Somethings that were suggested by folks online:

```
Biggest wins will come from strict data hygiene and a reproducible pipeline: fetch, normalize, enrich, report, backed by rate limits and solid logging.

Actionable stuff I’d add: respect robots.txt and add a domain-scoped rate limiter with exponential backoff; rotate user agents and proxies. Prefer asyncio with httpx or aiohttp for concurrency; use selectolax or lxml for parsing and only fall back to Playwright when needed. Store both raw snapshots and normalized records; use Pydantic for validation, Alembic for migrations, and content hashing for dedupe (simhash/minhash). For enrichment, try spaCy NER plus rapidfuzz for entity resolution; tag every fact with source and confidence. Schedule with APScheduler or Celery, and keep config in pydantic-settings with secrets via env.

Report gen: Jinja2 templates to HTML, then WeasyPrint to PDF; show citations inline and a timeline view per entity. Testing: VCRpy for request fixtures, Hypothesis for edge cases, docker-compose for an ephemeral Postgres.

I’ve used Zyte for scraping at scale and Supabase for auth/storage; DreamFactory helped auto-generate secure REST APIs over Postgres and MongoDB with RBAC when I needed quick integrations.

Nail data hygiene and a reproducible pipeline so the automation stays useful and trustworthy.
```