Trawl

Details

External ID

47296933

Source

HN

Company

—

Product

Trawlx

Website domain

github.com

Launched

March 8, 2026

Cohort

—

Upvotes

8

Upvotes percentile

0.491389913899139

Tags

—

Fetched at

Sept. 7, 2026, 9:26 p.m.

Updated at

Sept. 7, 2026, 9:26 p.m.

Description

Every scraper I've written has the same failure mode: it works for three months, a site redesigns, and my CSS selectors silently return empty strings. The data is still right there on the page — a human can find it instantly — but the scraper is blind.Trawl fixes this by splitting the problem. You describe what you want: trawl "https://books.toscrape.com" --fields "title, price, rating, in_stock" The LLM (Claude) looks at one sample item and derives a full extraction strategy — CSS selectors, attribute mappings, type coercion, fallback selectors. That strategy gets cached. Every subsequent page with the same structure is extracted with pure Go + goquery. No API calls, no token cost, full concurrency.The key insight: LLMs are good at understanding HTML structure, but you don't need them to extract 10,000 rows. Use AI for intelligence, Go for throughput.When a site redesigns, the structural fingerprint changes, the cache misses, and trawl re-derives automatically.You can preview exactly what it figured out: $ trawl "https://example.com/products" --fields "name, price" --plan Strategy for https://example.com/products Item selector: div.product-card Fields: name: h2.product-title -> text (string) price: span.price -> text -> parse_price (float) Confidence: 0.95 Some things that took real engineering effort:- JS-rendered SPAs: headless browser with DOM stability detection — polls until element count stabilizes and skeleton loaders resolve, scrolls to trigger lazy loading, auto-clicks "Show more" buttons - Multi-section pages: detects candidate data regions heuristically, target a specific section with --query "Market Share", scopes extraction via container selectors - Self-healing: monitors extraction health (% of fields populated), re-derives the strategy if it drops below 70% - Iframes: auto-detects and extracts from iframes when they contain richer data than the outer pageOutput is JSON, JSONL, CSV, or Parquet. Pipes cleanly: trawl "https://example.com/products" --fields "name, price" --format jsonl | jq 'select(.price > 50)' Written in Go. MIT licensed.

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