fantod

I don't really see how this is a reason not to carefully consider the type of an object when using truthiness.

For the basic sequence types (list, tuple, and range), len is definitely not expensive to compute. For custom types, it will depend on your implementation of __len__ (but then, computation of bool(...) will also depend on your implementation of __bool__).

If you know that "something" is a sequence, then is the idiomatic thing to do. The point is that any time you rely on truthiness of a value, you need to think (sometimes quite carefully) about what type of value you're dealing with.

To be fair, using "if something" in Python is pretty much always a good way to trip yourself up.

Yeah, the Python language favors an imperative style due to the absence of features necessary for writing more functionally, like lambdas that can do more than return an expression, tail call optimization, and lexical scope. This last one, additionally, tends to make people use classes for essentially no other reason than that closures don't work natively.

> My favorite example is: ','.join(['a', 'b'])

I've always found the discrepancy between this example (instance method call), list(('a', 'b')) (technically a method call, but resembles a functional call), and str.lower('AB') (class method call) to be quite maddening.

How much coffee were you drinking? I have 1-2 cups a day (for many years) and always felt fine.

Unhealthy food can be hard to avoid.

You made me realize, I think of the brackets as saying "where". vec[2] is vec where the index is 2. vec[vec > 5] is vec where the entries of vec are greater than 5, etc.

Both are overloaded (__gt__ and __getitem__). My question to you is: How would expect this to be written (in Python)?

This lines up remarkably well with something I've thought about myself. People often argue about what the "best" first programming languages to learn is. I think it depends on what your goals or interests are:

1. Interested in math and computer science? Start with Scheme. 2. Want to be an engineer? Start with C. 3. Want to develop a product (or just general interest)? Learn Python.

These categories line up precisely with the article's three tribes: Mathematician/poet, hacker, and maker.

Why would that even be blocked?

Opening in private mode usually works for Medium link (never understood why they reward people who hide their cookies, but ok).

Itamar Turner-Trauring's blog has a number of good articles on Docker best practices:

https://pythonspeed.com/docker/

Yes, I understand that Docker caches layers. Like I said, if docker system prune is clearing a huge amount of space, it's like a large number of different images and containers that might not share layers. If you're build, running, starting, and stopping containers all day, you might not be terribly careful about exactly what you're doing each time. That's why system prune is so useful.

So maybe I was quick to comment, but I glanced at the images I have sitting around and python:3.7 is 876MB. However, python:3.7-slim is 112MB (still nearly twice the size of the image you describe, though). But it's conceivable that many people running into limitations with the slim image would just opt for the full Python image instead.

Maybe a better example is the Tensorflow image, which is close to 4GB for the GPU version. It's also the kind of thing that would be a pain to rebuild from scratch if you wanted to find a way to save space.

Agreed, there are many things to get right about using Docker. But if you're just deploying an app to a somewhere, does it really matter if the image is a bit bloated (within reason)?

I mean it could just be very many containers that add up. And in many cases you'll have loads of images close to 1GB unless you go to great lengths to try and shrink them (e.g. anything that uses Python).

If you're referring to the monkeypatching, I would generally try to avoid it unless but that doesn't mean I wouldn't ever do it. Not sure why you would do this in this example though.

Yeah, these are great.

This is great! There aren't enough references on Python internals, I find.

Not sure you're at all interested in going in this direction, but I (and likely many people) would be really interested in a similar explanation of NumPy internals.

Sorry, I was referring to the assertion that "they wouldn't share the feedback on her paper".

I think the logic is that the paper would need to be retracted, rectified, and then resubmitted. Since the submission deadline was already passed, resubmission would be impossible.

From Dean's email:

"A cross functional team then reviewed the paper as part of our regular process and the authors were informed that it didn’t meet our bar for publication and were given feedback about why. It ignored too much relevant research — for example, it talked about the environmental impact of large models, but disregarded subsequent research showing much greater efficiencies. Similarly, it raised concerns about bias in language models, but didn’t take into account recent research to mitigate these issues."

Is it true that this information was not shared with her?

Not every lockdown entails tracking of individuals.

IIRC the number of hidden units required scales exponentially in the desired approximation error.

Agreed, I find this to be a great use case for voice assistants. Or on the occasion I feel like napping after lunch "Hey Siri, wake me up in an hour".

I'm not a web developer but whenever this kind of thing happens I just wonder why there isn't a single standard library in every common web language to deal with this and if there is, why it's not being used more often.

Really? My only experience with Julia was porting a simulation from Python and the speedup was incredible. Admittedly, that's an instance of numerical computing, but I would wonder why it was slow for you.

Why don't you just exclusively subscribe to the subreddits that interest you?

Have you actually met a person from Europe? They play on stereotypes constantly.