Should I Build It Should I Build It
02 Jun 2025
Social Media

An app that analyzes images of celebrities and influencers to find and ...

...display links to clothing and accessory products.

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.

Your are here

Your idea of creating an app that analyzes images of celebrities and influencers to identify and link to clothing and accessory products places you in a crowded and challenging market, a 'Swamp' as we call it. We found 10 similar products, indicating significant competition. The average engagement is low, with an average of just 1 comment per similar product, which suggests that while the idea has been tried, it hasn't typically captured much sustained user interest. Unfortunately we couldn't compute the net use or net buy signals. Based on the success and failures of similar products and the fact that there are so many of them we strongly advise against building it.

Recommendations

  1. Before investing further, deeply investigate why existing solutions in this space haven't gained traction. Look into user reviews, competitor analysis, and market trends to identify the common pitfalls. A common theme in similar products is 'bad recommendations' and 'irrelevant search results'. Understand these failures to see if you can truly differentiate.
  2. If you decide to proceed, narrow your focus to a specific, underserved niche within the fashion market. Instead of targeting a broad audience, consider specializing in a particular style, body type, or demographic. For example, one competing product was criticized for lacking options for males 20-29. Consider specializing in a specific niche to start with.
  3. Explore opportunities to provide tools or services to existing fashion retailers or e-commerce platforms rather than building a standalone app. This could involve developing image recognition technology that integrates with their existing websites or apps, offering a less risky entry point. This strategy could result in sales quicker than if you try to acquire users by yourself.
  4. Consider researching adjacent problems in the fashion or e-commerce space that might be more promising and less saturated. Perhaps there is a demand for AI-powered personal shopping, or automated fit recommendations.
  5. Given the challenges and competition in this space, it may be wise to conserve your resources and energy for a different startup idea with a higher potential for success. Not every idea is worth pursuing, and being selective is crucial for early stage founders.
  6. Given that one similar product required registration before showing its features, make sure that users can immediately try your product and see its value right away. This will drastically improve adoption and engagement.

Questions

  1. What unique data sources or algorithms will your app leverage to provide significantly better and more accurate product recommendations compared to existing solutions?
  2. How will you acquire initial users and generate enough image data to train your AI models effectively, given that similar products have struggled with engagement?
  3. What specific monetization strategies will you employ to ensure the long-term sustainability of your app, considering that there's no guarantee people will actually want to buy the clothes and accessories that your app recommends?

Your are here

Your idea of creating an app that analyzes images of celebrities and influencers to identify and link to clothing and accessory products places you in a crowded and challenging market, a 'Swamp' as we call it. We found 10 similar products, indicating significant competition. The average engagement is low, with an average of just 1 comment per similar product, which suggests that while the idea has been tried, it hasn't typically captured much sustained user interest. Unfortunately we couldn't compute the net use or net buy signals. Based on the success and failures of similar products and the fact that there are so many of them we strongly advise against building it.

Recommendations

  1. Before investing further, deeply investigate why existing solutions in this space haven't gained traction. Look into user reviews, competitor analysis, and market trends to identify the common pitfalls. A common theme in similar products is 'bad recommendations' and 'irrelevant search results'. Understand these failures to see if you can truly differentiate.
  2. If you decide to proceed, narrow your focus to a specific, underserved niche within the fashion market. Instead of targeting a broad audience, consider specializing in a particular style, body type, or demographic. For example, one competing product was criticized for lacking options for males 20-29. Consider specializing in a specific niche to start with.
  3. Explore opportunities to provide tools or services to existing fashion retailers or e-commerce platforms rather than building a standalone app. This could involve developing image recognition technology that integrates with their existing websites or apps, offering a less risky entry point. This strategy could result in sales quicker than if you try to acquire users by yourself.
  4. Consider researching adjacent problems in the fashion or e-commerce space that might be more promising and less saturated. Perhaps there is a demand for AI-powered personal shopping, or automated fit recommendations.
  5. Given the challenges and competition in this space, it may be wise to conserve your resources and energy for a different startup idea with a higher potential for success. Not every idea is worth pursuing, and being selective is crucial for early stage founders.
  6. Given that one similar product required registration before showing its features, make sure that users can immediately try your product and see its value right away. This will drastically improve adoption and engagement.

Questions

  1. What unique data sources or algorithms will your app leverage to provide significantly better and more accurate product recommendations compared to existing solutions?
  2. How will you acquire initial users and generate enough image data to train your AI models effectively, given that similar products have struggled with engagement?
  3. What specific monetization strategies will you employ to ensure the long-term sustainability of your app, considering that there's no guarantee people will actually want to buy the clothes and accessories that your app recommends?

