Warning: Output may be nsfw/offensive
This is due to being trained on real demotivators from open directories, some of which seem to have come from old /b/.
~6000 demotivators were gathered, and then split into image and caption fragments using Go, which were labelled / OCR’d by Google Cloud Vision. The resulting text blocks were then used to train OpenAI’s GPT-2 language model.
GPT-2’s output then likewise consists of image labels and captions. The labels are searched on DuckDuckGo images to find a suitable picture, to which the captions are then appended.
Ripcord is an amazing alternative Discord client, but its notifications support is rather lacklustre, displaying them only for DMs.
Since it’s closed source shareware, I can’t go in and tweak it to my liking.
So, this daemon displays notification following Discord’s own settings in a superior way.
Done as my final project for a Computational Morphology class. Quite limited (e.g. no strong verbs), but still handles things like double U-umlaut.
Backend is a Python + Flask app interacting with HFST to output JSON.
I couldn’t find a Finnish ➔ English dictionary for Kindle that supported in-book definitions, so I wrote this program to generate one from Wiktionary.
Chiefly an exercise to learn Vue.js and how to perform an OAuth flow without a dynamic backend.
Since WoW addons have no filesystem or network access, it does this via a thin strip of pixels in the corner of the screen that are then read by a Go program.
Because Wiktionary’s mobile site is bloated.