Embed live-ish follower counts

Hacker News

Cheers HN! I built gather.buzz to let myself (and maybe you?) embed live TikTok, Instagram, and YouTube follower counts into websites and apps. It’s simple, lightweight, and works even with profiles that aren't yours. ### Backstory At several points in the last years I have needed access to live social media data, but I got tired of bloated, expensive, overengineered tools and API restrictions. gather.buzz is my attempt to solve this with minimal setup and no unnecessary complexity. ### What it does - Live updates: Display follower counts directly on your site as plain text, updated automagically every 24 hrs. - Simple integration: Just copy-paste two small HTML snippet. If you've ever successfully added Google Analytics to a site, you are probably already overqualified for this. - Platform support: TikTok, Instagram, and YouTube (more coming soon). - Lightweight: Non-blocking, fast, and with minimal impact on Google Pagespeed. ### How it works 0. Go the the site and excuse the ugly design. 1. Pick a profile (= any public account). 2. Copy the embed code. 3. Paste it into your website or app. That’s it. ### Use cases - Add live follower stats to blogs, ecommerce pages, or portfolios showcasing social proof. - Create dashboards or dynamic UIs with live metrics. (I have an experimental integration with Looker right now...) - Support for WordPress, Webflow, Shopify, and custom-built sites. ### Pricing $1.99/month per profile. Includes daily updates and AI-free support by actual humans (me). ### Roadmap I’m working on: - Faster updates. Smarter caching. - Google Sheets and Zapier integrations. - API access for developers. Try it here: https://gather.buzz Feedback is appreciated, especially if something breaks. If you prefer to keep it private you can find contact details in my profile. Thanks for checking it out!

Visit product View on Hacker NewsNov 22, 2024

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49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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