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I made samspov.com to easily track updates

Hacker News

I made samspov.com to easily track updates

I've discovered this TikTokker called samspov a few months ago, and was immediately taken aback by the way he handled himself and tried to shine a spotlight on restaurants that were struggling but didn't have to be (great food, no audience). He tries to do updates and the bigger his fame got, the more incredibly the change for the restaurants. While TikTok is great for consuming lots of content, it's not great for context, so I made something that pulls in the videos, tries to figure out which restaurant it is, query Google Places API (paid but luckily, I think they have a fairly generous threshold before it charges ), and this way, tries to tie everything together, which lets me link videos to a restaurant, give them a page that gathers the videos there, so you can watch the orignal visit + any updates, but also gives links to their website, menu, phone number, opening hours, etc. Sometimes Sam does fundraisers for restaurants that are really struggling; I try to link active fundraisers as well. Sometimes, other people go to the restaurants after Sam's video and uploads them to TikTok; I added a way for people to submit their videos as well. I plan to make this community reviewed, but for now, I'll just manually review to make sure there is no weird stuff in there. You can add a reaction to a video to show what you think, which also doubles as a 'hide videos I've watched already' feature if you want it to be. It shows live reactions of other people just because it's fun. It took me a week to get Approved to integrate TikTok login (required for the reactions), so was very happy when that worked out they are VERY strict about approving things and you have to defend every claim you ask for (I use it to highlight verified users and users with a large following + link back to the users in the activity feed). I also made it into a PWA for iOS users, so they can get push notifications for when there is a new video. Sam also uploads to Instagram and YouTube Shorts; I integrated YT shorts (pull in the thumb + video so I can link them to the TikTok video); when there is a YT short linked, I alternate the thumbnails, and every video gets links to both TT and YT (I'm not trying to have people watch the videos on the site), so they can watch & engage with the content on their platform of choice. YT Reels were a bit of a challenge, so I could only do that by manually adding every video; not sure I want to do that yet; otherwise, I'd also try to cross-link Insta, but for now, just TT & YT. I hope you like it. I made this because I loved the content; there is no monetization strategy or anything. Happy to take any feedback. Cheers, Nick.

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89%89% 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: google, user, new · Missing: mac, agents, macos
88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, video, month · Missing: mobile apps, personal, entrepreneurs
62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
54%54% predicted probability of success on AppSumo, 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
42%42% 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: active · Missing: arr, mrr, revenue
22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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