I'm building a platform for the sharing art you love

Hacker News

So I made a website that enables people to share the art they create or like. I made it because this is what I'm passionate about. I made Bursts to be something clean, simple, and nimble. So you create an account and share stuff you like. That's it. You can follow people if you like their stuff, then on the homepage you get a chronological feed showing the images from everyone you follow. You can also comment on stuff: the usual things. I'm eventually going to charge a small recurring fee for this, but since I love you guys so much, I'm making it 100% free for life for anyone who signs up over the next little while. *tl;dr* I made a social network for sharing art. It's free at this moment, but not for long. https://bursts.us/ ps. expect bugs. Report them via the link in the footer if you'd be so kind.

Visit product View on Hacker NewsOct 16, 2024

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Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

59%59% predicted probability of success on AppSumo, 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

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

44%44% 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 · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue

18%18% 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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