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Remarc
Your feedback layer for AI collaboration.
Remarc is the feedback layer between you and your coding agent. Point at anything on your Mac - text, screenshots, web elements, voice - and your agent reads and resolves your comments over MCP. Free and open source.
I built Remarc because I kept running into the same problem with AI agents: getting a product to 90% was fast, but communicating the last 10% of feedback was painfully clunky.
Real product work is a chain of feedback loops. Change this line in the plan. Clarify that paragraph. Add a hover state to this button. Rephrase the third sentence in this draft. Chat is great for broad direction, but not for pointing at the exact thing you mean. I kept ending up with long feedback dumps or handling every detail one at a time.
So I built the feedback layer I wanted for myself. Remarc is a native macOS app for capturing comments on selected text, annotated screenshots, and web elements. It keeps the context attached and organizes comments into sessions. When you hand a session off, Claude Code, Codex, Cursor, or another MCP-compatible agent can read, work through, and update each comment.
It started as a small experiment in commenting on text anywhere on my screen. As I used it, I added the forms of feedback I reach for every day: screenshots for visual QA, web selection for UI work, voice for quick capture, and sessions for keeping different projects separate.
The bigger lesson for me is that building with AI has a last-mile problem. Models can get us surprisingly far, but high-quality work still comes from taste, QA, and precise iteration. AI makes the first pass faster. It does not make the feedback loop disappear.
Remarc is open source, local-first, and has no accounts or telemetry. If you’re an opinionated builder who cares about that final layer of polish, I’d love to know: where does your feedback loop with AI agents break down?
About Remarc on Product Hunt
“Your feedback layer for AI collaboration.”
Remarc was submitted on Product Hunt and earned 5 upvotes and 1 comments, placing #54 on the daily leaderboard. Remarc is the feedback layer between you and your coding agent. Point at anything on your Mac - text, screenshots, web elements, voice - and your agent reads and resolves your comments over MCP. Free and open source.
On the analytics side, Remarc competes within Design Tools, Open Source, Developer Tools and GitHub — topics that collectively have 889.2k followers on Product Hunt. The dashboard above tracks how Remarc performed against the three products that launched closest to it on the same day.
Who hunted Remarc?
Remarc was hunted by Mete Polat. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Remarc including community comment highlights and product details, visit the product overview.
Mete Polat
Hey Product Hunt, Mete here 👋
I built Remarc because I kept running into the same problem with AI agents: getting a product to 90% was fast, but communicating the last 10% of feedback was painfully clunky.
Real product work is a chain of feedback loops. Change this line in the plan. Clarify that paragraph. Add a hover state to this button. Rephrase the third sentence in this draft. Chat is great for broad direction, but not for pointing at the exact thing you mean. I kept ending up with long feedback dumps or handling every detail one at a time.
So I built the feedback layer I wanted for myself. Remarc is a native macOS app for capturing comments on selected text, annotated screenshots, and web elements. It keeps the context attached and organizes comments into sessions. When you hand a session off, Claude Code, Codex, Cursor, or another MCP-compatible agent can read, work through, and update each comment.
It started as a small experiment in commenting on text anywhere on my screen. As I used it, I added the forms of feedback I reach for every day: screenshots for visual QA, web selection for UI work, voice for quick capture, and sessions for keeping different projects separate.
The bigger lesson for me is that building with AI has a last-mile problem. Models can get us surprisingly far, but high-quality work still comes from taste, QA, and precise iteration. AI makes the first pass faster. It does not make the feedback loop disappear.
Remarc is open source, local-first, and has no accounts or telemetry. If you’re an opinionated builder who cares about that final layer of polish, I’d love to know: where does your feedback loop with AI agents break down?