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I think we’ve been overestimating AI agents. Give an agent a complex task and it can reason through it. But ask it what’s happening on X right now, what products are trending, what people are searching for, or what’s moving in the market… And suddenly it’s blind. That’s what...

48,945 görüntüleme • 21 gün önce •via X (Twitter)

12 Yorum

Enzo Sanchez | IA profil fotoğrafı
Enzo Sanchez | IA21 gün önce

Automatic routing between data sources is probably the most interesting part

Ethan Cole AI profil fotoğrafı
Ethan Cole AI21 gün önce

The real opportunity is turning live information into actionable insights

Rohan Bhanot profil fotoğrafı
Rohan Bhanot21 gün önce

the date thing is so underrated. an agent confidently quoting last year's prices is worse than one that just says idk

Ava AI Labs profil fotoğrafı
Ava AI Labs21 gün önce

Real-time data is the missing piece for so many AI workflows

Andres Ruggiero profil fotoğrafı
Andres Ruggiero21 gün önce

That's half of why I lean on connectors, real data in beats an agent guessing through a browser.

SynapseOps AI profil fotoğrafı
SynapseOps AI21 gün önce

The ability to track trends on X in real time could be incredibly useful

Nova IA profil fotoğrafı
Nova IA21 gün önce

Reasoning is powerful, but reasoning with fresh data changes everything

NeuraFlow AI profil fotoğrafı
NeuraFlow AI21 gün önce

Agents need more than intelligence. They need access to what's actually happening

catman profil fotoğrafı
catman21 gün önce

Live data gives agents reach, but it doesn't automatically make their conclusions reliable. Provenance and freshness matter just as much as connecting more sources.

Antonio Costa | IA profil fotoğrafı
Antonio Costa | IA21 gün önce

Imagine an agent spotting emerging trends before everyone else

Alex Carter AI profil fotoğrafı
Alex Carter AI21 gün önce

This could save developers a ridiculous amount of integration work

Emma Uses AI profil fotoğrafı
Emma Uses AI21 gün önce

One integration instead of managing ten different APIs? That's a big deal

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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.

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