Developer builds TikTok-style shopping feed with client-side recommender system in 150 lines of JS

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A developer has built ThodaSa, a reels-style impulse-shopping demo targeting the Indian market, where users scroll through single-product full-screen cards priced under ₹499. The recommender system runs entirely in the browser using 13-dimensional feature vectors and cosine similarity, with no backend server or login required. User preferences are tracked through engagement signals such as purchases, dwell time, and wishlisting, then stored locally via localStorage and decayed each session to prioritise recent behaviour. The feed balances personalisation and discovery by mixing two tailored 'For You' cards for every one random 'Fresh Find' card, avoiding echo chambers. The project is live at thodasa.com as a concept demo, with source code published on GitHub.

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