CoffeeSpace – A simpler cofounder matching app

Hacker News

We’ve been working on a playground that showcases what our cofounder-matching mobile app can do ( https://coffeespace.com ). It’s designed as a swipe gallery (similar to Hinge), but instead of matching romantically, you’re swiping through profiles of potential cofounders. The profiles are simulated and representative of the real users you'll find on our app. You can even try setting filters to refine your matches. Why we built it: Our mission is simple: to create more high-quality cofounder team-ups in the world. In the startup space, finding the right cofounder can be challenging, and we wanted to provide an intuitive, fun way to make that connection. We’ve even integrated LinkedIn information into the app to give you a fuller sense of who you’re connecting with. What to expect: You can try the playground now, using it to test out different filters and explore the user experience. If you’d like to try the app itself, it’s built using FlutterFlow and is ready for download. We’re still figuring out if the swipe method is the best way to match cofounders. We’re considering adding a browse feature where users can view recommended profiles in a more traditional manner. A semantic search bar that lets you find cofounders by specific traits and characteristics is also in the works. Constantly looking for feedback: We’ve gained around 6,000 users and are constantly improving the app based on what we hear. We’d love your feedback—especially as we continue to iterate on user experience for founders. Fun things to try: - Try different filters and see what kind of cofounders you find. - Let us know if swipe feels like the right approach for you, or if browsing/searching fits better. The tech stack: Mobile app: Built with FlutterFlow. Playground: JavaScript and HTML running on Webflow. Looking forward to hearing your thoughts! Cheers, Hazim, Carin, Fauzan

Visit product View on Hacker NewsOct 11, 2024

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92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using · Missing: mac, agents, macos

84%84% 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, 000, io · Missing: https docs, excited, just released

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, users · Missing: plus, platform, reviews

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% 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

10%10% 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.

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