Gem – Quickly Save and Search Your Social Media Finds

Hacker News

I built Gem to help you save and organize discoveries made through social media content like Instagram and TikTok. Whether it’s a new restaurant to try, a recipe to cook, or a movie to watch, Gem captures the essence of what you find and saves it in a clean, actionable format, so it’s easy to access when you’re ready to take action. The way content is presented in the discovery phase is different from the action phase. When you’re scrolling through TikTok or Instagram, short-form videos are perfect for exploring new ideas quickly. However, when it’s time to actually use that information—like planning a night out or cooking dinner—scrolling through videos again is frustrating and time-consuming. You need quick access to the details without the distraction of video playback. That’s where Gem comes in. Instead of simply bookmarking the original video, Gem works quietly in the background. All you have to do is share the post directly from TikTok or Instagram, and Gem will automatically extract key information—like addresses, ingredients, or recommendations—and save it in a format that’s easy to read and act on when the time comes. Whether you’re planning a night out, cooking a meal, or picking a movie, Gem, makes it simple to find what you need, without having to scroll through your video feed again. Currently, Gem works with TikTok and Instagram for content related to places, recipes, movies, or TV shows. We’re working to add support for more platforms and categories, and we’re adding new features and functionality every day. Give Gem a try to see how easy it is to share a post from TikTok or Instagram—no interruptions to your browsing, just smarter saving.

Visit product View on Hacker NewsOct 16, 2024

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58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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