A clean, no-frills event emitter for Javascript and Node.js

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post: http://ksdlck.com/post/15963718801/emitting-a-minimal-namespaced-emitter-for-javascript repo: https://github.com/ksdlck/Emitting I wrote this because I was unsatisfied EventEmitter2 (https://github.com/hij1nx/EventEmitter2), and because it was fun. The API is compatible with events.EventEmitter in Node.js core, but not all the elements of the EE2 interface.

Visit product View on Hacker NewsJan 30, 2012

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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

13%13% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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