SpacePigeon – Save and Restore macOS Workspaces

Hacker News

I switch between a few different “modes” on my Mac every day (work, coding, meetings, personal stuff). Each time I’d end up reopening the same apps and dragging all the windows back into the right Spaces. Super repetitive and honestly kind of annoying. So over the past few weeks I hacked together a small open-source tool called SpacePigeon that basically remembers your whole workspace and puts everything back where it was. Things it can do: Save your current layout (apps + windows + Spaces) Restore it with one click or a hotkey Keep separate presets (like “Work”, “Coding”, “Meetings” etc.) Handle external monitors Example: I hit my “Work” preset and it opens all the usual apps and moves them to the right desktops automatically. Saves me a few minutes every time. If anyone here lives in Spaces like I do, you might find it useful. Here’s the repo (MIT, free): https://github.com/louivers/spacepigeon Totally open to feedback — still very much a work in progress.

Visit product View on Hacker NewsNov 26, 2025

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

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

39%39% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

18%18% 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.

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