pypiscout.com – Find packages on PyPI with natural language queries

Hacker News

Finding the right Python package on PyPI can be a bit difficult, since PyPI isn't really designed for discovering packages easily. For example, you can search for the word "plot" and get a list of hundreds of packages that contain the word "plot" in seemingly random order. Inspired by [this blog post]( https://koaning.io/posts/search-boxes/ ) about finding arXiv articles using vector embeddings, I decided to build a small application that helps you find Python packages with a similar approach. For example, you can ask it "I want to make nice plots and visualizations", and it will provide you with a short list of packages that can help you with that. You can try it out at https://pypiscout.com

Visit product View on Hacker NewsJun 26, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

57%57% 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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: visual, using · Missing: mac, agents, macos

43%43% 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

38%38% 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.

Incorrect prediction on native model

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