Lossless compression is a data compression technique that allows the original data to be perfectly reconstructed from the compressed data, without any loss of information. It's commonly used for text, executable programs, and certain types of images where losing even a single bit of data would be disastrous, unlike those Instagram photos where you can barely tell the difference between the original and the compressed version that's been reposted 37 times.

How to use it in a sentence

• I tried to use lossless compression on my collection of rare Pepe memes, but the file size was still too large to email to my fellow memelords on my dial-up connection.

• The startup's revolutionary new lossless compression algorithm promised to reduce the size of any file by 99.9%, but it turned out to just be a script that deleted all the user's data and replaced it with a single ZIP file containing a Rick Roll video.

If you actually want to learn more...

• Real-time full-text search with Luwak and Samza - This article dives into the intricacies of indexing queries for optimizing search performance, particularly when dealing with large volumes of queries and complex boolean logic. It's a deep dive, but worth it if you want to level up your search game.

• Gauging Similarity via N-Grams - While not directly about lossless compression, this article from Paul Graham's list of Bayesian filtering resources explores using n-grams for measuring similarity between text documents. It's a useful technique to have in your toolkit for all kinds of text processing tasks.

• An Introduction to Latent Semantic Analysis - Another one from PG's list, this article provides an accessible overview of Latent Semantic Analysis, a technique for extracting hidden semantic structures from text using singular value decomposition. Again, not directly about lossless compression, but a powerful approach for text mining and information retrieval that any self-respecting data wrangler should be familiar with.

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