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A photo sharing app that isn't trying to be a TikTok clone

Hacker News

A photo sharing app that isn't trying to be a TikTok clone

We launched Pidgeon a few weeks ago, and since then we've had a chance to fix a lot of bugs and upgrade the usability a significant amount. The app is made just for photo sharing. No videos, no NFTs, just pictures, comments, and chats. Each post is ranked like on reddit - a combination of the user upvote/downvotes and the amount of time passed since the post's creation. We're also trying to stay away from ads, and instead adopt a freemium model. The main reason for this is because people are a lot more concerned with privacy these days, and if we aren't serving ads, then the user knows that we don't have an incentive to steal their data. There's already a small community of about 1500 on the app, with a lot of them being professional photographers. So the photos going up look really nice (at least in my opinion). But we don't just want to be a community of people who consider themselves photographers - that's a mistake that other platforms like Flickr and VSCO make. To become widespread, you need to make your platform simple to use for the casual person; only catering to professionals will necessarily keep your community smaller than it otherwise would be. Users have been asking for a desktop client, and while we don't have one yet, we do plan to add it - and it won't take us as long as IG to do it, and neither will it be an afterthought where the UX is intentionally made poor so that you're pushed to use the app instead. But we want to polish the mobile app first before we get there, and there's still a lot of work to do since it's super early days. If you want to try it out, here's the link: https://play.google.com/store/apps/details?id=app.pidgeon.pidgeon https://apps.apple.com/us/app/pidgeon-simple-photo-sharing/id1605784768 And please do provide any feedback you have on the app! It really does help us make it better.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
96%96% 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: model, apple, google · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, video, google · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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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