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Reworkd is your scraping co-pilot. It understands website structures and auto-generates Playwright code to take actions, visit subpages, scrape, and save data based on your custom schema. With Reworkd you can simplify web data extraction at scale.

63,943 Aufrufe • vor 1 Jahr •via X (Twitter)

11 Kommentare

Profilbild von anirudh
anirudhvor 1 Jahr

@ReworkdAI stagehand can help when your playwright code breaks!

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HUDIvor 2 Jahren

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Profilbild von Artypone
Artyponevor 1 Jahr

@ReworkdAI What a disappointment for the investments after MCP. You can do it by developing your own scraptool in 10 mins and keep your scrapped data in privately. There like 10 opensource projects. Would you like me to share github links?

Profilbild von Israr Ahmed
Israr Ahmedvor 1 Jahr

@ReworkdAI Scaling web data extraction is revolutionary. At @qryptum, we’re building decentralized AI and data solutions. What are your thoughts on how Reworkd could integrate with decentralized data sources?

Profilbild von vito
vitovor 1 Jahr

@ReworkdAI cool! just what i was looking for.

Profilbild von Steph from OpenVC
Steph from OpenVCvor 1 Jahr

@ReworkdAI This is awesome!

Profilbild von Raiders
Raidersvor 1 Jahr

@ReworkdAI Can we scrape twitter posts too? I doubt

Profilbild von Ashish
Ashishvor 1 Jahr

This is useless, even for simple single page websites. I tried scrapping 2 very simple web pages, and it failed miserably. Now think of all varying complexities with different sites(captchas, geolocation, sessions), it does not stand a chance. If it is only good for basic sites, it is too costly. I can get that done on chatgpt subscription.

Profilbild von Tyler Clark
Tyler Clarkvor 1 Jahr

@ReworkdAI @firecrawl_dev 4 life! 🔥

Profilbild von Vinnie 🌳
Vinnie 🌳vor 1 Jahr

@ReworkdAI 🌿 ReworkdAI could be a game-changer for eco-data projects! Let's see it simplify nature-data extraction! 🚀 #EcoData

Profilbild von Lantos (e/acc)
Lantos (e/acc)vor 1 Jahr

@ReworkdAI @talos256

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🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

Vikas gupta

18,053 Aufrufe • vor 12 Tagen