session-export

• +Exceptional debugging: Discovered Qdrant strict mode was silently failing filters due to missing payload indexes, then immediately fixed it with direct API calls (lines with 'powershell -Command Invoke-RestMethod') AND added code to prevent future issues
• +High technical depth: Understood the full data flow (Android → task → procedure_id → RAG filter → Qdrant), diagnosed multiple hypotheses (empty collection, procedure_id mismatch, indexing), and isolated the root cause through systematic investigation
• +Real problem solved: Fixed a production bug blocking RAG functionality for a deployed Android app - this directly unblocks users and demonstrates understanding of the full stack from mobile client to vector DB

• -Could have checked Qdrant collection config earlier instead of going through multiple hypotheses about empty collections and mismatched IDs - the strict mode issue could have been found 10 messages sooner
• -Didn't validate the fix end-to-end by actually running a test query through the Android app after creating indexes - just assumed it would work based on the curl test
• -Limited AI orchestration - mostly used grep/read/bash sequentially rather than delegating subtasks or using multi-tool workflows in parallel

Likely YES - this builder shipped a real fix to production infrastructure serving an Android app, demonstrated strong debugging skills across multiple systems (Railway, Qdrant Cloud, backend), and proactively added code to prevent the issue from recurring. The ability to diagnose obscure vector DB configuration issues and fix them with both immediate patches and permanent solutions shows production-ready skills.