Library to export Notion pages to Markdown for serving via Hugo

Hacker News

Export content written in Notion to markdown, compatible for [Hugo]( https://gohugo.io/ ) blog. You can use Notion as a CMS in order to author, edit, and manage all your content while leverage the power of Hugo in order to serve the content statically on your blog site. This lets you leverage the best of both worlds - powerful and expressive UX of Notion for authoring along with speed and pre-built feature rich themes from Hugo for personal blog site. The package ships with a script in order to export content from Notion in a compatible format. ``` $ pip3 install notion2hugo $ publish_notion_to_hugo [-h] config_path ```

Visit product View on Hacker NewsAug 16, 2023

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting

77%77% 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 · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

49%49% predicted probability of success on TrustMRR, 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

48%48% 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 · Missing: plus, platform, intuitive

26%26% 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

16%16% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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