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Baastronaut – open-source Airtable alternative built on PostgREST

Hacker News

Baastronaut – open-source Airtable alternative built on PostgREST

Hi HN, sharing a project that my friend and I are working on - Baastronaut ( https://github.com/baastronaut/baastronaut ) - an open-source Airtable alternative. While looking for Airtable-like tools for my work, I realised that existing tools don't suit what I need. They are either too technical for non-technical users or do not have good developer APIs. For example, to filter for certain rows in Airtable, you need to use filterByFormula, which is clunky. Our main design principle is "projects and spreadsheets should represent database relations but don't make it technical" (e.g., spreadsheets == tables, projects == schemas, links to other spreadsheets == foreign-key references). This is in contrast with other tools that use a wide table to store user's columns, type, and data. By sticking to the "represent database relations" principle, we are able to use PostgREST to generate flexible GraphQL-like REST APIs (plus, I play with PostgREST a lot, so i thought why not build something with it.) For users that are managing the spreadsheets, we make it non-technical-friendly by hiding certain details, e.g. only basic data types, instead of all the data types in Postgres, are shown to users. Users manage spreadsheets and not databases in our app. We spoke to a few people and we understand that it is common to build this (database + server to auto-generate APIs + simple crud interface) in-house to replace Airtable. We are still very early and there are a lot of features we want to build, e.g. a good fuzzy search. It’s not for production use yet but we hope people can try it and/or give us their comments. Please share with us your feedback on anything. Thank you!

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, apis, open · Missing: mac, agents, macos
91%91% 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
82%82% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, friendly, interface · Missing: platform, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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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