Digital Wardrobe – Track your daily outfit cost

Hacker News

*The Problem:* A $200 jacket worn twice = $100 per wear. A $50 t-shirt worn weekly = $2.50 per wear. Most wardrobe apps focus on outfit planning, but none track the actual cost of what you wear. *The Solution:* Digital Wardrobe lets you: - Upload photos of your clothes - Track how often you wear each item - Calculate cost-per-wear for every piece - Get data-driven insights for smarter buying decisions *Tech Stack:* - Next.js + TypeScript - Supabase for auth & storage - Tailwind CSS + shadcn/ui - Deployed on Vercel *Why I Built This:* I wanted a simple way to make fashion choices based on data rather than impulse. Now I can see what’s truly worth keeping and plan outfits smarter. MVP built in 2 days. Looking for feedback on: - UX/UI improvements - Additional metrics to track - Feature ideas for future releases Try it out and let me know what you think: https://digital-wardrobe-ivory.vercel.app/

Visit product View on Hacker NewsAug 24, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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