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Harlequin, the DuckDB IDE for Your Terminal

Hacker News

Harlequin, the DuckDB IDE for Your Terminal

Hi HN - For the past four months I've been working (part-time, this is OSS after all) on Harlequin, a SQL IDE for DuckDB that runs in your terminal. I built this because I work in Data, and I found myself often reaching for the DuckDB CLI to quickly query CSV or Parquet data, but then hitting a wall when using the DuckDB CLI as my queries got more complex and my result sets got larger. Harlequin is a drop-in replacement for the DuckDB CLI that runs in any terminal (even over SSH), but adds a browsable data catalog, full-powered text editor (with multiple buffer support), and a scrollable results viewer that can display thousands of records. Harlequin is written in Python, using the Textual framework. It's licensed under MIT. Today I released v1.0.0, and I'm excited to share Harlequin with HN for the first time. You can try it out with `pip install harlequin`, or visit https://harlequin.sh for docs and other info.

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5points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
91%91% 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 · Missing: https docs, just released, exist
80%80% 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
36%36% 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
29%29% 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
22%22% 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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