Back to case studies
2021-2023Product team
Resilient Data Collection Tooling
Data extraction tooling for a changing web environment with persistent anti-bot friction.
High-friction web
Resilience
HTTP + JS analysis
Resilience model
Reliability comes from understanding the request flow.
Stability improved through network and JavaScript analysis, not endless retries.
1Map request flows and client-side logic
2Adapt extraction logic to drift and anti-bot friction
3Reduce firefighting and restore a predictable workflow
R&D Data Collection Engineer
PythonWeb scrapingReverse engineeringPlaywrightClickHouse
Problem
Standard collection approaches kept breaking because of page drift, client-side logic, and defensive mechanisms.
Solution
Worked at the intersection of Python, HTTP, and JavaScript reverse engineering: traced request flows, adjusted extraction logic, and hardened the pipeline.
Impact
Less firefighting, more predictable extraction.
What I built
- Reworked unstable request flows into repeatable extraction logic.
- Improved resilience to anti-bot changes and page-structure drift.
- Balanced delivery speed with reliability under constant external change.
What this proved
- Data collection is an infrastructure problem as much as an extraction problem.
- Stability comes from understanding the request model, not from brute-force retries.
Related work
More projects
2025-2026
nnzen model catalog
A live catalog with 500+ model cards that makes model research less scattered.
PythonFastAPILLM APIs
2025
Custom Agent Core with MCP
LLM core with a plugin execution layer for a developer assistant: hot reload, tool chains, and explicit context handoff.
PythonFastAPIMCP