Phoenix 2.1 – a OS X window manager scriptable with JavaScript

Hacker News

This release includes some exiting new and requested features: including support for Spaces, creating timers to achieve delays or timed events and support for running Phoenix completely in the background! We also added some nice additions to the API, made various other improvements and fixed a few annoying bugs. Phoenix is a lightweight OS X window and app manager scriptable with JavaScript. You can also easily use languages which compile to JavaScript such as CoffeeScript. Phoenix aims for efficiency and a very small footprint. If you like the idea of scripting your own window or app management toolkit with JavaScript, Phoenix is probably going to give you the things you want. With Phoenix you can bind keyboard shortcuts and system events, and use these to interact with OS X. 1.) highly customisable, write your own configuration 2.) bind keyboard shortcuts and system events to your callback functions 3.) control and interact with your screens, spaces, mouse, apps and windows 4.) log messages, deliver notifications or display messages as modals and 5.) run external commands Take a look! https://github.com/kasper/phoenix/

Visit product View on Hacker NewsApr 25, 2016

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

82%82% 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.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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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