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Scenestamps – A website for sharing movie scenes with timestamps

Hacker News

Scenestamps – A website for sharing movie scenes with timestamps

Hello hackers, I've launched a website specifically for sharing scenes, complete with descriptions and timestamps from various films and TV shows. I'm reaching out to gather your perspectives and recommendations in these domains to improve the site and extend my outreach. Link : https://scenestamps.com You are not required to register/login to browse the site. Scenestamps Features: 1. Search : You can directly search for a scene or a source. 2. Upload Posts : You can register with your google account, login and start posting right away. Unlike other sites in this specific domain, users are allowed to upload posts. There are two types of posts - scene - source Source is a movie,tv show, documentary, etc... One source can have multiple scenes While creating a scene post, source can be selected there. Scene post will have the timestamp fields. There are two types of it: - single - one input field of timestamp in which the scene happens. - from-to - two input fields, from and to within which the scene takes place. 3. Share posts : Share feature is available on both source and scene posts, with which you can share the post to your favorite social media platforms 4. Tagging system : You can add tag to the scene posts. You can also get all the scenes that has that tag name by clicking on it. I think people wanting to create scenes is quite a small audience, but I want to make this the best it can possibly be so please post any problems or suggestions in the replies or at reddit.com/scenestamps.com or message me x.com/gjpx_ if you prefer.

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, single · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users, way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, 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
32%32% 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 · Missing: arr, mrr, revenue
13%13% 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.

Incorrect prediction on native model

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