In-browser analytics within JAMSTACK

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I have a product called SQL Frames that provides in-browser analytics. I was exploring how to integrate this into JAMSTACK. I learned about how to create docusaurus plugin and React components to make it easy to integrate SQL Frames. For example, after installing docusaurus, all it takes is "npm install @sqlframes/docusaurus-plugin@latest @sqlframes/docusaurus-components@latest" (extra configuration to specify a license key if desired) jamstackanalytics.com is just a preview website to show what kind of analytics UX is possible with JAMSTACK and in-browser analytics. The primary target for this technology is internal teams that need analytics and rapid data exploration capabilities and iterate fast without waiting for changes to data models in the backend. Docusaurus is one JAMSTACK candidate but I want to explore others as well. Looking for feedback on the idea, UX at the preview site and what other JAMSTACK are good choices to expand on.

Visit product View on Hacker NewsJul 21, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models · Missing: mac, agents, macos

76%76% 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

75%75% predicted probability of success on Indie Hackers, 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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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