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
Agent Browser Shield: Free OpenClaw Extension Blocks Prompt Injection & Dark Patterns
PixieBrix releases Agent Browser Shield, a free source-available browser extension for OpenClaw that blocks prompt injection, dark patterns, and context pollution while cutting token usage.
lazyclaude: A TUI for Managing Claude Code Configuration
lazyclaude is a terminal user interface tool inspired by lazygit that provides a single view for managing all Claude Code configuration stored on disk, including memory files, skills, agents, MCP servers, settings, permissions, hooks, keybindings, sessions, stats, plugins, and todos.
memv MCP Server: Persistent Structured Memory for AI Agents
memv, an open-source Python memory layer for agents, now ships with an MCP server. It provides five tools for persistent, structured memory with per-user isolation and LLM-optional extraction.
GLM-5-Turbo Shows Low Tool Call Error Rate in User Testing
The z-ai/glm-5-turbo model demonstrates a 0.57% average tool call error rate in testing, significantly lower than GLM-5's ~3% rate. A user reported successfully using it with a CLI tool to write a 97,000-word fantasy novel with minimal issues.