AgentPulse – Claude Code and Codex status in tmux

Hacker News

I use Claude Code and Codex every day, often across several tmux panes. I kept switching windows to check which agents were still working and which needed an answer. I built AgentPulse so I could see that at a glance. It is a TPM plugin that uses lifecycle hooks to show working, waiting, idle, and interrupted states. It adds indicators to the window list and pane borders, with an optional popup and sidebar for jumping between agents. It works with your existing tmux theme. Because it is tmux you can customize it the way you want and optimize your workflow. This is an early version that grew out of my own setup. How do you keep track of agents in tmux? I would like to hear what would make this useful in your workflow. I know herdr exists, but couldnt leave my tmux setup.

Visit product View on Hacker NewsSep 9, 2026

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4points

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

91%91% 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.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

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

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

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

34%34% 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

23%23% 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

21%21% 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.

Correct prediction on native model

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