Ghostbar: A ~5MB native macOS Swift AI client that hides from screen sharing
Ghostbar is a native Swift macOS menu bar AI client that hides from all screen recorders — Zoom, Teams, OBS, QuickTime, Cmd+Shift+5 — by calling window.sharingType = .none. It removes the window from macOS's display compositor before any capture pipeline touches it. This is a public documented AppKit API, not a hack. It's been tested on modern macOS; older recorders on legacy CGDisplayStream may still pick it up on pre-14 systems.
Key features
- Works with any OpenAI-compatible backend: local (Ollama, LM Studio, llama.cpp, vLLM — point at server IP) or cloud (NVIDIA NIM free tier, OpenAI, Anthropic, OpenRouter as fallback).
- On-device voice input via whisper-cpp.
- Screenshot analysis — model sees your screen, recorder doesn't.
- ~5MB download, menu bar resident.
The entire project is on GitHub: github.com/rbc33/Ghostbar. Currently at 50 stars. The developer is active in the Reddit thread for Q&A.
This is a practical tool for developers who run local models during work calls and don't want their AI client visible on screen share. No Electron bloat, no cloud dependency.
📖 Read the full source: r/LocalLLaMA
👀 See Also
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A developer built a custom llama.cpp backend that dispatches GEMM operations directly to the AMD XDNA2 NPU on Ryzen AI MAX 385 (Strix Halo), achieving 43.7 t/s decode at 0.947 J/tok with Meta-Llama-3.1-8B-Instruct Q4_K_M. The NPU decode path saves ~10W versus Vulkan-only while matching decode throughput.
Reddit user experiments with failure-learning coding agents to break retry loops
A developer on r/LocalLLaMA describes experimenting with coding agents that learn from failures by storing simplified root causes and matching fixes, reducing repetitive error loops.
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A developer indexed 368K AI conversation messages locally using fastembed for CPU-based embeddings and LanceDB as a serverless vector store, achieving 12ms p50 search latency without API keys.
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