Launching today
FeedbackFalcon
Happy clients. Happy developers. Zero debugging friction.
4 followers
Happy clients. Happy developers. Zero debugging friction.
4 followers
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.
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! ☕️
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@feedbackfalcon This is a clever approach to the reproduction problem. Piping runtime context directly into the IDE via MCP is genuinely different from the dashboard-first tools out there. The real test will be how well developers actually adopt it when they're deep in a sprint—does the context feel natural to work with, or does it add another layer of context-switching.
@osakasaul Spot on, and that exact friction is what we wanted to eliminate! The real magic here is that there is zero context-switching because the developer never actually leaves their IDE.
During a sprint, developers don't need to tab over to Jira, Linear, or even our dashboard. The LLM pulls the visual and runtime context natively via MCP, writes the fix, and handles the workflow tracking autonomously.
But getting the context in is only half the equation. The second half is what engineering leaders are really latching onto: using FeedbackFalcon as an accounting ledger for AI fixes to prevent 'cognitive debt.'
Right now, when an AI fixes a bug locally, the reasoning vanishes. Months later, teams might look at a block of AI-generated code and struggle to piece together the exact 'why' behind those specific design choices. The original intent becomes totally opaque.
We built the FeedbackFalcon MCP server to support exactly this. Once the AI agent finishes coding the fix, it can autonomously log its exact prompt, tool usage, and reasoning straight back to the ticket (which immutably syncs to Jira/Linear/GitHub).
The agent can use mcp_feedbackfalcon_update_task to append this to the task description, or mcp_feedbackfalcon_create_comment to log it as a new thread. It even supports an is_internal flag, allowing the AI to leave its reasoning as a private note for the engineering team without cluttering the public-facing ticket!
Code is cheap now, but context is expensive. We built this so developers can stay locked in their IDEs, while engineering teams never lose the 'why' behind the code!