AI Code Debugger — find and fix broken website code with AI!

A debugger is only as good as the report you give it. "It doesn't work" gets a guess; a tight report gets the fix. Include four things:

• The code that fails — paste the relevant HTML, CSS, and JS together, not one fragment. A CSS bug is often caused by markup three lines away, and a JS error usually needs the element it targets.
• The exact symptom — "the dropdown flashes open then closes" is actionable; "the menu is broken" is not.
• What you expected instead — the intended behavior tells the AI which of several plausible fixes you actually want.
• The error text, verbatim — copy the console message in full, including the line number and the stack trace. That single string often pins the cause immediately.

If the bug is visual, paste a screenshot of the broken UI alongside the code — the image-to-code reader sees the misalignment you are describing and ties it back to the offending rule.

Front-end bugs cluster into a handful of recurring shapes, and an AI debugger is quick on all of them:

1. Structural HTML — an unclosed <div>, a block element nested inside an inline one, or a duplicate id that quietly breaks a script. The debugger reads the tree and flags the invalid nesting.
2. CSS specificity and layout — a rule overridden by a more specific selector, a flex child that will not shrink, a z-index that does nothing because the element has no stacking context. It names the winning rule and why.
3. JavaScript reference and timing — Cannot read properties of null from a script that runs before the DOM is ready, or a listener attached to an element that does not exist yet.
4. Responsive breakage — a layout that holds on desktop and collapses at one breakpoint, usually a fixed width or a missing flex-wrap.

What it will not do is know your business logic. It fixes what the code says; you still confirm the code says what you meant.

A fix you cannot verify is a liability. Keep the loop tight and reviewable:

• Isolate before you paste — reduce the page to the smallest block that still reproduces the bug. A focused snippet gets a focused fix instead of a rewrite of things that already worked.
• Read the explanation, not just the diff — the value is understanding why it broke, so the same class of bug does not reappear two sections later.
• Diff the result — drop the corrected code next to the original in your editor or Git so you see exactly what changed and nothing sneaks in.
• Regenerate when repair costs more than replacement — if a section is tangled beyond a clean patch, generate a fresh, working version of that block and wire it back in.

Every corrected block exports as plain HTML, CSS, and JavaScript, so the fix lives in your codebase and your Git history like any other change you own.

These composite scenarios reflect the most common ways developers reach for an AI debugger — treat them as starting blueprints, not customer testimonials:

• The dev stuck on a CSS override. Has spent twenty minutes on a button color that will not change, buried in a long stylesheet. Pastes the CSS, gets told exactly which selector wins and why, and rewrites the rule cleanly without an !important arms race — back to real work in two minutes.
• The no-code site owner with a dead form. Built a site visually, but the contact form silently fails and there is no error to search for. Pastes the exported HTML and script, learns the listener was bound before the element existed, and drops in a corrected version that submits and validates.
• The freelancer inheriting a broken page. Takes over a client site that collapses on mobile with no documentation. Isolates the failing section, gets the responsive bug explained and fixed, then regenerates the two blocks that were beyond repair — delivering a working page without a full rebuild.

For current independent feedback on the platform, check Trustpilot, Capterra, and G2 — and sort by date: recent reviews of the AI builder matter more than the historical average.