#botally

Most Popular Twitter Bots

I got sent a link (by @botwikidotorg) to this post, and I thought it was interesting enough to share.

There’s a lot of fun bots on twitter, some of which I’ve talked about here. But I didn’t have a good sense of what the upper limit was for a bot’s follower count. (Turns out it can be 1.5 million people, though most are under 400,000. Still more popular than me.) Most reach for least posts goes to @year_progress, which informs me that the year is at ▓▓▓▓▓▓▓▓▓▓▓▓▓░░ 84%.

For my current students, note that the blogpost is using Glitch to generate the interactive charts that are embedded in the post.

I knew for a fact that for volume 3, I had to cover a twitter bot. Come hang with me and starlit void for a while and see why---

The boom of Creative Writing Twitter is a natural extension of how we communicate today: quickly, constantly, concisely, urgently. But short doesn’t mean simple: following the ethos of writing (or subverting) formal verse poetry, the restraints of Twitter often produce the most creative content. Among them are many creative writing-oriented twitterbots. These clusters of code generate tweets following a certain linguistic--and sometimes also visual--structure a set amount of times per day. Some results are "better” (more beautiful, or more hilarious, or more surprising, or more mundane, or more....) than others, but it’s always enchanting to watch unfold. The dependable, structured presence of twitterbots--however unexpected the results---on our feeds makes them eventually feel like a friend---oddly human. One creative writing bot that stands out to me is starlit void’s quietscape--the bot pairs a colorful, randomly generated, geometric digital landscape picture with a short, fantastical suggestion/description. Each tweet creates an environment for thoughts to exist in, like a creative writing prompt. At least for me, it serves an essential meditative function within hectic internet space. I knew that my conversation with starlit void would be a rad discussion about writing and tech, but it bloomed into so much more: an oral history of bot world, seriously cool meditations on mental health, Soundcloud playlist suggestions, + more! Keeping with the futurelit tradition (and my own personal tradition), we avoided a phone call and did our chat over Twitter DMs this time:

what is your favorite environment to create in? (whether it's a certain physical space, listening to a certain kind of music/silence, etc.) 

i typically like to be well-caffeinated, alone or in a cafe, & excited about getting something working.... there's a thin line between excited & stressed about how something is going to turn out. i used to go to "game jams" until i discovered it was actually really stressful for me. i'm trying to be more relaxed about my creative output (this is easier said than done) & trying to avoid equating prolificness w/ human value. i think i do my best work when the intended audience is very selective, even 1 or 2 people, or just for myself. i also listen to what i call "robot music" a lot, for example this sort of mix.

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Bottery

In exciting news for bot-makers, Kate Compton’s advanced bot-making agent platform was just open-sourced by Google.

You can play with a live demo here.

Kate’s earlier project, Tracery, already powers thousands of bots and other generative things. The combination of ease of use and expressive power opened up grammar-based generativity to everyone and lead to an explosion of creativity.

Bottery takes the concepts of Tracery and puts them within a Finite State Machine, greatly increasing the expressive power available. While some advanced Tracery features add the ability to remember some state, using FSM--which are all about state--makes previously difficult things trivially easy. And with an accessible interface, too.

I fully expect that, given time, Bottery will be as significant for bringing AI to all of us as Tracery was. Meanwhile, you can use it for your ProcJam project. Or, with a bit of work, for your NaNoGenMo project!

There’s going to be an online launch party on the 8th, where Kate will be streaming and doing tutorials.

Noisemaker Bot

I was poking around the internet, looking for good uses of procedural noise. (There are fewer comprehensive catalogs than you’d think.) And I came across Noisemaker Bot, a twitter bot by Alex Ayers that is combining various noise generators and functions to create patterns. Interestingly, it represents images as 32-bit tensors, which is, I think, rare in game applications but more common in scientific fields.

The bot has already generated a wide range of different designs, assembled from its large catalog of operations.

Noise-based patterns like these are more useful than just being pretty things to look at: deterministic, stateless noise pattern generation can be continued indefinitely while smoothly transitioning between any two given points, making it perfect for things like generating terrain. Or adding a bit of jitter to a procedural animation to give it life. Or animating screenshake. Or to pattern a fabric. Or to adjust timing delays on a data visualization to give it a more organic feel.

Any structured, quantitative signal can be used as an input to drive a whole host of different things, which is why I keep talking about unusual inputs. Generation needs structure, but that structure doesn’t necessarily have to be a realistic replication of anything.

@brutal_exe

In the same vein as my appreciation of Nail Polish Bot, I also like the way that Martin O’Leary’s @brutal_exe twitter bot generates a consistent aesthetic with its 3D explorations.

The generator uses a path tracer that renders views of fractal architectural forms. The grainy side-effect is re-purposed as part of the Brutalist, low-fi phorograph aesthetic.

I think this intersection of the source material and the frequency of image generation helps to maximize the interesting image content. The fractal forms have many interesting shapes, and the camera moves just enough to have most of the images be both unique and striking.

Martin was partially inspired by Íñigo Quílez. who has made a lot of procedural generation and demoscene works.

Inspirobot.me

I have no idea how Inspirobot is generating it’s seemingly endless depth of inspirational memes. Templating? Markov chains? Neural network? Tons and tons of hand-edited entries?

If I had to guess, I’d say that a template seems the most likely, based on where the cohesion breaks down: it seems to have a good grasp on the grammar of the inspirational image, which most Markov chains are bad at. And a character-based neural net would likely make up some words on occasion, unless it had much more training data and lower entropy than this seems to.

If I had to implement something like this, I might try a meaning-swerving approach, using something like ConceptNet or WordNik to find substitutes for words in inspirational phrases, but a massive Tracery grammar might be faster to implement. And, of course, you can mix and match some of these to get the input data for the templates.

If anyone knows how it actually works, do let me know.

Meanwhile, I think I’ve found a new motto for the blog...