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(To reproduce exactly the scenario being discussed, you fit a constant-only model to the data using least squares: that gives the average as the best fit. Then, you measure the leverage of each point of interest.)
> "content will be synced" -> "content is synced"
FYI that's still passive voice.
Some interesting stories about how these surveys work today and how they will work in ~10 years. Right now, it’s so rare to spot a weird thing in the sky that the alarms are all verified by grad students in graveyard shifts. When the new observatories come online, there won’t be enough grad students in the world :) so it’ll all be ML.
To Vardi's credit (the previous CACM EIC), CACM clawed back some of its technical chops in the 2010s. I wouldn't claim it's near the quality of 1970s CACM, but it actually has technical content in it again. Equations, even, gasp!
(I've used/done extensively the three mentioned things before, including blogging in a research context)
arxiv is a preprint server. Blog posts can, sometimes, play the same role as research preprints. Research papers are fundamentally about having a _structured conversation_ about a topic.
This paper is arxiv at its best.
(Disclosure: Quarto dev here) ..., like Quarto. You can use `great_tables` in code cells in Quarto to get great tables in your RevealJS presentation or website, https://quarto.org/docs/output-formats/html-code.html.
I was so amazed when I learned about it out ~10 years ago that I wrote a little interactive thing in javascript + webgl for it. I hope you'll forgive my self-indulging here: https://cscheid.github.io/lux/demos/beauty_of_roots/beauty_o...
With that said, Quarto works very well _with_ Jupyter notebooks. You can develop in them and then use them directly as inputs to our system. This is how, for example, Jeremy Howard and Rachel Thomas from fast.ai use it (https://www.fast.ai/).
We're for profit, but here's the relevant paragraph: "Together, RStudio’s open-source software and commercial software form a virtuous cycle: The adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in open-source software, which benefits everyone."
Speaking entirely for myself, this space is so important that I'm thrilled to have more activity rather than less. Quarto's great and Observable's great. I hope folks pick the tool that's best for their use case!
You'll want to use something like {.content-visible unless-format="revealjs"}
If y'all haven't seen the kind of magic that Tyler does with ray tracers in R (yes, you read it right), you're missing out. Go click on that link!
I don't know what forum you're referring to. We monitor our GitHub discussions very closely, though: https://github.com/quarto-dev/quarto-cli/discussions/
> How well does this work w/ a TeX-oriented editor? Say TeXshop?
Quarto can produce .tex output from .ipynb or .qmd inputs, which can then be further edited directly in your text editor of choice (TeXshop, or even something like overleaf) should you want to.
The scoping rules are by design and match .ipynb workflows in the case of multiple documents, so we're unlikely to change it.
The render latency of quarto is definitely higher than we'd love, but we have a plan and have been steadily improving it. Quarto 1.4 is generally about 20% faster than 1.3, and we have performance regression infrastructure to not let us slip on it.
The complication is from the implied dependencies. If you've designed from zero to be able to track the requirements everywhere in the code base, then that's (in principle, though still not trivial) possible. But if you're looking to ship fast with a small team on the large feature set that we do have, then it's actually a better call to work on a system that has a fixed set of dependencies known in advance, and quickly iterate to solve other customer problems.
tl;dr: tradeoffs. We chose one that still serves us well, but it does come with consequences.
My understanding is that Pluto has its own execution engine outside of Jupyter, and so would require the creation of a new "engine" in our codebase. We are a pretty lean team that has other priorities for 2024, but we would very much love to see Pluto running in Quarto.
There is an open PR right now (https://github.com/quarto-dev/quarto-cli/pull/8645) to add a Julia-native engine to Quarto, from the developers of Makie which we hope to merge soon. I don't think that will provide instant Pluto support, but it will certainly make it easier for other Julia-native folks to build on.
Folks have already noted that you can run content through any Jupyter kernel. That lets you run Python and Julia code in a familiar runtime environment, and without having to install the R runtime and dependencies.
I also think that our IDE tooling is pretty good. If you're running in VS code, we'll (for example) highlight problems in your YAML frontmatter, resolve document crossreferences in the editor, etc.
We do this in an editor-agnostic way, so the Quarto IDE tooling can be adopted by third-party. We (Posit the company) develop the RStudio integration and VS Code extension, but there exist modes developed by the community such as quarto-nvim.
A good way to think about Quarto is "a next-generation RMarkdown with many lessons we learned and an emphasis on multiple language support".
This is an area we want to improve in the future. Most of the JS libraries can be disabled in the project configuration. We find that our typical user prefers to have access to the features those libraries provide, but I agree with you that we should be doing better. This is a place where some guided documentation would help. We're working on it.
With that said, we dogfood Quarto pretty seriously, and consume the content from mobile devices. I admit that we use devices that are likely in the 90% percentile of speed, but website performance is something we do take into account.