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Data Caterer – Data generation and validation tool

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Data Caterer – Data generation and validation tool

Hi everyone! My name is Peter and I've been working on a data generation and validation tool called Data Caterer ( https://data.catering/ ). The latest version comes with a UI and you can now run it on Windows, Mac or Linux (also via Docker). I hope this tool makes generating and validating data across any data source simple and easy. Key features: - Batch and event data generation - Maintain relationships across any dataset - Create custom data generation scenarios - Clean up generated data - Advanced validation options - Suggest data validations Quick start to run it yourself: https://data.catering/get-started/docker Demo of the UI: https://data.catering/sample/ui/index.html Github repo: https://github.com/data-catering/data-caterer Happy to receive any feedback.

Visit productView on Hacker NewsMar 22, 2024
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting
67%67% 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: mac, dock · Missing: agents, macos, agent
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
28%28% 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 · Missing: mobile apps, personal, entrepreneurs
28%28% 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
14%14% 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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