Me

Meet people, have more conversations about what truly interests you

Hacker News

Meet people, have more conversations about what truly interests you

Hi all, Andrew here. I hate smalltalk[1] but love deep conversations and want more of them, for myself and for others. This is why I made pairup.social. It's a conversation-finding and friend-finding app based on the things you'd most like to talk about. Sure, you have your friends, colleagues and family you can talk to, but it can be hard to go deep on your unique interests. Finding and reaching out to people on the internet is possible, but in practice I find I don’t really do it that much. It’s a bit awkward. If I found them on a single-topic discussion group, I don’t know much else about them, including other interests or even if they’re open to meeting people at all. None of this is impossible to overcome, but there's enough friction to mostly stop me from doing it. The goal of this app is to make this easy enough that it actually happens. How it works: 1. Install the iOS or Android app. Sign-in can be deferred until after you’ve tried it out. 2. Add a bunch of links to capture what you are interested in at the moment. These can be links to accounts you follow, communities you've joined, books, movies or podcasts you love, causes you support – anything really. Your links/interests simultaneously describe you to others and enable the app to match you to people with overlapping and compatible interests. Your combination of links can be incredibly specific and nuanced but also very easy to build and maintain (try it!) 3. Complete your minimal profile: one picture, a name, optional bio, and locations you spend time in (if you want to emphasize local matches). Your interests are already a great way to describe you, so this kept very simple. 4. Fuzzy matching occurs. People with overlapping interests (either the same links or other links about the same or similar things) are suggested to you. You can see their interests and decide if you’d like the option to chat with them. Typical mutual-opt-in mechanics result in “pairups” and you can both take it from there and start chatting whenever you’re ready. No pressure, you already have things to talk about. I'd love it if you gave it a try and let me know what you think! [1] I did enjoy coding in Smalltalk in a past, past life https://techcrunch.com/2006/03/11/dabbledb-online-app-buildi... https://www.mercurynews.com/2010/06/10/twitter-buys-software...

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23points
8comments
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Indie HackersFits the IH revenue-focused audience · Strong signals: ios, including, compatible · Missing: supports, reddit linkedin, podcasting
88%88% 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: new, single, coding · Missing: mac, agents, macos
73%73% 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, including · Missing: https docs, excited, just released
59%59% 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: ios, way · Missing: mobile apps, personal, entrepreneurs
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
34%34% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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