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I built a friend app after watching Twitter/X tear itself apart

Hacker News

I built a friend app after watching Twitter/X tear itself apart

A month ago, I was doom-scrolling Twitter at 2am in Accra and got hit with post after post of people tearing each other apart. I closed the app thinking "we have such a long way to go." Then I remembered something: a few years back, I hesitated for 48 hours before messaging someone on Facebook who would become my mentor. I was scared—previous scammers had burned me. But I gave myself that window to decide. I messaged him. He changed my life. That night, I thought: what if that 48-hour window could work for everyone? So I built Eintercon. How it works: You match with someone internationally based on shared interests (sci-fi books, indie music, whatever you're genuinely into) You have exactly 48 hours to chat and see if the connection is real After 48 hours, you both decide: keep going or move on No ghosting. No guilt. No conversations dying in your inbox. Why 48 hours? Because it creates urgency without pressure. It's long enough to have real conversations, short enough that you can't procrastinate forever. Either the friendship sparks, or you both deserve better matches. Every other friend app lets you match with 50 people and message none of them. We force a decision. It's uncomfortable, but it works. Why international? Because you can't hate someone whose story you know. Because the person who gets your weird obsessions might be in South Korea, not your city. Because division thrives in bubbles, and I'm tired of bubbles. One month in: 200 users across 30+ countries Zero marketing budget (just word of mouth) People telling me they made their first real friend in years A user in Ghana and a user in Seoul are now best friends over sci-fi books The tech: React Native, Node.js, Firebase, PostgreSQL. Nothing fancy. I'm a student splitting time between classes and coding. It's scrappy but it works. What I'm struggling with: Keeping international matches feeling balanced (timezone challenges) Building safety features without killing spontaneity Figuring out monetization that doesn't feel gross What I learned: People are desperate for genuine connection Urgency > features for engagement Emotional intelligence beats algorithms You don't need funding to validate an idea Why I'm posting here: I want to hear from people who've tried to solve loneliness with tech. What worked? What didn't? Honestly? I'm hoping some of you try it and tell me what's broken. The bigger question: Can software actually fix loneliness? Or are we just creating better ways to be alone together? I don't know yet. But 200 people have real friends now who didn't a month ago. That feels like something.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
97%97% 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, inbox, coding · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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