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I Let AI Rewrite My Bug Fix — Here's What Happened

#ai #productivity #debugging

Last week I hit one of those bugs. You know the kind — a race condition in a WebSocket handler that only appeared under heavy load, disappeared when you added logging, and laughed at your breakpoints.

After three hours of console.log archaeology, I decided to try something different: I fed the entire module to an AI coding assistant and asked it to find the bug.

The Setup

The codebase is a real-time collaboration tool (think Google Docs but for developers). The problematic module handled concurrent edits from multiple users. Here's a simplified version of the core issue:

// The sneaky race condition
async function applyEdit(userId, operation) {
  const current = await getDocumentState();
  const transformed = transformOperation(operation, current.version);
  await saveOperation(transformed);
  current.version++; // Not atomic with saveOperation!
}
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Classic TOCTOU (time-of-check-to-time-of-use). Two users hit getDocumentState() simultaneously, both get the same version, both transform, and the second overwrite silently wins.

What the AI Got Right

I prompted the AI with: "Review this module for concurrency issues." Within seconds, it:

  1. Flagged the exact race condition — with a clear explanation of the TOCTOU pattern
  2. Suggested an optimistic locking approach — using version checks in the DB query itself
  3. Offered three alternative patterns — mutex, CRDT, and operational transformation

Here's the fix it proposed:

async function applyEdit(userId, operation) {
  const { rows } = await db.query(
    `UPDATE documents SET 
       state = apply_transform($1, state),
       version = version + 1
     WHERE id = $2 AND version = $3
     RETURNING *`,
    [operation, docId, expectedVersion]
  );

  if (rows.length === 0) {
    // Conflict — retry with fresh state
    return applyEdit(userId, operation);
  }
}
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Clean. Correct. And it even added the retry logic I hadn't thought of.

What the AI Got Wrong

Here's where it gets interesting. The AI also "helpfully" suggested:

  • Adding a distributed lock via Redis (overkill for a single-server deployment)
  • Refactoring the entire module to use RxJS observables (scope creep disguised as architecture advice)
  • Implementing a full CRDT library (200 lines of code to fix a 3-line bug)

The pattern I noticed: AI is great at identifying problems and suggesting solutions, but it has zero sense of engineering trade-offs. It doesn't know your infra, your team size, or your deadline.

My Workflow Now

After this experience, I've settled on a hybrid approach:

  • Finding bugs → AI (it's relentless)
  • Choosing the fix → Me (context matters)
  • Writing the fix → Collaborative
  • Testing edge cases → AI generates, I curate

The key insight: AI is your pair programmer, not your tech lead.

The Identity Problem

One thing that tripped me up early: managing credentials and access across all these AI tools. Each one wants its own API key, its own auth flow, its own user management. I started using web3id.xyz to consolidate my developer identity across tools — one DID (decentralized ID) to rule them all. Way cleaner than juggling 15 different OAuth flows.

(Not an ad, just genuinely tired of managing ~/.env files with 47 API keys.)

The Verdict

AI-assisted debugging saved me roughly 2 hours on this particular bug. But more importantly, it changed how I think about debugging. Instead of diving straight into the code, I now spend the first 5 minutes describing the problem to an AI. Even when its suggestions are wrong, the act of articulating the issue clearly often reveals the answer myself.

The real productivity hack isn't the AI — it's the discipline of writing clear problem statements.

What's your experience with AI-assisted debugging? Have you found it more helpful for finding bugs or writing fixes? Drop a comment below.

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