  • Confidence: High
    • Number of similar products: 10
  • Engagement: Low
    • Average number of comments: 1
  • Net use signal: 13.0%
    • Positive use signal: 32.0%
    • Negative use signal: 19.0%
  • Net buy signal: 5.0%
    • Positive buy signal: 24.0%
    • Negative buy signal: 19.0%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

Similar products

Relevance

Using Stable Diffusion to show you in awesome outfits you can buy

08 Jan 2024 Fashion Photography

Hey everyone! I'm Masha, Stanford alum and creator of Fotofit.Fotofit is a shopping app where you can see outfits in any style on you, and buy those outfits.I'm using an LLM to generate descriptions of outfits that fit your preferences, and using SD to generate the images of them.I came up with the idea after using social media for outfit inspiration. It was tough to find outfits in my style, and even if I did find them, I didn't know how they would look on me or how to buy the items to recreate that outfit.With Fotofit, you can search in natural language for whatever style / vibe you want ("Date night outfits for NYC winter", "Something in between formal and casual"), see thousands of outfits in that style on you, and tap to buy the items from your preferred brands, in your size and budget.It works for men's and women's styles.Please try the app, I'd love to hear your thoughts and feedback!

Registration required before seeing features.

Registration required before seeing features.

Avatar
6
1
-100.0%
-100.0%
1
6
Relevance

Using GPT-4 to buy outfits I liked from movies and TV shows

03 Mar 2024 Fashion Chrome Extensions

In a nutshell:1. I see so many outfits I like in shows, movies, F1 celebrity appearances etc., but I don't know where to buy those items, or they're too expensive. 2. Sometimes, I like the vibes of the outfit and I want to see similar items I can buy from my favourite stores.So I made this Chrome extension that lets you shop any outfit via picture. It uses GPT-4 to generate natural language descriptions of the items in the outfit, then searches the web for them. It also lets you set your preferred shops, sizes and budgets as filters, so you can see only stuff you would actually consider buying.Try it out if you're interested and let me know what you think!

Cool idea

Avatar
8
1
1
8
Relevance

Virtual Try-On for Online Shopping - See how clothes look on you with one click before buying

31 Aug 2024 Fashion E-Commerce Chrome Extensions

Chrome extension: Visualize clothes on your photo while shopping online. One-click on Nike, Amazon & more. Mix with your wardrobe, great for kids' clothes. Beta version - weekend side project. Expect quirks as we improve! Feedback welcome.

The product addresses online shopping challenges, particularly in the Nigerian fashion market. Users praise its thoughtfulness and express excitement for the final version following a smooth beta. The extension is helpful for finding clothes that fit well and allows for trying them on at home. It's seen as a cool solution that reduces the struggle of buying clothes online, especially items like T-shirts and polos.

No criticisms were provided for this Product Hunt launch.

Avatar
18
6
66.7%
50.0%
6
18
66.7%
50.0%
Relevance

Virtual Try-On, 1 click Chrome extension for visualizing clothes on you

31 Aug 2024 Fashion

We created a Chrome extension to let you virtually try on clothes from popular websites like Nike and Amazon using your own photo (I also use it for my kids - as a parent, they are the one I mostly buy clothes for).Key features and motivations:- One-click visualization on supported websites: no more downloading and re-uploading images -> this was really the flow I wanted and that other extension or website do not offer right now. I see something I like, I click, and that's it. - Use your own picture as the model - Works for tops, pants and dresses on multiple clothing sites (does not work for shoes or accessories)We used this AI to build the engine: https://idm-vton.github.io/ While working on it there was a new cool one (Kolors) that has been released and that we would like to try as well in the Future (anyone has been using it?)It's a weekend project built with my friend Kalkidan from Ethiopia, currently in beta. We'd love your feedback!

Avatar
1
1
Relevance

Fynspo. – Shop clothes that match your taste

24 Jul 2024 Artificial Intelligence Fashion

Hey HN! I'm Dhruv from fynspo. We're building a unified app to shop for clothes across any brand, and search based on outfits personalized to your style.We've been working on our first version for about 3 weeks and we're excited to finally have it published on the Apple App Store.This idea was born when we participated in a SPCxOpenAI hackathon a few months back and built an early version using GPT-4V. I've found that most technical solutions should stay away from using LLMs, they suck. Instead, creating a pipeline consisting of smaller ML models trained for specific tasks yields more accurate, cheaper, and faster results.I’d love for you to try it out and share your feedback. We are making rapid improvements for our next update and any thoughts or suggestions would be very appreciated!

Avatar
1
1
Top