Show HN: Remarc – better contextual feedback for AI agents
Remarc is an open-source macOS application that serves as a feedback layer between users and AI coding agents, preserving rich context (text selections, screenshots, web elements, voice notes) attached to each comment It supports multiple input modalities: text selection, annotated screenshots, web page capture via Chrome extension, and on-device voice transcription (WhisperKit/Parakeet) with Crit Mode for longer reviews Remarc integrates with major AI coding agents (Claude Code, Codex, OMP, Cur
Analysis
TL;DR
- Remarc is an open-source macOS application that serves as a feedback layer between users and AI coding agents, preserving rich context (text selections, screenshots, web elements, voice notes) attached to each comment
- It supports multiple input modalities: text selection, annotated screenshots, web page capture via Chrome extension, and on-device voice transcription (WhisperKit/Parakeet) with Crit Mode for longer reviews
- Remarc integrates with major AI coding agents (Claude Code, Codex, OMP, Cursor, Claude Desktop) via the Model Context Protocol (MCP), enabling agents to read comments with full attached context without manual prompt reconstruction
- The app is privacy-first: all data stays on-device with no accounts or telemetry, and supports session-based organization, comment lifecycle management, Markdown/JSON export, and webhook integration
Why It Matters
Remarc addresses a critical gap in the AI coding agent workflow: the loss of contextual fidelity when humans hand off feedback to agents. By preserving exact selections, screenshots, and web context alongside comments, it eliminates the "paraphrase tax" that currently forces users to repeatedly reconstruct context for their agents. This approach could become a standard pattern for human-agent collaboration tools, especially as MCP adoption grows across the developer ecosystem.
Technical Details
- Architecture: Native macOS app built with SwiftUI + AppKit in Swift 6, running from the menu bar; uses Sparkle for self-updating; vendored MCP server bundled into the app
- Input modalities: Text selection (captures quote + source app), screenshot capture with annotation tools (arrows, shapes, blur, pixelation), Chrome extension capturing URL/CSS/layout/accessibility/React component data, on-device voice transcription via WhisperKit and FluidAudio with Crit Mode splitting recordings into separate comment cards
- Agent integrations: MCP-based connections to Claude Code, Codex, OMP (via public
remarc-agent-pluginsrepo, MIT-licensed), Cursor (configured by app), and Claude Desktop or any MCP client (manual); all clients receive identical context and MCP tools for reading comments, managing sessions, and resolving work - Data model: Comments organized into sessions (by review, project, or agent conversation), with lifecycle states (Open → Handed Off → In-Progress → Resolved), searchable History for deleted comments, and export as Markdown/JSON with configurable references and metadata; webhooks support automation tool integration
- Privacy: Zero accounts, zero telemetry; all comments and screenshots stored locally on Mac unless explicitly handed to an agent or sent via user-configured webhook
Industry Insight
- The Remarc model—preserving rich, structured context at the point of human feedback rather than requiring agents to infer it—represents a scalable pattern for human-AI collaboration tools; expect similar "context preservation layers" to emerge across design, product, and operations workflows beyond coding
- MCP's role as the integration backbone (rather than proprietary connectors) demonstrates the practical value of open agent protocols; projects that embrace MCP-first integration will have a significant distribution advantage as the agent ecosystem fragments across vendors
- The privacy-by-design approach (on-device storage, no telemetry, no accounts) is increasingly a competitive differentiator for developer tools, especially among enterprise and security-conscious users; this positioning could drive adoption in regulated or privacy-sensitive environments where cloud-based feedback tools face barriers
Disclaimer: The above content is generated by AI and is for reference only.