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NocoBase - Scalability-first, open-source no-code platform

Hacker News

NocoBase - Scalability-first, open-source no-code platform

Hey, I'm Zhou, the founder of NocoBase. I'm excited to introduce NocoBase to you all, an extremely expandable open-source no-code development platform. What makes NocoBase different? There are already many excellent no-code development tools in the world, but user needs are ever-changing. NocoBase stands out in the following ways: Everything is implemented as plugins, designed for extension development. Facing complex and changing business scenarios, it is futile to try to exhaust all needs by stacking no-code capabilities.NocoBase adopts a microkernel architecture where all features are plugins, just like WordPress. It can meet various needs by freely extending plugins. What you see is what you get, incredibly easy to use. NocoBase can develop complex and distinctive business systems yet its usage is remarkably simple, with one-click switching between usage mode and configuration mode. The page is a canvas, where various blocks and actions are placed on the canvas to form a suitable interface, just like Notion. The interface configuration mode is designed for ordinary people, not for programmers. Data model-driven, "data structure" and "use interface" are separated. Compared with form and table-driven, data model-driven can provide unlimited development capabilities. Data and UI are decoupled, and any number and form of blocks and actions can be created for the same data table and the same record. It supports the main database, external database, and third-party API as data sources. Open source, using mainstream technology stacks NocoBase's core and foundational plugins are entirely open-source, ensuring absolute transparency and controllability.

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created, para · Missing: reddit linkedin, podcasting, latex
95%95% 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: model, user, using · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · Missing: https docs, just released, exist
62%62% 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: ios, way, para · Missing: mobile apps, personal, entrepreneurs
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews
34%34% 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
9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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