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Stress-tested Perplexity Perplexity Finance Computer on a real equity research workflow: Map a representative AI infrastructure supply chain across 70+ companies across multiple tiers with sourced financials, bottleneck analysis, and company classifications. The kind of deliverable that might take a junior analyst days to weeks. It produced ~2,000 lines...

42,176 Aufrufe • vor 2 Monaten •via X (Twitter)

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how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

134,438 Aufrufe • vor 2 Monaten

Marc Andreessen: Most successful companies started “product first” “There are products that become companies, and then there are companies that come up with a product. One of the interesting things over the years is that many of the most successful technology franchises were products first, way before they ever became companies.” In this talk, Marc gives a few examples: • His team at the University of Illinois worked on the research project that became Netscape for three years before it became a company • Bill Gates and Paul Allen were deep into PCs before there was a software business • Jobs and Wozniak built the first Apple computer as hobbyists • Mark Zuckerberg was running Facebook out of his dorm room before he ever thought of starting a company • Twitter was a side project at the failed podcasting app Odeo Marc believes that this “product becomes a company” template is successful because “it’s a demonstration that the product has to exist. The market needs the product so badly that somebody actually built it and deployed it and you can actually see evidence that people want it before there was an economic motivation to do so.” He contrasts this with the failure cases he often sees when entrepreneurs try to figure out the idea after starting a company. “It’s very easy in that process to fool yourself into believing that there’s a market because you want to find something and you have a very strong motivation to come up with an answer. It’s hard to go through that process for three months and then say, ‘you know what, we can’t come up with any good ideas.’” There are of course there exceptions. Marc gives Hewlett Packard as an example. But that’s more the exception than the rule. As Marc explains: “The moral of the story is it has to be a really good idea. That often will be an idea that is preexisting at the time you decide to start a company. And if it isn’t, be really careful because you’re walking on sharp rocks at that point with a high risk of falling off the cliff into the ocean.” Video source: Stanford eCorner (2010)

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35,823 Aufrufe • vor 9 Monaten

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