I followed a few Twitter accounts that post yuri photos, and I saved some of these images when I saw them. 女の子になりたい！

However, it would be better if there is a bot that could do all this for me. And of course, applying for a Twitter developer account is a must. When your developer account is ready, you will need to generate a consumer key & secret pair.

This program will be really simple and straightforward. It reads from a config file that contains the consumer key & secret pair, as well as a list of usernames you interested in, and the path to the directory where you'd like to save images. A sample of the config file is shown below.

{
  "key": "KEYKEYKEYKEYKEYKEYKEYKEY",
  "secret": "SECRETSECRETSECRETSECRET",
  "img_dir": "imgs",
  "users": [
    "username1",
    "username2",
    "username3"
  ]
}

Substitute your own consumer key & secret, and then list some users you interested in in the users array.

Also, I don't want the program to be too complex, so I decide to not use any database. If you want to add more users to the list while the program is running, then just add them into the JSON file and save it. The program will try to reload the user list before the next round of polling data.

And this program is on OwO now! https://owo.ryza.moe/. You can download and install it on Linux with only one line command. (For more details about OwO, you can refer to this post, Rust Learning from Zero (23) —— The OwO software distribution system!)

bash <(curl -sSLf https://owo.ryza.moe/twitter-img-saver/install.sh)

I believe that for almost anyone studying in computer science would know this famous problem below.

The travelling salesperson problem asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?" Wikipedia

I know that there are thousands if not millions approaches that we can use to solve this problem. But just for personal notes and demonstration, I will solve TSP using one evolutionary approach with Rust.

The idea of today's evolutionary approach (EA) is rather simple:

1. Randomly initialise some solutions and compute cost (described as fitness in EA) respectively.
2. Mutate one of them, compute the fitness of mutated solution. If the new fitness is worser than the last one, go step 3. Otherwise go step 4.
3. Reject this mutation and go step 2.
4. Accept it as a solution, discard the worst solution and repeat step 2.

For $n$ nodes in a path, there are maximum $n!$ (actually, $(n-1)!$) paths to select from. Therefore, if what we want if the global optimal, then this algorithm has to iterate all possible paths, and that gives us $O(n!)$ performance. However, EA could actually gives us a quite good suboptimal in much less time because of the way it works. One use case of EA is when we only have limited time, computation performance and we can accept suboptimal as a solution.

For example, if we have the following adjacency matrix in TSP,

Then we solve this problem with EA, the output could be as below.

Initial solution [0]: fitness => 33, seq => CABED
Initial solution [1]: fitness => 32, seq => CADBE
Initial solution [2]: fitness => 34, seq => CDEAB
Initial solution [3]: fitness => 32, seq => ABDEC
Initial solution [4]: fitness => 33, seq => ADEBC
[0] Reject mutation: fitness => 38, seq => DBEAC
[1] Reject mutation: fitness => 34, seq => CDAEB
[2] Accept mutation: fitness => 33, seq => CEADB
[2] Worst solution dropped: fitness => 34
[3] Accept mutation: fitness => 28, seq => EDABC
[3] Worst solution dropped: fitness => 33
[4] Reject mutation: fitness => 33, seq => CBDAE
[5] Reject mutation: fitness => 38, seq => ACDBE
Best solution so far: fitness => 28, seq => EDABC

As you can see, EA found quite a good solution with fitness 28, which is EDABC. Also, this is in fact the global optimal and EA actually found the global optimal in the 3rd mutation!