IPyflow: a reactive, dataflow-aware IPython kernel

Hacker News

IPyflow is a new Python kernel for JupyterLab that understands how variables and cells depend on each other, making it easier to reason about notebook state. It adds opt-in reactivity, so that pressing ctrl+shift+enter triggers execution of all cells that depend (recursively) on the current cell. Furthermore, with its `code` function, you can see exactly what code is needed to reproduce a given variable. I started working on it after watching the famous talk "I Don't Like Notebooks" by Joel Grus, and, anecdotally, I like notebooks just a little more when I use this kernel :)

Visit product View on Hacker NewsJan 20, 2023

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

84%84% 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.

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

47%47% 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

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

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: started · Missing: supports, reddit linkedin, podcasting

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

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

39%39% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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