Jeremy Longshore

When you’re building automation systems that need to work reliably across multiple brands and content categories, the quality of your data validation becomes critical. This week, I tackled the challenge of designing a scalable RSS feed validation and content distribution architecture - and learned valuable lessons about system design, data quality, and professional problem-solving.

The Challenge

I was tasked with building a content distribution system for three distinct brands (Intent Solutions, StartAITools, and DixieRoad), each requiring different content types from RSS feeds. The initial approach seemed straightforward: collect RSS feeds, categorize them, and route content to the appropriate brand.

The problem: Not all RSS feeds are created equal. Some redirect, some time out, some return HTML instead of XML, and some simply don’t exist anymore. Without proper validation, the entire automation system would fail silently or waste resources on broken feeds.

The real challenge wasn’t just finding feeds - it was building a reliable, maintainable validation system that could scale across different content categories and ensure long-term system health.

The Approach: Systematic Problem-Solving

Phase 1: Understanding the Scope

Instead of jumping straight to implementation, I started with research:

Key learning: When you inherit or discover scattered data, consolidation should be your first priority - not building on top of chaos.

Phase 2: Building Validation Infrastructure

I created automated bash scripts to test feeds against specific criteria:

test_feed() {
    local name=$1
    local url=$2

    response=$(curl -s -o /dev/null -w "%{http_code}" --max-time 10 "$url" 2>/dev/null)

    if [ "$response" = "200" ]; then
        content_type=$(curl -s -I --max-time 10 "$url" 2>/dev/null | grep -i "content-type")
        if echo "$content_type" | grep -qi "xml\|rss\|atom"; then
            echo "✅ PASS"
            return 0
        fi
    fi
    echo "❌ FAIL (HTTP $response)"
    return 1
}

Key learning: Automation isn’t just about speed - it’s about creating repeatable, reliable processes that others can use and maintain.

Phase 3: Iterative Validation and Discovery

The validation process revealed surprising insights:

Key learning: Real-world data is messy. Your validation process needs to handle edge cases gracefully and provide actionable failure reasons.

Phase 4: Creating Centralized Architecture

Instead of maintaining multiple feed lists across projects, I:

  1. Created a master MASTER-RSS-FEEDS.md in the brainstorm repository
  2. Organized feeds by category (16 total categories)
  3. Documented validation status and failure reasons
  4. Created symlinks from other projects to the master list
  5. Built a CSV version for programmatic access

Professional insight: Technical decisions about data structure have organizational impact. A well-organized master list becomes the foundation for team collaboration and system reliability.

The Work: What Was Built

Validation Infrastructure

Content Distribution Architecture

Documentation and Knowledge Management

Data Infrastructure

Professional Growth and Insights

What I Learned

1. Validation Before Implementation Early in the process, I made the mistake of proposing feeds without testing them. User feedback (“did u try the new feeds to ensure they match criteria for tier 1”) was a wake-up call - validation needs to happen before design, not after.

2. Data Consolidation is Non-Negotiable Finding scattered feed lists across 5+ locations taught me that system reliability starts with data organization. Creating a single source of truth wasn’t just convenient - it was essential for maintainability.

3. Failure Documentation is as Valuable as Success Documenting why 45 feeds failed (with specific HTTP codes and reasons) creates institutional knowledge. Future team members can learn from these failures and avoid wasting time on known bad sources.

4. Real-World Systems Require Iterative Validation The repair/maintenance feeds had a 23% success rate compared to 75% for tech feeds. This taught me that validation criteria need to be category-aware and that assumptions about data quality need constant testing.

5. Professional Problem-Solving is Iterative I went through multiple rounds of testing and refinement:

This iterative approach - test, learn, refine - is how real professional work gets done.

Skills Demonstrated

Technical Architecture:

Data Engineering:

DevOps & Automation:

Problem-Solving:

Professional Communication:

Impact and Results

Immediate Outcomes

Long-Term Value

Professional Development

This project demonstrated my ability to:

Looking Forward

This project revealed several areas for continued growth:

1. Automated Health Monitoring The validation scripts should run on a schedule (weekly) with alerts for feed failures. This ensures the system stays healthy without manual intervention.

2. Quality Score Refinement The current quality scoring (1-5 scale) could be enhanced with:

3. Multi-Source Content Aggregation Beyond RSS feeds, the validation framework could extend to:

4. Team Collaboration Infrastructure Building on this foundation, the next challenge is creating shared dashboards and alerting systems that make feed health visible to the entire team.

Key Takeaway

The most valuable professional skill isn’t knowing how to build things perfectly the first time - it’s knowing how to approach problems systematically, learn from failures, and create maintainable solutions.

This project started with a simple request to validate RSS feeds and evolved into building a scalable, documented, multi-brand content distribution architecture. Along the way, I learned that real professional growth happens when you:

  1. Question assumptions (not all feeds work)
  2. Validate before building (test feeds first)
  3. Consolidate before scaling (single source of truth)
  4. Document failures (institutional knowledge)
  5. Iterate relentlessly (test, learn, refine)

The result isn’t just a working system - it’s a reusable framework that demonstrates technical leadership, problem-solving ability, and commitment to sustainable engineering practices.

Technologies used: Bash scripting, Git/GitHub, RSS/XML validation, CSV/JSON data structures, n8n automation, system architecture design

Project repository: rssatoms-tier1-feeds

#Systems-Architecture #Automation #Content-Strategy #Data-Validation #Problem-Solving