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AirQL: An Airtable API with OAuth2 access control and permissions

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AirQL: An Airtable API with OAuth2 access control and permissions

Hey there! At our company, Bit Complete [1], we use Airtable a bunch for managing operations. We really like how easy it is to throw up a form to collect information, or to build automations for offline processing like aggregations. One challenge we kept running into was how to build more complicated interactive apps using the API, while maintaining fine-grained access control to Airtable data. Airtable ties identity to API access control via API keys, which makes it very hard to build apps with realistic access control needs. So we built AirQL! [2] It’s a utility layer between Airtable’s API and custom/internal web applications that supports Google authentication and flexible access control. We’ve found that it makes it practical to build simple, useful internal apps, that still benefit from everything else Airtable has to offer. If this sounds interesting, check out the docs [3], try out the product, and let us know what you think! We’re looking for feedback and to learn if this would be useful to others. If you have questions or suggestions, please get in touch at airql-support@bitcomplete.io. [1]: https://www.bitcomplete.io/ [2]: https://www.airql.dev/ [3]: https://docs.plz.dev/airql

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, apps, using · Missing: mac, agents, macos
94%94% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created
76%76% 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
73%73% 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
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, google · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
17%17% 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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