<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai on cloud_affects</title><link>https://cloudaffects.com/tags/ai/</link><description>Recent content in Ai on cloud_affects</description><generator>Hugo</generator><language>en</language><copyright>CC BY-NC-SA 4.0</copyright><lastBuildDate>Tue, 07 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://cloudaffects.com/tags/ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Firecrawl — Web scraping API for AI</title><link>https://cloudaffects.com/gems/firecrawl/</link><pubDate>Tue, 07 Apr 2026 00:00:00 +0000</pubDate><guid>https://cloudaffects.com/gems/firecrawl/</guid><description>&lt;figure&gt;
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&lt;p&gt;&lt;a href="https://www.firecrawl.dev"&gt;Firecrawl&lt;/a&gt; turns websites into clean data for AI. You give it a URL, it gives you markdown.&lt;/p&gt;
&lt;p&gt;The annoying part of scraping isn&amp;rsquo;t fetching HTML — it&amp;rsquo;s the JavaScript rendering, the anti-bot blocks, the rotating proxies, and then still getting back a wall of navigation and ads with the thing you wanted buried inside. Firecrawl takes care of that pipeline. &lt;code&gt;/scrape&lt;/code&gt; returns a page as markdown or JSON. &lt;code&gt;/crawl&lt;/code&gt; walks a whole site without needing a sitemap. &lt;code&gt;/search&lt;/code&gt; queries the web and returns full page content. There&amp;rsquo;s also an agent mode that can click, type, and scroll before extracting, which is useful for anything behind a login or a UI interaction.&lt;/p&gt;</description></item><item><title>Tavily — Search API Built for AI Agents</title><link>https://cloudaffects.com/gems/tavily/</link><pubDate>Tue, 07 Apr 2026 00:00:00 +0000</pubDate><guid>https://cloudaffects.com/gems/tavily/</guid><description>&lt;figure&gt;
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&lt;p&gt;&lt;a href="https://www.tavily.com"&gt;Tavily&lt;/a&gt; is a search API for AI agents. Not a thin wrapper around Google — it&amp;rsquo;s built from the ground up for LLM workflows.&lt;/p&gt;
&lt;p&gt;The problem it solves is simple: language models have a knowledge cutoff, and raw web results are a mess of HTML, ads, and noise. Tavily fetches and filters that before returning it, so the model gets clean excerpts it can actually use. &lt;code&gt;/search&lt;/code&gt; is the main endpoint. &lt;code&gt;/extract&lt;/code&gt; pulls structured content from a specific URL. &lt;code&gt;/crawl&lt;/code&gt; goes deeper. There&amp;rsquo;s also &lt;code&gt;/research&lt;/code&gt; for multi-step tasks, which recently topped some benchmarks.&lt;/p&gt;</description></item></channel></rss>