I used to think AI search readiness was just SEO with a new name. It's not. The more time I spend on this, the clearer the distinction becomes. The core difference Traditional SEO optimizes for ranking in a list of links. You want to be the #1 blue link on Google for "best project management software." The user clicks through to your page, you get the traffic, you monetize. AI search optimizes for being the source of an answer. When someone asks Perplexity or ChatGPT "what's the best project management software?", the AI reads multiple sources, synthesizes an answer, and cites the ones it used. The user may never click through. The fundamental units are different: SEO operates on pages and rankings AI search operates on facts, claims, and citations You can be #1 on Google for a keyword and never appear in a single AI-generated answer. And you can be cited in AI answers without ranking in the top 10 for anything. What still matters Some things carry over from SEO: Technical quality — Fast pages, HTTPS, crawlable content. AI crawlers care about this just like Googlebot. Clear content structure — Headings, lists, tables. Well-structured content is easier for AI models to parse. Internal linking — AI crawlers follow links like any other crawler. Good information architecture matters. Backlinks from authoritative sources — Being cited by Wikipedia, academic papers, and major publications signals trust to AI models just like it does to search engines. What matters for AI search that barely matters for SEO A few things that are critical for AI search but don't move the needle much for traditional rankings: LLMs.txt / LLMs-full.txt — These files don't affect your Google ranking at all. But they give AI models a clean, structured map of your site. I've seen sites with great LLMs.txt files get cited more consistently than sites with better backlink profiles but no AI-readable summary. Structured data for disambiguation — In SEO, schema markup helps with rich snippets. In AI s

AI crawlers now appear in many server logs alongside traditional search bots. Some are used for search retrieval, some for training, and some for broader web indexing. If you care about AI search visibility, you need to know which ones can access your public pages. The most common accidental blocker is simple: a robots.txt rule or CDN bot setting that prevents AI crawlers from reaching the content you want discovered. The major AI crawler tokens to check Here are crawler tokens you may see in logs or robots.txt rules: Crawler token Company Notes GPTBot OpenAI Documented OpenAI crawler token OAI-SearchBot OpenAI Documented OpenAI search-related crawler token ChatGPT-User OpenAI Documented OpenAI user-triggered agent token ClaudeBot Anthropic Documented Anthropic crawler token Claude-SearchBot Anthropic Documented Anthropic search-related crawler token Google-Extended Google Google control token for Gemini Apps and Vertex AI use CCBot Common Crawl Web corpus crawler used by many downstream systems PerplexityBot Perplexity Commonly referenced Perplexity crawler token Crawler names and purposes change. Always confirm against official platform documentation before making sitewide access decisions. First, check what is actually happening Before you change anything, find out who is already crawling. If you have server logs: grep -E "GPTBot|OAI-SearchBot|ChatGPT-User|ClaudeBot|Claude-SearchBot|Google-Extended|CCBot|PerplexityBot" access.log If you use Cloudflare, check bot and security events and filter by user agent. Three quick diagnostic steps: Open https://yourdomain.com/robots.txt and look for broad Disallow: / rules. Confirm the sitemap is listed in robots.txt or discoverable at /sitemap.xml . Use our AEO Checker to validate robots.txt and flag restrictive AI crawler rules. The most common mistake The blunt rule that makes sites invisible to many crawlers: User-agent: * Disallow: / This blocks every well-behaved crawler that follows the wildcard rule. If you see it on

Today, we’re excited to launch UIAble v1.0, an open-source component library built for developers, by developers. We explored a lot of UI libraries built on Shadcn. Most of them feel nearly identical — same structure, same aesthetic, same tradeoffs. That’s what pushed us to build something different. Not another library that looks like Shadcn with a coat of paint, but a design system with its own identity and a clearer sense of what it’s actually for. Why UIAble exists After enough frontend projects, one thing becomes obvious: the same UI patterns get rebuilt again and again. Inputs. Dialogs. Tables. Alerts. Dropdowns. OTP fields. Form validation states. Not because they’re hard to build, but because most existing libraries never quite fit real project requirements. Some are too opinionated. Some pile on unnecessary abstraction. Some become rigid after initial setup. And some make simple UI unnecessarily complicated. That friction is what led to UIAble. Not to launch another oversized library, just to build a cleaner, more practical foundation for modern frontend development. What UIAble actually is UIAble is a free, open-source UI component library built with Tailwind CSS , Shadcn-style architecture , and Base UI principles . The idea is straightforward: reusable components should stay flexible, readable, and easy to maintain. Instead of pulling projects into a rigid ecosystem, UIAble gives you components you can copy directly into your codebase, edit freely, and scale without fighting the library. No lock-in. No unnecessary abstraction. No dependency trap. What makes it different in practice A few things actually matter here. You get the code. UIAble doesn’t hide logic behind layers of packaging. You see the component. You edit it. You ship it. That alone changes how teams work with UI. It’s built for real product UI, not showcase pages. A lot of UI kits look great in demos and fall apart in production. UIAble focuses on the unglamorous stuff, forms that don’t bre

OpenAI is investigating issues with Codex usage limits Tibo wrote that the Codex team spent Sunday in a war room, digging through logs and looking for anything that could have caused faster usage drain for some users. As the investigation continues, OpenAI has issued a full reset of Codex usage limits for everyone. The funny part: this week at OpenAI is called RESET week. In US corporate culture, that usually means a lighter week to slow down, clear the calendar a bit, and recharge.

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