Note: This is not maintained. It was a fun weekend project a few years ago, and @showmepixels is still running, but I don't work to improve it and it's only a matter of time before Twitter or Tumblr change their APIs so it doesn't work. The code is left up for the curious.
ShowMeMore is an automated researcher. Given a list of tags to start with, it goes hunting for images, and over time grows its model in response to reactions, slowly reaching out to find things you weren't aware you already liked.
It started as an attempt to recreate @Archillect; you can read more about how it came to be and the motivations behind it here.
Currently, it's configured to be run as a Twitter bot pulling images from Tumblr and Flickr.
Currently setting up your own bot involves a lot of manual steps; I cannot pretend it is anything but tedious. Before getting started you should be familiar with concepts like API keys and have at least a little experience in the Unix shell. (If someone wants to automate more of this it would be much appreciated.)
While the technical steps don't take a lot of time, you'll need to spend a while evaluating early posts to nudge your bot in the right direction, which might take a few days.
Create a Twitter account for the bot to use. I would be flattered if you called
it showme<something>, but anything will do.
Have the account follow your main Twitter account; you'll send DMs to control it after it's running.
It's fine to make the account private; in fact, I would recommend keeping it private until you make sure the posts are what you're going for.
You should tweet once via the web client so Twitter knows a human is fiddling with the account.
You need to use at least one of Flickr or Tumblr as a source for posts.
You can register a Tumblr API key here. They will give you two API keys; the one you care about is your "OAuth Consumer Key". We'll use it later, so just copy it somewhere for now.
You can register a Flickr API key here. Flickr will also give you two keys; you'll need the longer one.
You also need a Twitter app, which you can register here. The app will need to request permission to read and send DMs, which is not the default setting, so be sure to specify that.
You'll need to clone the repository and perform a few more steps. You need Python 3 installed. The only library dependencies are requests and twitter.
git clone git@github.com:polm/showmemore.git
cd showmemore
pip install -r requirements.txt
echo mybot > name # use your bot's Twitter handle instead of "mybot"
mkdir out # downloaded images will be saved here
You'll need to stash your Twitter App credentials in the directory for the bot
to get. twitter-creds.json should be a JSON file with key and secret
fields, as indicated by your Twitter app info.
At this point, have a browser open and logged in to Twitter as your bot. We're going to connect the application to the account. Run the script like this:
./laser.py initdb
This will do a couple of things. First it may open a web browser, possibly in your terminal; kill that and a Twitter URL will be displayed. Open that while logged in as the bot account, authorize the application, and post the numeric code you get into your terminal. After that it will initialize the database, and you're almost ready to go.
Using your normal Twitter account - the one the bot is following - it's time to give the bot some information to get started with. The bot recognizes several commands:
- key: Set an API key for a source service. Currently, valid service names are just
flickrandtumblr. Example:key flickr [your api key] - seed: Assign points to a tag, used to get the bot started. Example:
seed some cool tag - ignore: Don't use the given tag to pick posts. (Posts with the tag won't be banned, but it'll never be used as a starting point to look for candidates.) Example:
ignore some bad tag - ban: Ban posts from a Tumblr blog. The "blog name" is usually the
exampleinexample.tumblr.com. Alternately, to ban a Flickr user, use the formatflickr:[user-id], where theuser-idis the automatically assigned Flickr user ID (looks like12345678@N00, not a normal username). Posts from a banned blog will never be selected.
The seed, ignore, and ban keywords can be prefixed with un to undo
them. Don't quote arguments to commands, it'll just confuse the bot. For Tumblr
tags, use spaces or dashes the same way Tumblr blogs do - the API treats them
the same, but it's better to use the more common form for seeding or ignoring
to avoid confusing the bot.
To get started you should set at least one API key and at least five seeds. I'd
also go ahead and ignore a few overly general tags, like art, gif,
tumblr, and ifttt.
We're almost done. The script needs to be run repeatedly in order to post. Here's an example cron entry to run it every ten minutes:
*/10 * * * * /home/you/code/showmemore/post.sh
The post.sh script just handles logging and encoding. Try running it manually
once to be sure everything works. The bot should reply to each DM you've sent
to let you know it was processed. If it posts successfully, it will add a line
to the log file in its script directory consisting of JSON that describes the
post. One field in this JSON to look out for is origin - while every aspect
of a candidate is judged, the origin is the aspect that was used to find the
candidate in the first place. Checking the origin is a good way to understand
what's going on when the bot surprises you.
At this point the bot should be running successfully, but it doesn't have much of a model to go on. My suggestion would be to let the bot post ten or twenty items, then favorite the ones that match your image of what the bot should post. This will give it more potential sources to draw from when posting. After that watch it for a day or two, favoriting good posts and building up your ignore list, to guide it to what you want it to be.
If you don't like cluttering up your favorites list, use the bot to favorite its own posts. You can also reply to posts from any account the bot follows and every emoji star (⭐) will be counted as an extra like; you'll know it worked if the post is automatically favorited.
Good luck, and have fun!
The algorithm for selecting posts is simpler than you might think. It doesn't use anything you'd describe as artificial intelligence, and the actual visual properties of candidate images are never considered. Its lack of sophistication means it falls down spectacularly sometimes, but it does have some advantages - calculations are fast and don't require large banks of data.
Overall, the algorithm is a bit like Pagerank working on photo metadata.
"Aspects" are true binary properties of a post - tags and authors (or at least
source blogs) are the most important aspects, but liking or reblogging users on
Tumblr and photo groups on Flickr are also aspects. Aspects have a type such as
tag, author, reblog, liked, flickr-pool, and a value that identifies
the relevant resource.
When a tweet by the bot is liked, that's treated as a vote for every aspect of the source post. (RTs are just treated as multiple likes for scoring purposes.) Votes are used to calculate two kinds of scores: all-time historical score and per-post score. So if an aspect has been present in 10 posts that have together gotten 500 likes, it might have an all-time score of 500, but a per-post score of just 50.
In actuality, it's a little more complicated than that - points a tweet gets are divided equally between all aspects on the tweet. So a tweet with a source with many tags will give fewer points to each.
Anyway, regarding how points are used, the algorithm has three phases:
- Candidate Gathering
- Culling
- Post Selection
In Candidate Gathering, per-post scores are used to make a series of weighted random picks of aspects. These aspects are used to query source APIs and get posts to look at.
In Culling, items that have been posted before or are from banned blogs are removed from the candidate list. This is the simplest phase, but it's important to keep the bot from going in circles. How to determine if two posts are duplicates is also a bit subtle; the current design errs on the side of posting duplicates sometimes while being simple in implementation.
If no candidates are left at the end of culling, the algorithm returns to the Candidate Gathering phase. Otherwise, it proceeds to Post Selection.
In Post Selection, candidates are scored for all their aspects based on all-time aspect scores. Then the highest-scoring post is made into a tweet, and the aspects attached to the post are recorded in the application's database.
These three steps are repeated every time the script is run.
See the issues page for small things.
A bigger change that would be nice would be generalizing the program to work on webpages rather than Tumblr and Flickr API items, as a kind of guided Pagerank.
Kopyleft, All Rites Reversed, do as you please. WTFPL if you prefer.
-POLM