Clickbait. Fame. Fortune.
My first attempt at the Ludum Dare Game Jam competition (#36). The theme: Ancient Technology, which is why this is a terminal-based game. That, and because I didn't have time to start from scratch learning graphics libraries and game engines for Rust.
The year is 2016. During the depths of the credit crisis, you took the only job you could find, an unpaid internship with PooSprinkler, one of the earliest clickbait sites. After years of toil trying to come up with fresh content and listicles, you worked your way up to editor-in-chief. With only a small budget for stories, you're reduced to begging others to create content for free.
But you have one superpower: the ability to write clickbait headlines which keep the stream of ad revenue trickling in just enough to keep the lights on.
And then it happens: Harambe, beloved gorilla, is assassinated.
This is your time to turn your media empire around.
Spend your story budget wisely. Write great headlines. Know your audience. Get clicks, get paid.
A rise goes up from the internet: Clicks out for Harambe!
- Rust
- ncurses-rs
- Currently only builds for Mac OS X.
To build and run:
git clone https://github.com/csf/clicks-out-for-harambe
cd clicks-out-for-harambe
cargo build --release
./target/release/clicks-out-for-harambe
To build and run with the intro text skipped, use:
cargo build --release --features=skip-intro
The game mechanic for Clicks Out for Harambe, which won't be fully realized during the weekend Ludum Dare Jam, is to use a suitable machine learning algorithm against a corpus of actual clickbait headlines to model the likeihood that various demographic segments of a non-player character population would click on a given link, which the player then probes by publishing various stories with features that generate clicks, with time and resource constraints applied. The human player is trying to find an optimal strategy for published story features against these overlapping demographic models.
The same game mechanic might also work for a game based on a presidential election, where as a candidate or campaign manager, the player tries to find an optimal messaging strategy for soundbites.
The fun part, from a game design perspective, is trying to understand how to derive text features that result in different models for different populations.
I picked Rust as a programming language, not because it is particularly well-suited to game development, but because I like it, and it has some good libraries I want an excuse to explore in terms of visualization, machine learning, text processing, and (eventually) audio.