| Hi HN, I’m an amateur naturalist, and I couldn't find good visual content to study for animal/plant taxonomy. Standard text-based flashcards don't work well for visual identification. So, I built a visual spaced-repetition engine specifically designed to drill taxonomy using perceptual interleaving (showing you visually similar species back-to-back to train your eye). The stack:
SvelteKit / CDK (S3) / Fallback R2 / Postgres Could probably use some better optimization but it works well enough. It uses a customized implementation of the FSRS (Free Spaced Repetition Scheduler) algorithm adapted for image-heavy visual drilling. The hardest part is building out the curated image database. There is planned content for many more species coming soon! The platform is currently in a 100% free beta. Would love some feedback, advice, etc if you have time! https://species.app |
Ideas for your feature pipeline: geographic filtering (e.g., learn to identify plant/bird species in southern Florida); temporal filtering (explore extinct species --> currently endangered species); audio to learn calls/songs.