Bloom filters are efficient probabilistic data structures used to quickly determine if an element is part of a set, allowing for rapid membership queries with a trade-off for false positives. They utilize a bit vector and multiple hash functions, where the choice of hash functions and the size of the filter can be optimized based on the expected number of elements and acceptable false positive rates. The article also discusses various implementations and use cases of Bloom filters across different technologies.

The article discusses modern perfect hashing for strings, focusing on an implementation that improves upon traditional hash tables by using fixed sets of strings mapped to predefined integers. It highlights the challenges of optimizing for different architectures and provides a coding example demonstrating the use of a "magic" number to avoid collisions in hashed values.