Sp

SpacePigeon – a macOS workspace automation tool built on Hammerspoon

Hacker News

SpacePigeon – a macOS workspace automation tool built on Hammerspoon

I’ve been experimenting with a workspace automation script for macOS using Hammerspoon, and wanted to share it. I was looking for ways to do this because I found myself often opening the same apps in certain situations (phd research apps at the office and a clean workspace at home for instance) but I didn't find a tool that made this easy. It’s extremely early (a single Lua file), but it already sets up a full “workspace” by opening apps, placing windows, and assigning them to Spaces. Feedback welcome; planning to evolve this into a Spoon and eventually a full macOS app.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apps · Missing: agents, agent, cursor
81%81% 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 · Missing: supports, reddit linkedin, podcasting
79%79% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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
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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