Bookmark links, take simple notes and store images and pdfs. Automatically tags your bookmarks using AI for faster retrieval. Automatically fetches title, description and images for links. Automatically archives what you add. Sort your bookmarks into lists for better organization. Search through all your bookmarks using full text search. SSO support.
Available for iOS and Android. Addons for Chrome and Firefox.
All AI/LLM functionality is local. No external services are used.
The purpose of beets is to get your music collection right once and for all. It catalogs your collection, automatically improving its metadata as it goes using the MusicBrainz database. Then it provides a bouquet of tools for manipulating and accessing your music. Because beets is designed as a library, it can do almost anything you can imagine for your music collection. Via plugins, beets becomes a panacea.
textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, textacy focuses primarily on the tasks that come before and follow after. Abstracts away the boilerplate for the stuff you actually care about.
Quickstart: https://chartbeat-labs.github.io/textacy/getting_started/quickstart.html
Picard is a cross-platform music tagger written in Python. Install and run it on your library and it'll clean it up. Supports multiple file formats, accoustic fingerprinting to figure out what the track is.
Give the web application on this site a URL, some text, a phone number, or an SMS code and it'll generated a 2d barcode called a QRcode, which you can save as a .png file to use as you like. Good for stickers, business cards, and other ways of making some data machine readable and scannable.
prose is a natural language processing library (English only, at the moment) in pure Go. It supports tokenization, segmentation, part-of-speech tagging, and named-entity extraction. Parses English text, can also natively extract e-mail addresses, hashtags, @mentions, URLs, and emoticons. Can tag segmented and analyzed text by part of speech, including punctuation marks. Can identify types of entities (people, places). Also has the option to build and train custom models.