looking at it.

See above -- don't jump to conclusions.

They're not on God's green earth anymore, now are they?

It’s also blue, not green.

touche!

The poverty mindset is that you still need to check your mail while riding around the moon. Style would be no email at all.

Please imagine the luxury of being SO FAR AWAY from all the crap happening on our planet right now, only to be spoiled by some lousy marketing emails from Microslop hawking their latest Copilot incursion.

Most don’t seem to think about morals or quality at all: https://www.theatlantic.com/ideas/2026/03/introspection-andr...

This is cool!

I use Zotero [1] to manage/read/annotate all my papers and it's got a built-in PDF inverter that works pretty well. I'll take Veil out for a spin some time and see if it works well in places where Zotero's algorithm fails.

[1]: https://zotero.org

Thanks! I'm curious to see how the comparison goes, especially on papers with lots of images and color charts. Let me know how it goes

You can have private repositories. It's discouraged because Codeberg is meant for FOSS projects, but it's totally possible.

That song is gold.

The core of this little essay seems to be this:

Instead of "I understand the causal mechanism and can predict what happens if I change X," you get something more like "I have a sufficiently rich model that I can simulate what happens if I change X, with probabilistic confidence." The answers are distributions, not deterministic outputs. That's a different kind of knowing.

At the beginning this sounded like, "hard problems are complex, machine learning can help us manage complexity, therefore we will be able to solve hard problems with machine learning", which betrays a shallowness of understanding. I think what this essay argues here is a little deeper than that trite tech-bro hype meme.

But I disagree with this conclusion: I don't know that we can begin to build these models to begin with or that our new LLM/transformer-powered tools can help solve these problems. If simulation were the answer to everything, why will new ML tools make a significant difference in ways that existing simulation tools do not?

Stuff like AlphaFold is amazing—I'm not saying that better medical results won't come about from ML—but I feel like there's some substance missing and that even this level of excitement that the author expresses here needs more and better backing.