Solving Wordle using information theory

This podcast delves into how information theory, particularly the concept of entropy, can enhance your Wordle gameplay. The host explains the development of a Wordle-solving algorithm that began with the assumption of equal probabilities for all words. However, the algorithm was refined by adding word frequency data, which significantly boosted its accuracy. A key insight discussed is how entropy can evaluate both the expected information gained from a guess and the uncertainty of the remaining possible answers. This led to a notable improvement in the algorithm's performance, reducing the average number of guesses from 4.124 to 3.6 per game. This example vividly illustrates the practical impact of information theory in real-world applications.