Frolic – open-source Back end for your Customer Facing Dashboards

Hacker News

Hi HN, Today we’re launching Frolic - An Open Source backend to make customer facing dashboards 10x faster written in Rust.( https://www.frolicorg.com ) You can directly connect your database to the project and use ready made APIs to query data in frontend. You no longer have to write custom APIs for different dashboard components and create/maintain SQL queries for them. You can also use frolic-react library for your UI along with frolic to create full stack dashboards much faster: https://github.com/frolicorg/frolic-react What do you think? Feel free to comment with questions, remarks, and thoughts. Happy to hear them. Thanks

Visit product View on Hacker NewsAug 8, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: apis, open · 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.

Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

77%77% 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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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

25%25% 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

15%15% 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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