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VibeBar – macOS Menu Bar Monitor for Claude Code, Codex and OpenCode

Hacker News

VibeBar – macOS Menu Bar Monitor for Claude Code, Codex and OpenCode

I've been running multiple AI coding sessions in parallel and kept losing track of which one was thinking, which one was waiting for my input, and which one had quietly finished. Alt-tabbing through terminals every 30 seconds got old fast. VibeBar is a lightweight macOS menu bar app that solves this. It shows real-time session states (running / awaiting_input / idle / stopped) for Claude Code, Codex, and OpenCode without you leaving your current window. How it works Rather than relying on a single detection method, it uses three data channels and merges them: 1. PTY wrapper (vibebar): wraps the CLI in a pseudo-terminal and monitors output patterns to infer interaction state with high fidelity. 2. Plugin events (vibebar-agent): a local Unix socket server that receives lifecycle events from dedicated hooks in Claude Code and OpenCode. 3. Process scanner: a fallback that runs ps to detect running tool processes when the stronger channels aren't available. State priority when channels conflict: running > awaiting_input > idle > stopped > unknown. Honest caveats - Without the plugin, awaiting-input detection uses regex heuristics on terminal output — it works but isn't perfect. - Codex has no plugin event channel yet, so it relies on the PTY wrapper or process scanner. - Automated test coverage is still thin. - macOS 13+ only. Built in Swift 6.2 with strict concurrency. The menu bar icon has four styles (Ring, Particles, Energy Bar, Ice Grid) and supports English, 中文, 日本語, 한국어. Source and releases: https://github.com/yelog/VibeBar Happy to answer questions about the PTY/plugin architecture or the Swift implementation.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, agent · Missing: agents, cursor, model
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.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para · Missing: reddit linkedin, podcasting, created
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para, scanner · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
24%24% 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
19%19% 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
16%16% 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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