A detailed analysis of the DeepMind/Meta study: how large language models achieve unprecedented compression rates on text, image, and audio data - and the implications of these results
![An Analysis of DeepMind's 'Language Modeling Is Compression' Paper](https://lemdro.id/pictrs/image/472a96bd-b9f1-438b-b76e-7deb521cb643.jpeg?format=webp&thumbnail=256)
A detailed analysis of the DeepMind/Meta study: how large language models achieve unprecedented compression rates on text, image, and audio data - and the implications of these results
Inside CPython's Clever Use of Bloom Filters for Efficient String Processing
![Why and How Does Python Use Bloom Filters in String Processing?](https://lemdro.id/pictrs/image/3945f857-6dbd-4f67-86f4-cc79efe9e877.jpeg?format=webp&thumbnail=256)
Inside CPython's Clever Use of Bloom Filters for Efficient String Processing
A detailed examination of Python 3.12's internal changes featuring the concept of 'immortal' objects, for performance enhancements
![Understanding Immortal Objects in Python 3.12: A Deep Dive into Python Internals](https://lemdro.id/pictrs/image/b1ef94f0-3553-4b65-9daa-a35b885436dd.jpeg?format=webp&thumbnail=256)
A detailed examination of Python 3.12's internal changes featuring the concept of 'immortal' objects, for performance enhancements
An Extensive Walkthrough of Python’s Primary Memory Management Technique, Reference Counting
![How CPython Implements Reference Counting: Dissecting CPython Internals](https://lemdro.id/pictrs/image/f591ea18-9fc6-4f01-9cf5-6581dd196bff.jpeg?format=webp&thumbnail=256)
An Extensive Walkthrough of Python’s Primary Memory Management Technique, Reference Counting