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Giraf – Share and discover cultural life

Hacker News

Giraf – Share and discover cultural life

Dear HN, Over the last few years, I've built an app called Giraf, for sharing and discovering cultural life [0]. "Cultural life" encompasses any kind of creative work (music, books, films, paintings, poems, podcasts, etc.), as well as events (concerts, film screenings, museum exhibitions, book events, etc.), places (museums, parks, bookstores, etc.), and the artists themselves who create these things. Giraf is essentially a knowledge graph plus a social network. The knowledge graph consists of all the cultural things just mentioned, as well as facts about relationships between them (e.g. song P is by artist Q, who's playing concert R, at music venue S). The social network is built around posts of items from the knowledge graph. You can reply to posts with thanks, comments, or suggestions. A suggestion features another item from the knowledge graph -- so it provides a way to say "if you like X, you should check out Y". The social network uses a follow-based mechanic that will be familiar from other social apps, but it's fully human-powered; there's no algorithmic curation. Giraf represents the use case for social media that has always appealed to me. Most people, whether they'd think of it in these terms or not, are on a lifelong journey of cultural exploration -- listening to music, reading books, watching films, going to shows, visiting places that in some way embody local culture, and so on. Giraf is meant to be a tool that serves this journey. The app has been really useful among my group of friends over the last few years, for exchanging recommendations in a low-key, async way. It turns out that a dedicated tool like this can bring out a lot of fruitful communication that wouldn't have been brought out in a group chat. I'm now opening up Giraf to a wider audience. If the idea resonates with you, please check it out and give it a try! And I welcome any and all feedback here. I'm committed to Giraf for the long term. My goal is to make Giraf financially viable, solely out of revenue from users who find it valuable and want to support it. To that end, I've built an optional, Patreon-style membership functionality [1]. Thanks for reading, and thanks for checking out Giraf! Ian Hinsdale [0] https://giraf.app [1] https://giraf.app/membership/

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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: apps, user, open · Missing: mac, agents, macos
79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps, users, way · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, users · Missing: platform, intuitive, reviews
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
38%38% 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 · Strong signals: revenue · Missing: arr, mrr, profit
18%18% 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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