Ev

Event Lifecycle Oriented Hooking

Hacker News

Event Lifecycle Oriented Hooking

Hi HN ! This project is my personal way of implementing a hooking [1] mechanism. It is made from scratch after several disappointing attempts. I designed the hooking mechanism so that its API is both minimalistic and intuitive (at least from my point of view). Since an event [2] has a beginning and an end, I thought it would be cool to be able to specify at what stage of its lifecycle a given hook should be called. So, when binding a hook to an event, I can set that spec which by default is the start of an event. I also introduced the ability to break, from a hook, the execution of an event and also to temporarily freeze a hooking session. The library is written in Python and is available on PyPI [3]. I'd like to know what you think of this project and its API (if we abstract from the underlying implementation language). [1] https://en.wikipedia.org/wiki/Hooking [2] https://en.wikipedia.org/wiki/Event_(computing) [3] https://github.com/pyrustic/hooking#installation

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
60%60% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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