u/infinitely_zero

Problem

Like any side project, Remarc started with my own problem: there was no good way to give AI coding agents contextual feedback on their own output.

There are plenty of tools for collaborating with humans, but surprisingly few for collaborating with AI. Chat was not cutting it for the feedback I wanted to give:

This thing in the implementation plan? Change it to that. What did you mean here? See this button? Here is a screenshot. I circled it because it is missing a hover state. This sentence in the third paragraph? Rephrase it.

If you are an opinionated builder, this gets clunky quickly. You either dictate paragraphs of messy feedback or resolve every detail one by one. AI is good at parsing mess, but garbage in, garbage out still applies.

I could get products to 90% quickly. The last 10% of feedback and polish was the bottleneck. So I built Remarc.

Remarc lets you select text in any Mac app, capture and annotate a screenshot, or comment on a web element. Each comment keeps its original context, status, and session. A connected agent can read the session, work through it, update statuses, and leave resolution summaries over MCP.

Think of it like leaving comments on a google doc vs sending a long rambling feedback note via Slack - the more granular you get with your feedback, the more it benefits from structure & better context.

Comparison

The closest tools I found are CuePin, which focuses on annotated screenshot handoff, and Agentation, which focuses on web UI feedback. Remarc combines those workflows across macOS: selected text, screenshots, and web elements in one persistent session on an OS-level that your agent can read and update.

It also includes exports and webhooks if you want to customize the handoff.

Pricing

$0. Remarc is free and open source, with no account, subscription, or telemetry. It requires macOS 14 or later.

Download: https://remarc.app
Source: https://github.com/metedata/Remarc

About the developer

I’m Mete, the developer of Remarc. I'm also the developer of Relinq which debuted on this subreddit.

Github: https://github.com/metedata
LinkedIn: https://www.linkedin.com/in/mete-polat/
Contact: mete@metedata.com, Metedata LLC
Privacy Policy: https://docs.remarc.app/reference/data-and-privacy/

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As I've been building out my knowledge base setup with the agent as an intermediary, I've been thinking a lot about the friction trade-off.

As you decrease the friction between AI and your knowledge base, you increase the friction for yourself. Here's what I mean:

Craft, Apple Notes, or Notion may feel better for the human because you get infinite customization, control, and ways to access your data directly. But now updating some dynamic sub-field in some database in your Notion habit tracker takes 150 tool calls for your LLM.

Obsidian is not as polished or comfortable as the other apps, but it operates on top of local files. And those files “live” closer to the AI on the same machine - it can directly write to and manipulate them without having to go through an MCP on a remote server.

A useful way to pick your tool - decide who is the primary actor. If you are primarily writing, logging, reviewing, and living in the app yourself, optimize for your own friction. If you want the agent to live on top of your knowledge base, optimize for agent friction, where local files and simple formats win.

This is one of the principles I've been thinking about as I've been building out and experimenting with my own setup which I describe here in more detail: https://substack.com/home/post/p-198511065