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Claude Code sessions next to the macOS dock

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Claude Code sessions next to the macOS dock

Ever since I started orchestrating multiple agent sessions in Claude Code, it's always been a challenge to keep track of which sessions are ongoing, need my input and are complete. Sometimes, there are 5+ agents running at the same time. I usually keep a single terminal window with multiple tabs and an agent view tab. One thing I usually do is go do something else while my agents are working, like scrolling on X, reading articles and stuff. When I do that, i had no way to see what was happening in the agent sessions. I had to switch back to the terminal window to see the sessions state. sometimes i got carried away and 5 agents needed input for a long time. since we're all in a rant to be fast and break things, i wanted a way to have my sessions state always visible. so i created Port. i looked at the macOS dock and the empty space to its left, and I thought that's it. port is a tiny window that shows claude code sessions state. it fills the blank space to the left of the macOS dock and tracks its height so that it never interferes with anything. it persists across all desktops. we're using a unused space to show information. now whenever i'm doing something else, i get a view and a notification if a session needs my input or it's complete. it's a tiny project, there are probably other workarounds, so feedback is important. i've built this for the way that I work and that I would like to keep track of my sessions. hearing from you guys is what would make me actually keep building it for further improvements.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: cursor, model, apple
99%99% 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 · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
31%31% 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 · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
26%26% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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