Nice framework design! One pattern I have found invaluable when working with dozens of APIs is adding automatic retry with exponential backoff and rate-limit header parsing. Most APIs return Retry-After or X-RateLimit-Reset headers — parsing these and sleeping accordingly saves you from getting banned. I have built clients for 78+ different APIs (Reddit, Spotify, HN, ArXiv, etc.) and the abstraction that pays off most is separating pagination strategy from the HTTP layer. Some APIs use cursor-based, others use offset — having a pluggable pagination interface keeps your client clean. Would love to see how you handle API-specific auth flows (OAuth2 vs API keys vs JWT) in your framework.

Great point about the limitations of AI-powered scrapers on dynamic content. In my experience building 78 production scrapers on Apify, the most reliable approach is combining headless browser rendering with structured selectors rather than relying on LLM extraction alone. For sites with heavy JavaScript, I use Crawlee with Playwright — it handles SPAs, infinite scroll, and auth walls much better than pure HTTP+AI parsing. The key insight: AI is best for understanding scraped data (classification, entity extraction), not for the scraping itself. Curious if you have benchmarked latency differences between LLM-based vs selector-based approaches?

Solid explanation. In my experience building data processing tools, the biggest win from Worker Threads came when handling JSON parsing of large API responses. Moving the parse step off the main thread kept the event loop responsive for incoming requests. One caveat: SharedArrayBuffer is great for numeric data but for string-heavy payloads, the serialization overhead can negate the benefit. MessagePort with transferable objects was the sweet spot for us.

Great breakdown. One thing I learned running serverless scraping pipelines: cold starts compound when you chain multiple API calls. I found that keeping a warm pool of 2-3 functions and using connection pooling (especially for DB writes) cut our P95 latency by 60%. For anyone doing data-intensive serverless work, batching API responses before writing to storage also helps avoid those timeout surprises.

Excellent guide! Running this locally with Ollama is smart — saves API costs and keeps data private. If you want to scale this further, consider adding a vector index alongside Neo4j for hybrid search: graph traversal for relationships + vector similarity for semantic queries. LangChain has a nice Neo4jVector integration that makes this surprisingly easy to set up.

Great writeup! Polars lazy evaluation is a game-changer for memory-constrained pipelines. One tip from my experience: combining scan_csv() with sink_parquet() lets you process files larger than RAM without ever loading them fully. For recurring ETL jobs, I found that Polars + DuckDB is an incredibly powerful combo — DuckDB can query Parquet files directly, so you get the best of both worlds.