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OneGen – A new way to design data models

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OneGen – A new way to design data models

Hi, I’m the technical founder of OneGen. I've been a software engineer for over a decade, but it wasn't until the pandemic that I decided to take a leap of faith and build an innovative solution to designing data models. Most applications & SDKs support more than one platform. And for each platform, software engineers write and maintain the same (or similar) data models. I thought that if I could design data models completely independent of a database or a programming language in a way that allows versioning and collaboration and then generate the model code for whatever the technology stack is, it would be pretty cool. OneGen doesn't work as a one-time code generator. Instead, you design and continuously maintain your data models right inside the app to achieve a single source of truth. We're excited to announce our free beta release to you.

Visit productView on Hacker NewsNov 9, 2022
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
90%90% 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: excited, ide, io · Missing: https docs, just released, exist
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
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
36%36% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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