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OrangeWalrus, an aggregator for trivia nights (and other events) in SF

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OrangeWalrus, an aggregator for trivia nights (and other events) in SF

Two problems I encountered personally: 1) Some buddies and I went to a trivia night late last year, only to arrive to find it cancelled (with signs still on the walls saying it happened every Tuesday, etc) 2) Sourcing ideas for fun things to do in the city on a given night, in a given neighborhood. Some sites help a ton (e.g. funcheapsf), but often don't have everything I'd want to see, so we decided to build that out a bit. Anyway, I built this originally to solve #1, then a buddy and I expanded it to also start addressing #2 (still in progress, but we've added more event types already). Thanks for checking it out! We're very open to thoughts / feedback.

Visit productView on Hacker NewsFeb 25, 2026
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% 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: open · Missing: mac, agents, macos
60%60% 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: ide · 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 · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% 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.

Correct prediction on native model

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