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Lotad, an Open-Source DuckDB Differ for Data Exploration

Hacker News

Lotad, an Open-Source DuckDB Differ for Data Exploration

Lotad makes comparing DuckDB files effortless. Get started with a simple setup command that generates a config YAML file tailored to your needs. The tool creates a comprehensive DuckDB output file containing: High-level change summaries per table Detailed table deltas Complete schema difference analysis For tables with drift, Lotad generates dedicated comparison tables that preserve the original schema while adding an observed_in column to track data origins. Lotad is under active development, with support for additional database types on the roadmap. It's actively being worked on and I do have expanding the number of supported Databases on the roadmap.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
68%68% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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