tomtom1337

Any suggestions for «orchestrating» this type of experiment?

And how does one compare the results in a way that is easy to parse? 7 models producing 1 PR each is one way, but it doesn’t feel very easy to compare such.

You need something "more" on the website before you ask people to create an account. "Team workspace that stays fast" isn’t clear enough for me, at least. What is a workspace? What does the interface look like? Is it in the browser? Is it an app?

People will go "what is this?", "huh, I’m not gonna make a user for this, can’t tell what it is". Those are my 2 cents.

Haha, I started reading this, got interrupted, came back and got confused by the graph. Then came to the comments, saw your comment, reloaded the post and voila!

Thank you for a lovely post!

They mean «this kind of demand/leadership» no longer exists. Not the particular feature of the wake up time.

Oooh, thank you for sharing! New product lineup looks interesting, but I echo other concerns here about it thread maybe eventually requiring internet.

Are the ikea zigbee devices going to stop being sold? Massive shame if so, they are extremely reliable and easy to use.

Super quick feedback - opening that link on my phone shows me two options next to each other, seemingly with the same name / description (followed by …) and same pricetag. I had to turn my phone sideways to see that there is a windows and a Mac version.

I think you can afford the extra characters to show the whole page in portrait mode. (iPhone 16 pro Safari)

https://imgur.com/a/aTxO3sp

Could you provide a bit more context here? I’m looking at the math example (https://github.com/ohmjs/ohm/blob/main/examples/math/index.h...) and would like to learn a bit more.

If you’re young, new to data science and hoping to get a job in it after some time, then I absolutely recommend learning Python instead of Julia.

But if you are just interested in learning a new language and trying it in data science OR are not currently looking to enter the data science job market, then by all means: Julia is great and in many ways superior to Python for data science.

It’s just that «everyone» is doing data science in Python, and if you’re new to DS, then you should also know Python (but by all means learn Julia too!).

Look, this article is absolutely excellent, and answers your questions. Please read the article before commenting this sort of thing.

As someone who has had to use geopandas a lot, having something which is up to an order of magnitude faster is a real dream come true.

Ah, of course. Slightly ambiguous English tricked me there. Thank you Ritchie!

Out of curiosity, what makes a rust library easier to use? Could you expand on that?

Why would it be difficult to monitor the slowness? Wouldn’t a million function calls to the from_F_to_K function be very noticeable when profiling?

On your case about swapping between image representations: let’s say you’re doing a FFT to transform between real and reciprocal representations of an image - you probably have to do that transformation in order to do the the work you need doing on reciprocal space. There’s no getting around it. Or am I misunderstanding?

Please don’t take my response as criticism, I’m genuinely interested here, and enjoying the discussion.

I interpret your question as «given that I am doing many conversions between temperature, because that makes it easier to write correct code, then I worry that my code will be slow because I am doing many conversions».

My response is: these conversions are unlikely to be the slow step in your code, don’t worry about it.

I do agree though, that it would be nice if the compiler could simplify the math to remove the conversions between units. I don’t know of any languages that can do that.

Relevant xkcd: https://xkcd.com/2205/

You have a typo: In your last sentence you effectively wrote «from int to float» twice in contradicting ways. «To float from int than (…) from int to float».

Could you expand on this? It sounds a bit preposterous to save a text, as json, inside an image - and then expect it to be immediately usable… as an image?

I suggest you wrap the misconceptions in quotation marks to make it clearer what is the misconception and what is not. Took me two passes to realize what was what.

I echo the other response here. You absolutely should switch to using notebooks in VSCode with their static typenchecker. Language Servers do exactly what you are wanting, with both notebook integration and «live coding».

Turn it off?

This resonates strongly! In Python, I often now find myself declaring Pydantic data structures, and then adding classmethods and regular methods on them to facilitate converting between them.

It makes for great APIs (dot-chaining from one type to another), well-defined types (parse, don’t validate) and keeps the code associated with the type.

Which GPU Are you running on? I just got a 4080 SUPER and I’m thinking of trying Linux with it.

I use mise with uv for automatic activation of venvs when I cd into a directory containing one (alongside a mise.toml). Do you tackle this in some other manner?

Ive used this several times, I paid for pro. Really like it! Check out the hand recognition, it’s super cool (albeit, not terribly useful).

There’s a very active and helpful community on discord too.

I didn’t understand this. What is it about the gamma factor that will cause the cosmic microwave background to evaporate you?

I would have liked to see the performance of the ash c version, seems like a surprising omission.

I’m also confused about the perf2 performance. For the threads example it starts around 70_000 reqs/sec, while the processes example runs at 3_500 reqs/sec. That’s a 20 times difference that isn’t mentioned in the text.

Same on iPhone, Safari.

If you’re looking to analyse your hyperspectral images (spectrum-images, image-images or n-dimensional- n-dimensional datasets), I can highly recommend hyperspy [1].

One of the brilliant ideas hyperspy incorporates is that we consider datasets to have a navigation dimension and a signal dimension (think, you measure a spectrum at each point on an image), and you can easily transpose between them. This means that you can «move around» on the image and see what the spectrum looks like, or transpose and see what the image looks like as a function of the spectrum.

In particular I think the model building, where you can fit components to your dataset, is really useful.

It works best with the Jedi LSP - pyright doesn’t support the way we added lazy loading / extensions to the base hyperspy package.

[1] https://hyperspy.org/

I enjoyed reading your solutions! I’m surprised how terse they are!

Rather than the snarky response, I’ll answer: closer is likely to be impossibly more expensive for the requirements OP needs (e.g. living area, number of bedrooms or similar).