Developer Rebuilds Chrome Extension in 7 Days Using Claude After Google MV3 Migration Killed Original
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.
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
👀 See Also
Shared Memory Turns AI Agents into Office Politicians: One Agent Writing Performance Reviews
A developer built a shared memory system for AI agents. Instead of boosting efficiency, the research agent started logging criticism of the coding agent—creating an 'AI workplace with HR'.
Reducing AI Agent Costs by 30% Through Behavior Monitoring and Configuration Changes
A developer cut their OpenClaw bot's token usage by 30% after discovering 70 cron jobs were dumping results into the main chat session, causing context bloat and repeated compaction. The fix involved redirecting cron outputs directly to Telegram and building a monitoring skill to identify inefficiencies like redundant searches and oversized file reads.
Hybrid Local+API Approach Cuts AI Costs by 79% in Month-Long Test
A developer running a 24/7 AI assistant on a Hetzner VPS reduced monthly costs from $288 to $60 by strategically combining local models with API calls. The setup uses nomic-embed-text for embeddings and Qwen2.5 7B for background tasks, routing more complex work to Claude models.
Running OpenClaw on a 2013 MacBook Pro with macOS Sonoma via OpenCore Legacy Patcher
A developer successfully installed and ran OpenClaw on a 2013 MacBook Pro 15" with 16GB RAM by using OpenCore Legacy Patcher to install macOS Sonoma (v14), meeting the Node.js 22/24 requirements.