zoom in to the sprite so u can read it, if u want I can try making it clearer by typing it all out in the notes and credits - I'll do it anyways.
Pseudorandom number generators (PRNGs) are algorithms that produce sequences of numbers that appear random but are actually generated deterministically from a starting value called a seed. This means the same seed will always produce the same sequence, making the results predictable if the seed is known. For true randomness, true random number generators (TRNGs) are used, which rely on unpredictable physical phenomena. How PRNGs work Algorithm: A PRNG uses a specific mathematical algorithm to create the sequence. Seed: The process starts with a seed, an initial value that determines the entire sequence. Deterministic output: Applying the algorithm to the seed generates a series of numbers. These numbers are "pseudo-random" because they are not truly random but are a predictable output of the algorithm and seed. Example: If you use the same seed, you will always get the exact same sequence of numbers, which is useful for reproducible simulations, but it's a security risk if a pattern can be predicted. Types of PRNGs Simple algorithms: Early methods like Linear Congruential Generators are simple but may have limitations. Mersenne Twister: A widely used algorithm known for its long period and good statistical properties, it is the default PRNG in languages like Python and R. Cryptographically Secure PRNGs (CSPRNGs): These are designed for security applications. They are built to be unpredictable, so even if you know some of the generated numbers, you cannot predict future or past outputs, or the seed itself. Generative Adversarial Networks (GANs): A newer approach where a neural network is trained to act as a PRNG.
