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I've read on cppreference that with C++20, it's possible to omit specifying the type aliases reference, pointer and iterator_category when defining a new iterator. In this case, the corresponding aliases in std::iterator_traits will have "default" values. The details of this are a bit confusing to me, so I'm trying to split this topic into answerable questions. So, my first question is: Are all (or some) of these "default" aliases guaranteed to be correct?

As I understand it, the iterator_category tags are based on the C++17 named requirements. If an iterator satisfies the forward iterator requirement (but not the bidirectional requirement), its tag should be forward_iterator_tag.

I'm specifically worried about the distinction between forward_iterator_tag and input_iterator_tag. Forward iterators have to be able to be used in multipass algorithms, but I don't think the compiler checks that this is the case. Are there cases where I can know that the "default" aliases will be correct (e.g. an output iterator is always classified correctly, or the default reference alias is always correct)?

I'm a novice at ML but I'm trying to create a model to detect a few objects in my custom photos. Before training my model, I'd like to know if and how I should modify my images to improve its accuracy.

I don't have access to the photos at the moment, however, I can provide an example of the characteristics of the images I'll be working with:

1. There's a white piece of paper (so white background), and on it are a bunch of insects.
2. There are a few different kinds of insects, and they look unique from eachother (different colors, shapes, sizes etc.).
3. The camera is pretty zoomed out, so each insect is probably ~ 40x40 pixels (so it's not really high definition).

I don't know much about machine learning, but I'd assume that because the insects will be captured in low quality, the model will mainly end up relying on the general shape and color to distinguish/identify the insects (e.g. long or circular spot on photo, etc.).

Therefore, I was wondering if I should do anything to to the photos to achieve higher accuracy (before I train it). For example, if I increase the contrast in my photos, would the insect's borders be more defined and thus make it easier for the model to detect/identify them? Or, should I convert the images to grayscale or stick with RGB? Are there any other factors that should be considered? Any help will be greatly appreciated!

Edit: I'm not sure why someone voted to close this as opinion-based, however, I'm not asking for an opinion. I'm trying to understand more about image-detection process by learning what constitutes a "good" photo versus a "bad" one. Even though this sounds like it's opinion-based, it's not. For example, I'm sure having extremely low-light photos would be terrible for training models. This wouldn't be an opinion, but a evidence-based fact.

Similarly, I'd like to learn what kinds of general characteristics make "better" photos, such as if I should use high contrast, brightness, etc. I think this is an answerable question that is not opinion-based.

Please note: my question mentions MySQL, but it is a Docker/Docker Compose volume management question at heart, and as such, should be answerable by anyone with decent experience in that area, regardless of their familiarity with MySQL.

My understanding is that Dockerized MySQL containers, when defined from inside a Docker Compose file like below, will be ephemeral, meaning they store all data on the container itself (no bind mounts, etc.) and so when the container dies, the data is gone as well:

Please note: this question mentions Java and Java keystores, but in reality has nothing to do with Java and should be answerable by anyone with sufficient knowledge of Docker and writing Dockerfiles.

I have a web service (myservice) that is a JVM application that has been Dockerized. The Dockerfile for it looks like this:

NOTE: This is not a duplicate of any existing question, it's intended to show why such an extremely common and seemingly simple question is unanswerable and provide guidance on how people posting such questions can modify them to make them answerable so we don't have to keep providing the same guidance in comments almost every day and can just refer to this instead.

Given the following input file:

Please note: even though this question mentions MySQL and Docker, it really has nothing to do with either and should be answerable by any knowledgable bash scripter.

Rusty on my bash scripting here. I'm looking at the official MySQL 8.0 Dockerfile and see that it calls and runs a bash script called docker-entrypoint.sh. Looking at that script, it appears to (conditionally) run a snippet of code that creates a database, if it doesn't already exist:

I have some JSON (let's call it json1), and I want to combine it with some other JSON (json2). I don't just want to merge or append data, I also want to be able to delete data from json1 by embedding "commands" in json2.

Example

Is there a free way to use IntelliJ when pair-programming?

My question is similar to this question which is 10 years old with Remote Pair Programming in IntelliJ but I was hoping for a more updated answer since many links like screenhero on that question are now broken. I use special libraries that only exist in IntelliJ so using VSC Liveshare is out of the question and I have no where else to ask so please do not close this question for reputation points from closing a question, many people have this question and with COVID + transition to work online this is more important than ever.

SO has a perfect audience to see questions and answers like this one. Questions on the SO are not only about code itself but also SO should be a place for: "a specific programming problem" or "software tools commonly used by programmers" or "a practical, answerable problem that is unique to software development" In other words, I strongly disagree with closing questions like this, especially that these tools evolve. – Sławomir Lenart

This is somewhat of a broad topic, but I will try to pare it to some specific questions.

I was thinking about a certain ~meta~ property in Python where the console representation of many basic datatypes are equivalent to the code used to construct those objects: