Mercury – Publish Jupyter Notebook as web app by adding YAML header

Hacker News

Mercury is a perfect tool to share your Python notebooks with non-programmers. - You can turn your notebook into web app. - You can add interactive widgets to your notebook by defining the YAML header. Your users can change the input and execute the notebook. - You can hide your code to not scare your (non-coding) collaborators. - Users can interact with notebook and save they results. - You can share notebook as a web app with multiple users - they don't overwrite original notebook. The demo running at Heroku (free dyno) http://mercury-demo-1.herokuapp.com , at AWS EC2 (t3a.small) http://mercury.mljar.com - No need to register.

Visit product View on Hacker NewsJan 20, 2022

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

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, coding, code · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

17%17% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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