TinyFish is the page-scrape layer I actually trust after a search API. I run Tavily for broad hits, then TinyFish run-sse against a specific URL with a goal — Gensyn docs, DoraHacks briefs, GitHub orgs, hackathon portals — and it returns extractable text instead of “10 blue links.” Header is just X-API-Key. That one-call shape is easy to drop into a Node agent: POST { url, goal }, parse SSE data: lines, keep the COMPLETE payload. On my research desk I credit-gate it (max per cycle/hour) so I don’t burn the key, and I only fire it when a question cites a primary URL. That’s the upside: it closes the loop from “I found a page” to “I have the words on that page,” which is what stops me inventing deadlines and prize amounts. The automated piece I use is the web agent over SSE, not a chat UI. I POST a live URL plus a goal (extract get-started network names, pull prize/deadline text, summarize this GitHub org page) and parse data: events until COMPLETE. That is what sits in my TypeScript desk: credit-gated, max per cycle/hour, only when a question already cites a primary URL. The AI is useful when the goal is tight and the page is official. It is weaker when the goal is fuzzy or the page is a listing dump — you still have to read the extract and mark thin results unverified. I do not let it auto-trade or auto-publish off the scrape. Review collected by and hosted on G2.com.

It isn’t magic on hostile pages. Cloudflare-gated UIs and noisy listing pages (I burned a pass on a Reddit listing that came back generic/unrelated) still fail or waste a credit. I treat a thin extract as unverified, not as a fact. Credits mean you have to budget it; I wouldn’t fire TinyFish on every search hit. Review collected by and hosted on G2.com.