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Mercury – Turn Python Notebooks to Web Apps

Hacker News

Mercury – Turn Python Notebooks to Web Apps

Hi! We're Piotr and Aleksandra, founders of Mercury (https://RunMercury.com), an open source framework for converting Jupyter Notebooks to Web Apps. You can turn Python notebook into interactive web app, static website, presentation, report or dashboard and share it online with non-technical users. You can self-host Mercury or use our hosting service (coming soon!). Our GitHub: https://github.com/mljar/mercury Sharing Python notebooks is challenging. You can't send notebook directly to non-technical stakeholders. You need to copy-paste results/charts into Word/PowerPoint or rewrite notebook to web framework. Mercury converts notebook to web app. User can execute cells but can't edit them. Mercury offers set of widgets that can be added to the notebook. When serving notebook with Mercury, widget change triggers automatic re-execution of cells. Not all cells are re-executed, only cells with widget definition and below, so you can cache results from previous cells execution (loading large dataset or model). Mercury comes with handy feautures to make sharing easy: - decide to hide or show notebook's code, - add authentication to notebooks, so only selected users can view them, - export final notebook to PDF or HTML file, - all to create output files in notebook, and make them downloadable, - share multiple notebooks on one Site. How does Mercury differ from existing solutions? - it was desinged for notebooks, it offers simple re-execution of cells after widget update, - it has built-in authentication. Mercury is available on AGPLv3. We would like to offer a hosting service, to make deployment very easy (just upload notebook to have website). We offer commercial license for companies looking for private forks and dedicated support. We’d love to hear from the HN community about this framework!

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Actual performance

6points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, user · Missing: mac, agents, macos
83%83% 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 · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: host, soon, users · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, users, widgets · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
13%13% 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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