Lean content and data management for small projects

Hacker News

Hey HN! We created IncludeCore to enhance how content and data are managed for smaller sites and apps, designed to meet the needs of indie developers and small development teams. We take pride in offering a fair pricing model, reduced complexity for seamlessly combining various use cases, all aimed at simplifying the process of efficiently managing your content and data. With auto-generated visual editor panels, REST APIs, and webhooks, we make management and collection a breeze. The best part? It's fully hosted, eliminating painful plugin updates or maintenance headaches! Main features: - 17 field types to define the database models of your content and data - Advanced data validation and relationships Zero maintenance: fully hosted, no breaking changes and secure by default - CRUD APIs with examples and access control Media library - Visual editor panels for non-technical users to manage content and data - Webhooks to integrate to any systems - Static screens and global items support - Databases that support a large number of records for more generic cases We'd love for you to try it out and give us your feedback!

Visit product View on Hacker NewsMar 18, 2024

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, efficiently · Missing: supports, reddit linkedin, podcasting

90%90% 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, apps, user · Missing: mac, agents, macos

81%81% 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

61%61% 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: apps, users · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, efficient, users · Missing: plus, platform, intuitive

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

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.

Incorrect prediction on native model

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