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Manganite – Quickly turn Jupyter notebooks into web apps

Hacker News

Manganite – Quickly turn Jupyter notebooks into web apps

Manganite allows easy conversion of Jupyter notebooks into dashboards. Simply annotate existing notebooks with Jupyter magics and serve them as interactive web apps. Manganite has been created to empower master and doctoral students in econ and management to turn research notebooks into interactive dashboards. The students use Python for data analysis, math programming, and basic machine learning. Instead of pouring their results into slides, posters, and papers to communicate findings, they can now use Manganite to create interactive dashboards without having to leave their familiar Jupyter environment. Manganite is built on top of the powerful Panel framework [1], but it does not require the user to convert their existing code into callbacks. Instead, Manganite parses the Jupyter notebook and builds a dependency tree between its cells. Scalar variables and Pandas DataFrames are transparently wrapped in Param classes, which lets them watch their values for changes and propagate these changes downstream. [1] https://panel.holoviz.org

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, apps, user · Missing: agents, macos, agent
85%85% 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: created, para · Missing: supports, reddit linkedin, podcasting
75%75% 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, existing, ide · Missing: https docs, excited, just released
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, para · Missing: mobile apps, ios, personal
61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% 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
18%18% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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