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No-Code OAuth integration plugins for Bubble

Hacker News

No-Code OAuth integration plugins for Bubble

We are Sameera & Ralph, co-founders of Pathfix (https://pathfix.com). We have been working very closely with the no-code community for a few months now trying to bring our low-code oauth integration platform to the no-code community, starting with Bubble. Based off of these discussions, we released a few plugins that handle: - Entire oauth process for each provider - End-User authorization - Token management - Relevant API calls The plugins are available for free with the option to whitelabel. Here is the list of all the live plugins available (https://pathfix.com/integration-plugins/) - SSO Login (with 8 different providers) - Spotify Search API - Twitter connector - Quickbooks (Customer + Invoice) - Slack Thanks so much. We look forward to your feedback!

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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.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, code · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, 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
66%66% 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 · Strong signals: month · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews
35%35% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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