Prompt
What should I use for change detection across websites?
Latest observation
If you mean detecting changes in web pages/sites over time (pricing, content, availability, layout, etc.), the best choice depends on what kind of changes and how many sites you’re monitoring.
Good options by use case
1) Simple page monitoring
Use a tool like:
- Distill.io
- Visualping
- Wachete
- Hexowatch
Best when you want:
- little/no coding
- alerts by email/slack
- monitoring a few pages
- visual or text diffs
2) Your own scalable solution
If you need custom behavior across many sites, use:
- Python + Playwright or Puppeteer for dynamic sites
- Python + requests/BeautifulSoup for static sites
- Hashing / diffing of normalized content
- Cron / queue / scheduler for periodic checks
Best when you want:
- control over selectors
- handling logins, JS-rendered content
- custom alerts and deduping
- integration with your systems
3) Large-scale website change detection
Use a pipeline with:
- crawler/fetcher
- content extraction/normalization
- change detector
- storage/versioning
- notification system
Good technologies:
- Fetching: Playwright, Scrapy, httpx/requests
- Diffing: text diff, DOM diff, hashing, SimHash/MinHash for near-duplicate detection
- Storage: PostgreSQL, S3, Elasticsearch/OpenSearch
- Jobs: Celery, RQ, Airflow, Temporal
Which kind of change detection?
- Text/content changes: compare extracted text or structured fields
- Visual changes: screenshot comparison
- DOM/HTML changes: compare normalized DOM
- Semantic changes: compare extracted entities/fields
- Near-duplicate page detection: hashing/embedding-based methods
My practical recommendation
- For a quick start: Distill.io or Visualping
- For a custom build: Playwright + content normalization + hashing + diff
- For many pages at scale: Scrapy/Playwright + PostgreSQL + queue + alerting
If you tell me:
- how many sites/pages,
- whether they’re static or JS-heavy,
- whether you need visual or text diffs,
- and whether you want no-code or code,
…I can recommend a specific stack.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
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