I have been writing Elixir professionally for the bulk of my career. (It’s been pretty awesome!) Despite this, I consider Racket to be my native language because it’s so easy for me to think in Racket. It’s the perfect bridge between my brain and the problem domain.

As an aside, have you seen Typst? It’s got LaTeX-level typesetting quality but the markup syntax is a lot friendlier (close to Markdown) and the scripting language is a Real Language™ with sensible error messages and sub-second compilation times even for big documents.

But for less-excellent authors, where’s a good place besides Amazon to get ebooks?

Not possible for all problems. We cannot decide correctness (ie adherence to a specification) for all programs, but we can definitely recognize a good chunk of cases (both positive and negative) that are useful.

The Halting Problem itself is recognizable. The surprising result of Turing’s work was that we can’t decide it.

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!

> 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.

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

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.

    ;; Speed up magit status by removing some things
    (remove-hook 'magit-status-sections-hook 'magit-insert-tags-header)
    (remove-hook 'magit-status-sections-hook 'magit-insert-status-headers)
    (remove-hook 'magit-status-sections-hook 'magit-insert-unpushed-to-pushremote)
    (remove-hook 'magit-status-sections-hook 'magit-insert-unpulled-from-pushremote)
    (remove-hook 'magit-status-sections-hook 'magit-insert-unpulled-from-upstream)
    (remove-hook 'magit-status-sections-hook 'magit-insert-unpushed-to-upstream-or-recent)

https://arewedecentralizedyet.online/

In undergrad my iPad was far and away my favorite note-taking device. Digital pen-and-"paper" beats laptop for 99% of note taking.