nathanwh

“ The difference is in what I call shape stability. If the shape of a type is stable, then there is no way to mutate it such that a previously valid reference into it becomes invalid.”

From an application programmers standpoint, isn’t this much weaker? Part of the appeal of not having shared mutability is not having to worry about data races in general, while this shape stability approach prevents some classes of data races it requires others to be enforced by the programmer, as far as I can tell.

It’s hard for me to tell the net effect of this, maybe preventing reference errors is enough, but I suspect that once you start having to enforce other invariants manually, it will feel like the savings from the automatic shape stability enforcement are not enough of an improvement over languages without any significant compile time memory safety checks.

I think a more fair comparison would be that you hired a consulting firm to create a report and give you a summary of it, but you’re charged for the report itself separately from the summary, and you are not allowed to access the unsummarized report.

I use the same analogy explaining to people what my meds do. They’re a catalyst in that they lower the activation energy of doing anything other than doomscrolling

Thank you for this, I have wondered for more than a year why Google Maps would not show up when I searched for it.

Another happy aerospace here! IMO it does a great job with barely any configuration required (the default config works great, I have barely tweaked it over years of use), that said I’m not exactly power user of tiling WMs, I have one app per workspace 90% of the time

I don’t understand the screenshot, the “attacker” sent that to customers? Or Delve created this screenshot as a dramatic reenactment? The post is not clear

This reminded me of ViperGPT[1] from a couple of years ago, which is similar but specific to vision language models. Both of them have a root llm which given a query produces a python program to decompose the query into separate steps, with the generated python program calling a sub model. One difference is this model has a mutable environment in the notebook, but I'm not sure how much of a meaningful difference that is.

[1] https://viper.cs.columbia.edu/static/viper_paper.pdf

I only skimmed this but it has several things that make me suspicious. Mainly that they did not require a diagnosis for ADHD but instead separated participants based on a questionnaire regarding symptoms. Additionally their participant pool somehow contained more than 20% of neurodevelopmental disorders and other mental health disorders which seems very high.

Not only that, but 2,6,7 is also a set but not included in the results

Ah makes sense, thank you

Neat project! Maybe this decision is copied over from unreal engine, but instead of `ensure` and `ensure_always`, having names like `ensure_once` and `ensure` would have been more clear to me.

I did read that part, I just feel that the idea that somehow something good comes from removing the feather (which is negligible expense relative to changing the name), is just plainly saying that we care about inclusivity but only so long as it’s not expensive. Which is more or less the policy of every large organization, but it’s rare that it gets said.

I don’t know that I’ve ever seen such a short article so clearly demonstrate how much don’t actually care about the people who they are to trying to placate. They’re changing the feather because it’s apparently offensive to reference it, but they’re not changing the name because it would cost money. Even though the name is way more tightly tied to the people than the feather. I don’t actually care about the logo or the name I just find having the dichotomy so clearly spelled out interesting.

Settings > Accessibility > Display & Text Size > Color Filters > Greyscale

Previously your unit test was broken one day every four ish years. Now it's broken every day :)

> Name a language and I'll tell you a much worse issue.

Not sure if the offer was only open to OP but I'll bite. How about Java?

"68,000 iterations of an empty loop in a millisecond" The factor of 1000 comes from the conversion from seconds in the question, to milliseconds in the blurb.

Don't you have the same problem with the salt that you have with the water, no way to tell what dose everyone is receiving?

Alacritty being so slow is surprising to me here. I only use it on macOS, but it feels faster than kitty when I'm looking at application logs scrolling quickly across the screen. Perhaps responding to typing events has different latency than tailing a log file or listening to stdout?

Pedantic, but UUID v4 (which is the random one) only has 122 bits of randomness, 6 bits are used to identify the version and variant.

https://en.wikipedia.org/wiki/Universally_unique_identifier#...

Perhaps Carbonyl may be of interest to you?

https://github.com/fathyb/carbonyl

Something I've always wanted from my shell history is to be able to record relative filepaths as their absolute equivalent in the history, is that supported in atuin?. If you do a lot of data munging on the CLI, you end up with a lot of commands like `jq 'complicated_selector' data.json`, which if I want to remember the selector is good, but if I want to remember which data I ran it on is not so good. I could do it with better filenames but that would involve thinking ahead. I also run into this a lot trying to remember exactly which local file has been uploaded to s3 by looking at shell history.

This explains why "What if I move out early?" for the sample document doesn't mention any of the information in the lease break section, which is definitely the most important section for moving out early. Whatever space they're projecting the question into doesn't capture that "lease break" and "moving out early" are synonyms.

Something about how this article was animated out hurt my brain to look at. It had a lot on page telling me about how much there was too much information on the screen of the phone which was nested inside the page. And then every arrow press would move around the dialog boxes (some of which jiggled to fight for my attention extra hard) and update the little avatar. The content was interesting but it felt kind of ironic that a product psychology website was this mentally taxing to use.

Oh interesting. I skimmed that page to make sure it wasn't already referenced and ironically didn't recognize that the kanji were the same as the link I posted. More practice needed I guess.

There's also a stroke order font which is used in the Anki deck I have for learning kanji. The svgs in the post look great, but as far as I can tell there's not an easy way to copy the actual kanji character (you can get it from the actual SVG source however). The font is nice because as far as the clipboard is concerned it's just the character.

https://www.nihilist.org.uk/

Out of curiosity I tried chat GPT for this and it was able to generate a reasonable set of test cases but only after prompting it for including international access and "+" characters. I also had to prompt it to use the parameterize feature, but I guess that's personal preference.

Final test: https://pastebin.com/J36JY78i

First response: https://pastebin.com/S23GFPDq

Until someone makes the AI write the test too, I don't think there's going to be any red-tape style solutions to this problem that isn't just "no AI written code allowed".

I do agree that I see that comment a lot on here. However that seems to be a pretty uncharitable reading of the parent comment, as it seems they were more expressing surprise that burning the explosive is a legitimate means of defusing a bomb.

From the doc linked "Partitioning is done manually.", which seems like a pretty big limitation to me? For static data sure, but for non static data it seems like it would be an operational headache to manage manual partitions, compared to elasticsearch which partitions for you (although IIRC shard count is fixed)