Show HN: Tinytowns, a small model to generate realistic sounding town names (quirkyagents.com)

The launch of "tinytowns," a small AI model designed to generate realistic-sounding town names, marks a creative advancement in the AI/ML landscape. This model utilizes a character-level transformer architecture to predict the next letter in a town name based on patterns learned from 128,435 existing place names. With three different sizes available—nano, small, and tinytowns—each offers varying accuracy, with the largest model boasting a lower error score of 2.15. The model operates in a user’s browser, allowing for quick and local name generation without the need for server support. The significance of tinytowns lies in its unique application—creating fictitious town names, making it particularly useful for game developers and data scientists who require non-existent place names for testing purposes. With a simple user interface and adjustable parameters for name generation, the model empowers users to explore a range of invented names while offering a clear distinction between real and fabricated entries. As a testament to advancements in small model architectures, tinytowns showcases the potential for tailored, lightweight AI applications that require less computational overhead while still delivering effective results.