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Using Google Sheets as the back end/APIs of your app

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Using Google Sheets as the back end/APIs of your app

Hello everyone! At a company I worked for, we needed to develop an MVP (basically a web page) and apply certain business logic to a Google Drive spreadsheet that was frequently updated by the Sales team. In this case, we had two options: Develop a backend to replace the current spreadsheet and have the sales team use it as a new "backoffice" - This would take a very long time, and if the hypothesis we were testing was wrong, it would be time wasted. Create the web page and use Google's SDK to extract data from the spreadsheet. We chose to go with the second option because it was quicker. Indeed, it was much faster than creating a new backoffice. But not as quick as we imagined. Integrating with Google's SDK requires some effort, especially to handle the OAuth logic, configure it in the console, and understand the documentation (which is quite shallow, by the way). Anyway! We did the project and I realized that maybe other devs might have encountered similar issues. Therefore, I developed a tool that transforms Google spreadsheets into "realtime APIs" with PATCH, GET, POST, and DELETE methods. Since it's a product for devs, I think it would be cool to hear your opinions. It's still quite primitive, but the basic features already work. https://zerosheets.com

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, using · Missing: mac, agents, macos
93%93% 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
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google, way · Missing: mobile apps, ios, personal
54%54% 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
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% 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
19%19% 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.

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