This tool can be described as a Tiny, Dirty C command that looks for coreutils basic commands (cp, mv, dd, tar, gzip/gunzip, cat, etc.) currently running on your system and displays the percentage of copied data. It can also show estimated time and throughput, and provides a "top-like" mode (monitoring).

git-remote-gcrypt is a git remote helper to push and pull from repositories encrypted with GnuPG, using a custom format. This remote helper handles URIs prefixed with gcrypt::.

Music Blocks is a great way to learn coding through music (as well as learn music through coding). Move colorful blocks around the screen to design dynamic musical creations. Test your code at the click of a button. Create everything from simple songs to puzzles and games.

The world’s most widely used web app scanner. Free and open source. Actively maintained by a dedicated international team of volunteers.

Daemon application able to fetch messages from supported websites and send them by mail. It can also be used to send a reply to a message (on a module which supports this feature), by piping an email to it.

Looks like it should be possible to read (perhaps even write) private messages on Reddit without having to deal with Reddit's own, pretty terrible, web interface. Worth looking into.

Typesense is an open source, typo tolerant search engine that is optimized for instant sub-50ms searches, while providing an intuitive developer experience.

This is a list of (Fuzzy) Data Matching software. The software in this list is open source and/or freely available.

The term data matching is used to indicate the procedure of bringing together information from two or more records that are believed to belong to the same entity. Data matching has two applications: (1) to match data across multiple datasets (linkage) and (2) to match data within a dataset (deduplication). See the Wikipedia page about data matching for more information.

Similar terms: record linkage, data matching, deduplication, fuzzy matching, entity resolution

Suppose we want to combine a BERT-based named entity recognition (NER) model with a rule-based NER model built on top of spaCy. Although BERT's NER exhibits extremely high performance, it is usually combined with rule-based approaches for practical purposes. In such cases, what often bothers us is that tokens of spaCy and BERT are different, even if the input sentences are the same. For example, let's say the input sentence is "John Johanson 's house"; BERT tokenizes this sentence like ["john", "johan", "##son", "'", "s", "house"] and spaCy tokenizes it like ["John", "Johanson", "'s", "house"]. To combine the outputs, we need to calculate the correspondence between the two different token sequences. This correspondence is the "alignment".

Simple command line tool for text to image generation using OpenAI's CLIP and Siren.

Instead, I have a computer that is designed largely to maximize the profits of the computer industry. Except for a handful of very over-priced models that I can't afford to buy, our computers are increasingly designed to be little more than advertising platforms and vehicles for maximizing the cloud revenues of their true owners: online data gatherers, advertisers, and cloud companies. Our computers have numerous hardware and software back doors that are designed to allow governments and corporations to spy on and track us around the Internet.

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