superdimwit

https://closequarters.io/ This is similar to what you are describing, although development has sadly appeared to have stopped.

Shared memory format is supported in Julia too!

yes, it integrates with the VSCode plotting pane. Not exactly sure where this happens, though.

I would recommend Plotly.js in VSCode for interactive plotting.

For back office only

Thanks, this is interesting. Seems like it would be quite cheap to cobble together an ESP32 + cheap sensor. How would you power it - battery or solar? Any estimates of battery runtimes I could get if it woke up every 4 hours or so?

This is cool. Does anyone know any very low-power wireless sensors that I could put in, say, 10 plants and have them all talk to a single receiver (rather than having 1 RPi / plant)? Ideally the sensors would have mini solar panels (since plants would be in the sun anyway).

In the same way that the ReLU derivative is not defined at x=0. Most of the time, in practice, this all doesn't really matter and you can still get gradient descent to work in a useful way.

What did you make this with?

If these things interest you, check out Julia

This is clickbait / an ad

Why is this being posted? It was written 3 years ago, has been discussed before on this site, and the language has changed enormously in those 3 years.

Yeah. I think the benchmark game entries for Julia are unfortunately poor / stylised - IIRC there was a fork that delivered actually fast implementations (at the cost of some prettiness), but I don't think it ever got merged in.

I do agree that there a few cases where Julia in theory could do what you wanted, but it's a bit awkward to get the guarantee from the compiler that it will do so (e.g. SIMD this thing, constexpr this thing..). I do hope this situation improves.

But it has a Venn diagram so it's science!

Out of curiosity, what sort of code aren't you able to write in Julia that you can in Rust?

Thanks, this makes more sense.

Hmm, the first plot took me a long time to understand - way too much going on.

What's the difference between backcountry and tent? I feel like they overlap - I often camp with a tent in the backcountry..

I feel the circular plots make the data harder to read. I suspect some boring line plots with time of year on the X axis would be much more visually efficient

Yeah, but they're just benchmarking a wrapper around gzip. It looks to be around 300 LOC and does very little.. It's a bit deceptive. A large table of benchmarks across different domains and workloads would be more honest.

In my opinion, it's an unfortunate accident that Python became popular for numerical / data-ey workloads. It's good for some things, but fast low-overhead loopy code is definitely not one of them!

I think if you're just plugging together reasonably "vanilla" components from python / R libraries, and only using vectorised operations, those languages are fine and you can get away with using vectorised libraries wrapping C++.

The moment Julia shines is when your workloads can't be phrased by stringing together the limited set of vectorised verbs that python / r libraries give you: this is anything stateful and loopy like reinforcement learning, systematic trading, monte carlo simulations etc. It's also useful if you really care about performance and are doing "vanilla" computations at a truly large scale. If you want to avoid copying memory (i.e. doing vectorised operations), or want to tightly optimise / fused some numerical operations, it's great.

The other issue with python / r wrapping c++ libraries is that different libraries will generally not play well together (without coming out into python / r space, and doing a lot of copying / allocation). This tends to encourage large monolithic c/++ codebases like numpy and pandas, that are pretty impenetrable and difficult to extend / modify.

I'd really recommend anyone doing mildly numerical / data-ey work in python to give Julia a patient and fair try.

I think the language is really solidly designed, and gives you ridiculously more power AND productivity than python for a whole range of workloads. There are of course issues, but even in the short time I've been following & using the language these are being rapidly addressed. In particular: generally less rich system of libraries (but some Julia libraries are state of the art across all languages, mainly due to easy metaprogramming and multiple dispatch) + generally slow compile times (but this is improving rapidly with caching etc). I would also note that you often don't really need as many "libraries" as you do in python or R, since you can typically just write down the code you want to write, rather than being forced to find a library that wraps a C/C++ implementation like in python/r.

I presume you're aware of the heavy competition you're going up against? tribeco and the like

Came here to say the same thing. Annoys me when a (I thought) quite reputable site is so sloppy with their data presentation.

Well, it just has a cartesian namespace

I agree. I get the sense that a lot of these old text munging tools are seriously fast, and are mostly IO-bound. It would surprise me if moving chunks of text back and forth to a GPU would be very efficient.

...this assumes we're all the same person....

He's not talking about an object hierarachy, but more of a type hierarchy

This reminds of the classic early linear programming problem / study, the Stigler diet: https://en.wikipedia.org/wiki/Stigler_diet