C9watch – macOS menu bar app to monitor all Claude Code sessions

Hacker News

I was running Claude Code across 10+ terminal tabs and constantly switching between them to check which session needed permission, which was done, which was idle. Tried existing tools but they required launching sessions from within their app — I wanted to keep using my own terminals. c9watch scans running processes at the OS level and reads from `~/.claude/` to detect every active Claude Code session automatically. Works with any terminal or IDE — VS Code, Zed, iTerm2, tmux, Ghostty — no plugins or workflow changes needed. The tray popover gives you a quick overview. The full dashboard lets you: - Group sessions by status or by project (with git branch info) - Expand any session to read the full conversation — formatted markdown, code blocks, tool calls - Stop sessions, rename them, or jump to the parent terminal/IDE - Get native macOS notifications when sessions need attention - Use a WebSocket-based web/mobile client via QR code Built with Tauri (Rust + Svelte), not Electron. Rust handles process scanning and JSONL parsing. The binary is small and memory usage is minimal. MIT license, no telemetry. GitHub: https://github.com/minchenlee/c9watch

Visit product View on Hacker NewsFeb 27, 2026

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

95%95% 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

59%59% 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

46%46% 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 · Strong signals: host, calls · 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 · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

Correct prediction on native model

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