Redis-Powered Crypto EdTech Content Pipeline: Scraping, Processing & Curation

This is a submission for the Redis AI Challenge: Beyond the Cache.

What We Built

With Nodelet, when we set out to create the "Duolingo for Crypto Finance," we knew that content curation would be our biggest challenge. Crypto education content scattered across the web is dense, technical, and frankly boring for most learners. Our solution? A Redis-powered content aggregation pipeline that transforms fragmented crypto resources into engaging, digestible lessons.
While we started with Redis as a high-performance data layer, our architecture is designed to leverage Redis's advanced AI and streaming capabilities as we scale.

The Architecture: From Simple Cache to AI-Powered Pipeline

Our current implementation uses Redis as a critical buffer between our scraping services and LLM processing, but the real innovation lies in how we've architected for Redis's next-generation features:

• 1. Intelligent Backpressure Management with Redis Streams Our scraping jobs run 3x faster than our Qwen3 LLM processing can handle. While we currently use Redis as a simple cache, we're migrating to Redis Streams for sophisticated flow control:

python
# Future implementation with Redis Streams
r.xadd("content_pipeline", {
    "source": "coinbase_learn", 
    "content": scraped_data,
    "priority": content_score,
    "timestamp": time.now()
})

• 2. Content Deduplication with Bloom Filters Crypto content is highly repetitive across platforms. Our next phase implements Redis Bloom filters to eliminate duplicates before expensive LLM processing:

python# Planned: Probabilistic deduplication
if not bloom_filter.exists(content_hash):
    bloom_filter.add(content_hash)
    # Process with LLM

• 3. Semantic Search with Vector Embeddings The real "beyond cache" magic happens when we implement Redis vector search for content similarity and recommendation:

python# Future: Vector-powered content recommendations
r.ft("content_idx").search(
    Query("@vector:[VECTOR_RANGE $radius $vec]")
    .sort_by("relevance")
    .return_fields("title", "difficulty", "vector_score")
)

How Redis Scales Our Vision

• Current State: Redis handles async scraping bottlenecks and data persistence between services.
  Future Ready:

• Redis Streams for real-time content processing pipelines
  Vector Search for semantic content matching and personalized learning paths

• Bloom Filters for efficient duplicate detection at scale
  Pub/Sub for real-time learning progress notifications

Demo

This is just the first draft - but here we demonstrate leveraging AI and Redis to develop our first draft lesson templates

Technical Impact

By using Redis as more than just a cache, we're building infrastructure that can:

• Process thousands of crypto articles daily without duplication
• Provide instant semantic search across educational content
• Scale to millions of learners with real-time personalization
• Handle complex data flows with built-in backpressure management

What's Next

It was so much fun building this! We started with only two days left in the competition, so some of it is still rough around the edges.
Redis's AI-focused features are transforming how we think about content pipelines. Our simple caching layer is evolving into an intelligent content brain that can understand, deduplicate, and recommend crypto education content at scale.

Team Submission

Evan: [e-mail]
Elliot: [e-mail]

Comments

[Elliot Sones]

🔥🔥🔥🔥🔥🔥🔥🔥🔥

[Evan Dickinson]

thanks everyone for all the feedback

[Evan Dickinson]

Also we're both active on Discord if you'd like to learn more about the project, and our interests! :))

[Ansell Maximilian]

Awesome! 🔥🔥

[Anik Sikder]

Really impressive architecture! I love how you’re evolving Redis beyond just a caching layer into a full AI-powered content pipeline. Using Redis Streams for backpressure management is a smart way to handle the speed mismatch between scraping and LLM processing. The integration of Bloom filters for deduplication shows great attention to efficiency crypto content definitely tends to get repetitive across sources.

The semantic search with Redis vector indexes sounds like the real game-changer for personalized learning and content recommendations. I’m excited to see how this scales and handles real-time updates with Pub/Sub.

Overall, this submission showcases a very modern, scalable approach to content curation and AI integration perfect for tackling the complexity of crypto education. Looking forward to seeing future iterations with those Redis AI features fully baked in!