This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
FeedbackFalcon
Happy clients. Happy developers. Zero debugging friction.
Most feedback tools hand you a screenshot and leave you trying to reproduce the bug locally. We built an MCP server to skip that step. When a client reports an issue, FeedbackFalcon grabs the actual browser state, including the DOM, console logs, and network requests, and pipes it directly into Cursor or Claude. Your AI assistant gets the exact runtime data from the failing session. It does not have to guess what broke. The bug's exact state just shows up in your editor, ready to fix.
We built FeedbackFalcon because we got tired of the "it works on my machine" loop.
If you do client work, you know the drill. A client says "the checkout button is broken" and attaches a cropped screenshot in a Word document. You spend the next three hours trying to reproduce the error locally.
The problem with existing tools
Most visual feedback widgets stop at the screenshot. They show you what the bug looks like, but not why it is happening. AI coding assistants are great, but if you ask them to fix a bug without the runtime context, they just guess.
What we built
We didn't want to build another standard feedback widget. We wanted a way to get the bug's actual state into the editor.
Here is what FeedbackFalcon does:
Context capture: When a user flags an issue, we grab the DOM state, console errors, and network requests directly from their session.
The MCP pipeline: Instead of making you read logs on a dashboard, we pipe the failing data straight into your IDE using a Model Context Protocol (MCP) server.
No reproduction needed: Your AI assistant gets the actual failing state of the user's browser. It reads the context and suggests a fix, without you having to trigger the bug yourself.
We are trying to skip the detective work. We'd love for you to try it out.
Let us know how your AI handles the context, and drop any questions below. We'll be in the comments all day! ☕️
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About FeedbackFalcon on Product Hunt
“Happy clients. Happy developers. Zero debugging friction.”
FeedbackFalcon was submitted on Product Hunt and earned 9 upvotes and 3 comments, placing #57 on the daily leaderboard. Most feedback tools hand you a screenshot and leave you trying to reproduce the bug locally. We built an MCP server to skip that step. When a client reports an issue, FeedbackFalcon grabs the actual browser state, including the DOM, console logs, and network requests, and pipes it directly into Cursor or Claude. Your AI assistant gets the exact runtime data from the failing session. It does not have to guess what broke. The bug's exact state just shows up in your editor, ready to fix.
FeedbackFalcon was featured in Software Engineering (42.4k followers), Developer Tools (511.7k followers) and Artificial Intelligence (467.3k followers) on Product Hunt. Together, these topics include over 163.5k products, making this a competitive space to launch in.
Who hunted FeedbackFalcon?
FeedbackFalcon was hunted by Abner Rojas. 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.
Want to see how FeedbackFalcon stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Abner Rojas
Hey Product Hunt! 👋
We built FeedbackFalcon because we got tired of the "it works on my machine" loop.
If you do client work, you know the drill. A client says "the checkout button is broken" and attaches a cropped screenshot in a Word document. You spend the next three hours trying to reproduce the error locally.
The problem with existing tools
Most visual feedback widgets stop at the screenshot. They show you what the bug looks like, but not why it is happening. AI coding assistants are great, but if you ask them to fix a bug without the runtime context, they just guess.
What we built
We didn't want to build another standard feedback widget. We wanted a way to get the bug's actual state into the editor.
Here is what FeedbackFalcon does:
Context capture: When a user flags an issue, we grab the DOM state, console errors, and network requests directly from their session.
The MCP pipeline: Instead of making you read logs on a dashboard, we pipe the failing data straight into your IDE using a Model Context Protocol (MCP) server.
No reproduction needed: Your AI assistant gets the actual failing state of the user's browser. It reads the context and suggests a fix, without you having to trigger the bug yourself.
We are trying to skip the detective work. We'd love for you to try it out.
Let us know how your AI handles the context, and drop any questions below. We'll be in the comments all day! ☕️