Developer Rebuilds Chrome Extension in 7 Days Using Claude After Google MV3 Migration Killed Original

✍️ OpenClawRadar📅 Published: March 18, 2026🔗 Source
Developer Rebuilds Chrome Extension in 7 Days Using Claude After Google MV3 Migration Killed Original
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A developer rebuilt a Chrome extension, its API, website, and a QA agent in 7 days using Claude after Google's Manifest V2 to V3 migration killed the original version, which had taken almost a year to build and had tens of thousands of users.

What the Extension Does

The Chrome extension finds real discounts on Amazon products users are already searching for, not random coupon codes. It scrapes across 21 Amazon domains (including US, UK, DE, JP) with different languages, currencies, and page structures. Every discount a user finds gets automatically shared with the community, and every discount the community finds gets shared back to the user.

The Rebuild Process

The developer fed Claude the entire legacy codebase and asked it to:

  • Map every module and dependency
  • Identify bugs and redundancies
  • Propose a better architecture
  • Suggest cheaper solutions for scale

Claude found issues they'd lived with for years, identified redundancies in the scraping logic, and proposed restructuring how domain-specific adaptations are handled across the 21 Amazon sites.

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Technical Stack

  • Claude - core development, code analysis, architecture decisions, scraper logic
  • ChatGPT - prompt engineering, design direction, UX ideation
  • Vercel - deployment for the website
  • Custom QA agent - error monitoring + auto-fix proposals

Results After First Week

  • 4,000 new installs
  • High stability
  • Users opening the extension on almost every Amazon search
  • Most common feedback: "It's so simple to save money"
  • 99% coupon success rate (vs. ~10-20% on most competitors)

Key Challenge

Amazon isn't one website - each domain has slightly different HTML structures, price formats, and coupon display logic. Claude handled the initial mapping and domain-specific adaptations, with human fine-tuning.

The team also built a QA agent that monitors production errors in real-time, analyzes the context, and proposes fixes - essentially an always-on QA engineer.

📖 Read the full source: r/ClaudeAI

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👀 See Also