- https://xenodium.com/an-ios-journaling-app-powered-by-org-plain-text - Lately, I'm having a go at building a privacy-focused plain-text-based iOS journaling app. I starte building it for someone important in my life but now using it myself. - https://flathabits.com - After reading Atomic Habits, I wanted a habit tracker but most had more friction than I wanted, required accounts, had distractions, lock-in etc.... - Source: Hacker News / 6 months ago

After reading "Atomic Habits", I tried a bunch of iOS habit trackers and none of them worked for me. They often wanted me to log in, had a social component, a game, analytics, or some form of lock-in. In the end, I built my own without any of this: https://flathabits.com. - Source: Hacker News / 11 months ago

- A ChatGPT shell that integrates well into my editor of choice https://xenodium.com/chatgpt-shell-available-on-melpa - A scriptable screenshot/video capture utility https://xenodium.com/recordscreenshot-windows-the-lazy-way - An iOS habit tracker that's neither cloud-based, nor needs an account, social, wants my attention, data, etc. https://flathabits.com - An iOS scratch pad that removes further friction than... - Source: Hacker News / about 1 year ago

Not quite. https://plainorg.com and https://flathabits.com both get daily downloads. Source: over 1 year ago

After reading Atomic Habits, I built my own tracker for iOS https://flathabits.com It’s straight to the point, skipping the nonsense. No accounts, login, social, analytics, lock-in, stealing your attention… Privacy-oriented and frictionless. Source: over 1 year ago

TL;DR: The authors pretrain the model to classify images into Wordnet synsets[a] that appear in the caption, using a standard Cross Entropy loss. They keep the number of classes relatively small by removing any synsets that don't show up in captions at least 500 times in the dataset. It seems to work well. My immediate question is: Why not classify among the entire hierarchy of all Wordnet synsets? --- [a]... - Source: Hacker News / about 2 months ago

To operationalize this intuition, the Microsoft and UC Berkeley researchers use WordNet and Wiktionary to augment the text in image-text pairs. The concept itself is augmented for isolated concepts, such as the class labels in ImageNet, whereas for captions (such as from GCC), the least common noun phrase is augmented. Equipped with this additional structured knowledge, contrastively pretrained models exhibit... - Source: dev.to / 3 months ago

If you like this, definitely check out WordNet (https://wordnet.princeton.edu/). - Source: Hacker News / 6 months ago

I didn't understand well what you meant, but maybe this site can help you: https://wordnet.princeton.edu/. Source: over 1 year ago

What I'd do is work with a huge database like WordNet and then try to "extrapolate" BIP39 to 4096 words by creating queries against WordNet to obtain words meeting the constraints you'd like to keep. Source: over 1 year ago