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We’re building an agent (MK1) that does large-scale competitor analysis across dozens of newsletters automatically! Scraping → Structuring → Compressing → Multi-LLM analysis → aggregation → dashboards. If it were as simple as “drag a few n8n nodes,” trust me, we’d be doing that. Allow me to elaborate: 1. The data sources we pull from are NOT friendly to scrapers. - Requests get blocked instantly - HTML structure changes unpredictably - Anti-bot systems shut down your pipeline mid-run - Content loads dynamically - Layouts differ per issue - Rate limits kick in - Rendering methods break your parser When you have to keep the entire structure consistent for downstream LLM analysis, a single DOM change breaks the whole chain. No-code tools don't handle that kind of fragility well. 2. The content isn’t simple text, it requires meaningful structure. When you’re analyzing 30–100 newsletters at a time, you need: - Section extraction - Visual mapping - CTA identification - Ad block recognition - Tone markers - Intent patterns - Word & emoji stats - Structural compression (to cut token costs by ~70%) 3. Real orchestration > visual workflows People underestimate what happens when you’re: - Running 40+ analysis jobs in parallel - Retrying failed tasks - Re-queuing partial data - Handling timeouts - Managing token budgets - Caching compressed representations - Tracing every run end-to-end - Ensuring idempotency 4. Maintaining the scraper is half the battle When the website changes structure (which happens often), your scraper must: - adapt automatically or - be fixable with minimal downtime You cannot do that reliably in a visual builder. These aren’t static URLs. Each issue is rendered differently and sometimes changes backend structure.Our scraping approach has to evolve constantly. Even a small structure shift breaks an entire n8n chain.