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egl2020

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I find most of my references while browsing, and capture from the browser is Zotero's winning feature for me: automatically save the pdf and extract the bibliographic data. And while reading a paper, I get a search engine to look up its references and repeat.

The rest of the interface is ok.

Come back when your computer is in a room with a raised floor (for cabling), tremendous AC, and windows in walls so a passer-by can look in and see the tapes rotating. Geezer here.

This also works in drawing and painting. One of my painting teachers used to admonish us: "copy, copy, copy".

I'm a pretty conservative emacs user, partly because I don't want to spend time tinkering to get things to work consistently across Windows, ubuntu, and mac os -- all of which I use daily. My most "modern" adoption is probably using lsp and eglot with various language modes, notably golang and rust.

Should I consider adding tree-sitter into the mix?

Similar thoughts here. That was when I realized the potential of the Internet: I didn't have to be a grad student at a tier 1 research university to learn about the frontier.

I have a Kelty BB5 that I purchased in 1972, and I used it as recently as 2025. On family trips where you end up carrying odd loads like two sleeping bags or outsized tents, it beats the internal frame packs I use for fast and light trips.

I went the other way: from a Pixelbook to a Macbook Air. I mostly do SW development in the CLI, so the Linux subsystem on the chromebook was fine, as is macports/homebrew/etc. on the mac. I would still be using the Pixelbook if I could have replaced its battery. The low-end Air had good price-performance tradeoff, and the Neo would probably be today's choice.

The Greenland shark appears, if I remember correctly, in her book "Golden Mole", which is about many interesting creatures. This is published as Vanishing Treasures in some countries. Her "Super-Infinite: The Transformations of John Donne" is interesting and also not a children's book.

I enjoyed Werner Herzog's "Encounters at the End of the World" at many levels, not the least of which was how different it was from "Aguirre, the Wrath of God".

I used to ask myself the same question, but then I realized that for these people it doesn't matter how much they spend. When you are worth billions of dollars, the difference between spending $10M or $50M on your home Does Not Matter. You still have many other $M to spend on other things. It's perfectly rational for them to spend what seems like a large amount of money for an apparently small marginal improvement.

"You can learn anything now. I mean anything." This was true before before LLMs. What's changed is how much work it is to get an "answer". If the LLM hands you that answer, you've foregone learning that you might otherwise have gotten by (painfully) working out the answer yourself. There is a trade-off: getting an answer now versus learning for the future. I recently used an LLM to translate a Linux program to Windows because I wanted the program Right Now and decided that was more important than learning those Windows APIs. But I did give up a learning opportunity.

There's a mystique around Mathematica's math engine. Is this groundless, or will you eventually run into problems getting correct, identical answers -- especially for answers that Mathematic derives symbolically? The capabilities and results of the computer algebra systems that I've used varied widely.

The surge in laptops contributed, too. The opportunity or need for expansion cards, additional memory or storage upgrades, and peripherals disappeared or shrank.

I used to think of the sales staff as the United Nations of Fry's. It was always thrilling to see someone starting their American dream, even if the service was haphazard.

I was at G when "mobile first" was the slogan, and it led to "odd" choices such as designing and leading with a travel app rather than the web site. Perhaps locally suboptimal, but in the long run brutal forcing functions were needed to move a company as big and successful as Google into something new. I hear that going all-in on AI was internally disruptive and probably had some bad side-effects that I'm ignoring, but in hindsight it was the right thing to do. When ChatGPT, perplexity, and you.com came out, my immediate thought was "Google is toast", but they've recovered.