Villagers – A group travel app

Hacker News

I've built Villagers, a group travel app, in an attempt to overcome my own social isolation while working remotely, especially as someone who's friends are now largely parents and much less available for group travel. I find small group travel magical from an interpersonal and experience standpoint but organizing trips is absolutely brutal. With Villagers, I wanted to see if software could overcome some of those planning challenges with financial incentives, social features, and automation -- especially around messaging. I love climbing, so I've built a trip to co-work and climb in Red Rocks (Nevada) in November. https://villagersapp.com/t/gkkfv I'm looking for 4-8 high EQ tech folks who'd like to co-work and climb. Everyone's identity will be verified and I'll make sure it's a broad culture fit (i.e. polite, respectful, reasonably extroverted, etc.). We'll keep the costs down to less than $500 per person for 3 nights. For more social proof before you join me on a trip, you can click through my profile on the trip page above to see my LinkedIn work history as an early engineer at Plaid and most recently the founder of a 70+ person company (Tremendous).

Visit product View on Hacker NewsOct 26, 2022

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

72%72% 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 · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

23%23% 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

20%20% 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.

Incorrect prediction on native model

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