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Automating AI News Translation with Claude Code

TL;DR

  • Claude Code executes scripts to auto-fetch X and LinkedIn AI news
  • YAML config drives translation pipeline, enabling batch processing
  • Claude API handles long text while preserving English for reference
  • Cron jobs provide daily automated updates to an intelligence feed
  • YAML-based configuration keeps data sources and rules maintainable

The Problem

For six months, I spent 40 minutes each morning scanning X and LinkedIn for AI developments. The English information overload was substantial—X alone produced hundreds of relevant tweets hourly, and LinkedIn’s algorithm favored local content, reducing my English signal density significantly. I calculated that only 15% of the 5 hours weekly spent curating content actually converted to useful knowledge. This inefficiency pushed me toward automation.

I initially assumed translation would be the primary hurdle. In reality, three blockers emerged: X’s API restrictions and rate limits for personal developer accounts, inconsistent translation quality with technical content losing context (like “Context Window” translated differently depending on article framing), and rapidly escalating maintenance costs as each new data source required script modifications.

The Solution

I decomposed the system into four independent modules: data source configuration, filtering rules, translation tasks, and output formatting. All configuration lives in YAML files while Claude Code handles execution. The sources.yml file defines monitored accounts, keywords like “LLM” and “agent,” and fetch limits per platform.

The core insight: separating “what to do” from “how to do it.” YAML defines data sources and scheduling strategy, Python scripts manage execution logic, and Claude handles translation quality with consistent terminology handling. A translate_task.py script loads configuration, fetches content from configured sources, and calls the Claude API with contextual information to maintain translation accuracy.

This configuration-driven approach means adding new sources or adjusting filters requires editing YAML—no code changes needed. The system runs on a 120-minute interval, pulling fresh content and generating bilingual reports that preserve original English alongside Chinese translations.

Key Takeaways

If you manage information streams from multiple platforms, separate configuration from logic to reduce maintenance burden.

If you translate technical content, provide context to Claude to ensure terminology consistency across documents.

If you build automated pipelines, use YAML or similar config files instead of hardcoded values to enable rapid iteration.

If you monitor social media APIs, implement request rate limiting to avoid triggering platform restrictions.

If you want usable outputs, always preserve original text alongside translations for reader reference.

Read the Full Article (Chinese)

This is an abstract. The full technical walkthrough, including complete code samples for sources.yml and translate_task.py, is available in Chinese in the original article.