Py

Python-Lotion – A Lightweight Wrapper for Notion API (Beta)

Hacker News

Python-Lotion – A Lightweight Wrapper for Notion API (Beta)

Hi HN, I’ve started building Python-Lotion, a lightweight Python wrapper for the Notion API. This project is currently in beta and still a work in progress. The main idea is to provide a simpler and more streamlined way to interact with the Notion API, building on top of the popular notion-client library. • GitHub: https://github.com/koboriakira/python-lotion • Why I built this: I wanted a cleaner, more intuitive interface for my own projects, and thought it might help others too. • What’s next: • Enhancing the documentation to make it more comprehensive and user-friendly. • Expanding functionality for managing blocks within pages, such as creating, updating, and organizing them more effectively. • Feedback: As this is still under development, I’d love to hear your thoughts, suggestions, or use cases that could guide future improvements! Thanks for checking it out.

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Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos
63%63% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, friendly, interface · Missing: plus, platform, reviews
40%40% 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
12%12% 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.

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