Remarc – better contextual feedback for AI agents

Hacker News

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. Remarc is free and open source, with no account, subscription, or telemetry. https://remarc.app/ https://github.com/metedata/Remarc

Visit product View on Hacker NewsSep 8, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, way, para · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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