4h • General Discussion
Every time I share the system we're building, at least one comment says: "Why not just use n8n? This is easy". So here's why... it's not :)
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.
Python lets us patch fast and deploy fast.
5. The UI we’re generating requires structured, reliable data
If input is unstable, the whole insight layer collapses.
6. And finally… this Reddit thread says it perfectly
Not going to paste it here, but if you’ve seen the developer post titled “Why I left n8n for Python”, it’s exactly the same set of problems we ran into...
- tasks become semantic
- concurrency grows
- failures multiply
- data becomes unstructured
- sources fight back
Then they become technical debt.
TL;DR (for the folks who skim):
Where did you hit the limit with no-code automation tools?And what did you switch to?
Would love to hear war stories.
[P.S., notice the battery %? ;)]
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Every time I share the system we're building, at least one comment says: "Why not just use n8n? This is easy". So here's why... it's not :)
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