From Screenshot to Solution: Stop Forwarding Me Vague Bug Tickets

"The hardest part of fixing a bug isn't the code. It's deciphering what the user meant by 'it made a weird noise on Tuesday.'"

Bug reporting in 2026. Still a game of telephone. Still completely, fundamentally broken.

"It's not working." — an actual, verbatim Jira ticket description sitting in your queue right now, assigned to you by a PM who didn't ask any follow-up questions.

We live in a timeline where AI can literally write production code for us, yet the standard industry bug report is: a screenshot cropped to a vague red box, pasted into Slack, followed by four words and the fervent prayer that the developer receiving it is also a certified psychic.

By the time that ticket reaches an AI coding agent, the context has fully decayed. The agent writes the wrong fix. You spend 20 minutes explaining to the agent what was actually broken because the user didn't tell you. Classic workflow. Very efficient.

Enter AI Feedbacks — which exists specifically because somebody got tired of receiving "it doesn't work" as actionable development context.

The Problem: Context Decay (AKA Your Entire Morning Wasted)

When a bug fires on the client side, a bunch of incredibly useful information exists for exactly a few seconds: the DOM state, the console errors, the failed network requests, the exact UI configuration at the exact moment of failure.

Then the user immediately closes the tab, writes "the button did something weird," attaches a screenshot of the completely wrong screen, and hits submit.

By the time that ticket reaches an AI agent like Cursor or Windsurf, the prompt is so thin it's basically a riddle. Developers end up spending 20 minutes reconstructing the crash scene before they can even ask the agent to start fixing it. Coding agents hallucinate because they have zero context. Everyone loses.

The technical term is "context decay." The honest term is "why does this keep happening every single sprint."

The Solution: Give Your Agent Enough to Actually Work With

AI Feedbacks captures the full crash scene at the absolute moment it happens — screenshot, DOM context, console errors, failed network requests — and synthesizes it into a fully structured, agent-ready prompt. No manual log-copying. No vague Jira descriptions. Just: here is exactly what broke, in the precise format your agent needs to fix it in one pass.

Google Gemini 3 Flash Preview does the heavy lifting:

// The AI processing pipeline
const result = streamText({
  model: google('gemini-3-flash-preview'),
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: `Analyze this bug: ${description}` },
        { type: 'image', image: screenshotBuffer },
      ],
    },
  ],
})

// The AI processing pipeline
const result = streamText({
  model: google('gemini-3-flash-preview'),
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: `Analyze this bug: ${description}` },
        { type: 'image', image: screenshotBuffer },
      ],
    },
  ],
})

Gemini identifies the broken component, cross-references the network logs, and returns a structured prompt. The kind that actually works on the first pass instead of requiring five clarification messages where the LLM apologizes to you repeatedly. Wild concept, I know.

How the Chrome Extension Captures Context

The extension runs silently in the background and on crop+submit it:

• Grabs the exact area of the screen the user flagged.
• Captures all console.error and console.warn from the session (all the ones you ignored).
• Packages failed network requests (4xx/5xx) from the last 60 seconds.
• Sends the whole thing to the backend without the user doing literally anything else.

The developer receives a ready-to-use prompt. Not a blurry screenshot. A prompt.

The Dashboard: Because You Also Need Last Tuesday's Bug

The web dashboard is built with natural language semantic search because nobody has time to remember the exact spelling of the tags they used in a ticket from two weeks ago.

Search for:

• "that auth error"
• "the white flash on mobile"
• "whatever was completely broken with the chart last week"

And it just finds it. Not because you tagged it correctly in a 14-step triage process. Because you described it like a normal human being.

The UI is monochrome, high-contrast, and deliberately boring-looking. The tool is the content. There are no decorative gradients competing for your attention while you're just trying to ship a fix and go to lunch.

Who This is Pointedly For

If you or your team uses Cursor, Windsurf, GitHub Copilot, or literally any agentic tool for development — you already know the dirty secret: the quality of the output is entirely, 100% constrained by the quality of the prompt you feed it. AI Feedbacks automates the hardest part of writing a good bug prompt: capturing and structuring the context at the exact moment it exists.

Feed your agents better context. Get first-pass fixes more often. It's not rocket science, it's just better tooling for lazy people (which is what engineers are).

Source code: github.com/Mic-360/ai-feedbacks