An app for buying home decor that fits with things you have at home

Hacker News

Hello HN, I'm building an interior design assistant that helps you buy home decor and furniture that match with things you already have at home. Instead of searching for products, you take a picture to an object or to the entire room and the app will suggest you what products to buy. I had the problem of decorating my home and I spent a lot of time browsing and driving from store to store for finding the products that I needed. An interior designer was to expensive and even if I had a design I'll be limited to just the products in my locar area or in what the designer knows. Have any of you had the same problem before? So that's why I' m building this. You can try what I've already done here: https://marketio.ai/assistant Also, if you are a coffee drinker, you can take a picture to your coffee (coffee cup, mug, coffee machine, etc) and see what products it will suggest you. I'm open to any feedback from you. Thanks.

Visit product View on Hacker NewsOct 7, 2020

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Actual performance

3points

3comments

Did not reach leaderboard

Launch Intel predictions

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

76%76% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% 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

15%15% 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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