Springus – Outfit recommendations from your real wardrobe using AI

Hacker News

Hey HN, Since starting to work from home, I noticed my motivation to get dressed in the morning tanked. I’d default to the same sweatpants, which started affecting my mood and productivity. I wanted something to nudge me to dress better—without making it a chore. That’s why I built Springus, a wardrobe companion for iOS. Instead of manually cataloguing every item, Springus uses a multi-class segmentation model to build your digital closet from fit pix. The recommendation system then suggests outfits from clothing you actually own, aiming to reduce decision fatigue and help you find combinations you might not have considered. The hardest part was making the segmentation work reliably with real-world photos — messy backgrounds, bad lighting, and all. I ended up training a custom model on hundreds of my own fit pics and some of friends, iterating until it was good enough to share. I’ve been using Springus every day for the last 2 months. It’s free, and there’s no catch — I plan to monetize later by recommending clothes that fit your style, but right now, it’s just a passion project I wanted to share. If you’re interested, I’d love feedback — especially on the segmentation accuracy and the outfit recommendations. What would make this genuinely useful for you?

Visit product View on Hacker NewsApr 25, 2025

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

88%88% 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 HuntOn track for Day 1 leaderboard · Strong signals: model, using · Missing: mac, agents, macos

77%77% 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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

21%21% predicted probability of success on Acquire.com, 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

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

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · 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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