I

I built a Photoshop expert for clothing brands

Hacker News

I built a Photoshop expert for clothing brands

Hey guys, my name's Chris. The feeling of shipping your first app is incredible. This year, in February I paused my main business (a digital agency) and decided to learn full stack. Yep, not the wisest thing to do. Agree. But I was always passionate about apps. Most of my clients were startups. And gosh, I loved talking to founders. I remember first time running 'npm run dev' in my IDE and seeing the Next screen on my localhost. Great feeling. So I spent 3 months building my first app - Delle. It helps small clothing brands to create professional photo shoots for their clothing line. back in 2023, I had a few eComm clients. And I've noticed they spend a lot of money into professional photo shooting. Most importantly, they waste time and effort on finding a good studio, hiring models, scheduling.. And while there are plenty of tools that integrate with VTON (Virtual Try On) technology, I kept Delle a bit different: 1. I wanted to allow users to generate images for different fits, like S, L, XXL. This would help their customers to see how the product would look on them. It would also help with the store conversions. 2. Realistic models: the competing apps lack that human touch. You can easily spot it's AI. You can go ahead and give Delle a try here: https://www.usedelle.com/ If you'll have questions around the tech stack or simply want to share your feedback/bugs, please let me know. If would mean a lot to me.

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4points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
96%96% 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, apps, user · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps, month, users · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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.

Incorrect prediction on native model

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