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Refero – web and iOS design references for UX/UI research

Hacker News

Refero – web and iOS design references for UX/UI research

Together with my partner, we are bootstraping a service that allows product designers (and actually startup founders) to find design references from real life product (web and iOS). Why is this important and why does it even work? Every designer spends up to 6 hours a week (we've talked to over 100 of our users to make sure) looking at references on sites like Dribbble and Behance for sources of inspiration. Sometimes they just need to find payment form or specific dashboard, for example. And Refero solves this problem in the best way possible. Refero allows you to find iOS pages or web screens with the pattern or element the designer needs. Thus, it saves a lot of time. We started our work almost 2 years ago. Without any funding, we spent all our weekends and any free time on it. To date, we have collected more than 60,000 screens. All screens are in high quality with @x2 resolution and full size. We start every new feature by doing reference research with Refero, so we eat our dog food and really enjoy it. Why am I posting this here? Of course, we are looking for new users. And I believe that here we can find many founders here who will benefit from our service. In fact, up to 5% of our customers are early stage founders. We offer a trial version without entering card details and all that bullshit. Just give it a try: https://refero.design

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Actual performance

5points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting
91%91% 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: user, new · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, users, way · 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 · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real life · 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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