Prototyping with AI should feel fast, collaborative, and on brand. Most AI tools have cracked the "fast" and "collaborative" parts, but can struggle with feeling "on-brand". This disconnect usually stems from a lack of context. For v0 to produce output that looks and feels right, it needs to understand your components. That includes how things should look, how they should behave, how they work together, and all of the other nuances. Most design systems aren’t built to support that kind of reasoning. However, a design system built for AI enables you to generate brand-aware prototypes that look and feel production ready. Let's look at why giving v0 this context creates on-brand prototypes and how you can get started.

Building an AI app can now take just minutes. With developer tools like the AI SDK, teams can build both AI frontends and backends that accept prompts and context, reason with an LLM, call actions, and stream back results. But going to production requires reliability and stability at scale. Teams that connect directly to a single LLM provider for inference create a fragile dependency: if that provider goes down or hits rate limits, so does the app. As AI workloads become mission-critical, the focus shifts from integration to reliability and consistent model access. Fortunately, there's a better way to run. AI Gateway, now generally available, ensures availability when a provider fails, avoiding low rate limits and providing consistent reliability for AI workloads. It's the same system that has powered v0.app for millions of users, now battle-tested, stable, and ready for production for our customers.

Code and Theory is a digital-first creative and technology agency that blends strategy, design, and engineering. With a team structure split evenly between creatives and engineers, the agency builds systems for global brands like Microsoft, Amazon, and NBC that span media, ecommerce, and enterprise tooling. With their focus on delivering expressive, scalable digital experiences, the team uses v0 to shorten the path from idea to working software.

How do you tell an AI agent what it needs to do when it hits a protected page? Most systems rely on external documentation or pre-configured knowledge, but there's a simpler approach. What if the instructions were right there in the HTML response? llms.txt is an emerging standard for making content such as docs available for direct consumption by AIs. We’re proposing a convention to include such content directly in HTML responses as <script type="text/llms.txt">.

Autonomous coding agents are not the future. They are already here. Agents can now design, build, test, and deploy an entire full-stack feature from front end to back end without a human touching the keyboard. The reality is that while this technology has advanced quickly, Generally Accepted Accounting Principles (GAAP) have not traditionally focused on the cost of tools used in development. Under current U.S. GAAP, you can capitalize certain third-party software costs if they are a direct cost of creating software during the application development stage. Historically, though, developer tools were treated as overhead because their cost could not be directly tied to capitalizable work. Under GAAP, work that meets the criteria should be capitalized. When agents perform that work, they should be treated no differently than salaried engineers.

There is a long-standing myth that serverless compute inherently requires more connections to traditional databases. The real issue is not the number of connections needed during normal operation, but that some serverless platforms can leak connections when functions are suspended. In this post, we show why this belief is incorrect, explain the actual cause of the problem, and provide a straightforward, simple-to-use solution.

AI bot traffic is growing across the web. We track this in real-time, and the data reveals three types of AI-driven crawlers that often work independently but together create a discovery flywheel that many teams disrupt without realizing it. Not all bots are harmful. Crawlers have powered search engines for decades, and we've spent just as long optimizing for them. Now, large language models (LLMs) need training data, and the AI tools built on them need timely, relevant updates. This is the next wave of discoverability and getting it right from the start can determine whether AI becomes a growth channel or a missed opportunity. Blocking AI crawlers today is like blocking search engines in the early days and then wondering why organic traffic vanishes. As users shift from Googling for web pages to prompting for direct answers and cited sources, the advantage will go to sites that understand each type of bot and choose where access creates value.

Coxwave helps enterprises build GenAI products that work at scale. With their consulting arm, AX, and their analytics platform, Align, they support some of the world’s most technically sophisticated companies, including Anthropic, Meta, Microsoft, and PwC. Since the company’s founding in 2021, speed has been a defining trait. But speed doesn’t just mean fast models. For Coxwave, it means fast iteration, fast validation, and fast value delivery. To meet that bar, Coxwave reimagined their web app strategy with Next.js and Vercel.

Ready.net is a core platform that helps utility companies manage their financing and compliance, the company works with a wide network of state-level stakeholders. New feature requirements come in fast, often vague, and always critical. With limited design resources supporting three teams, the company needed a way to speed up the loop between ideation, validation, and delivery. That’s where v0 came in.

With a single prompt, anyone can go from idea to deployed app with UI, content, backend, and logic included. v0 is now agentic, helping you research, reason, debug, and plan. It can collaborate with you or take on the work end-to-end. From product managers writing specs to recruiters launching job boards, v0 is changing how teams operate.

Zapier is the leading AI orchestration platform, helping businesses turn intelligent insights into automated actions across nearly 8,000 apps. As AI tools and agents become more capable, Zapier provides the connective tissue to operationalize them, bridging the gap between decision and execution.  Powered by Zapier extends this capability to partners. It enables SaaS and AI companies to embed Zapier’s automation engine directly into their products without needing to build or maintain thousands of integrations in-house. But explaining to partners what that experience can look like in their product was a challenge. Needing to move quickly with finite resources, the Zapier team could take a few weeks to design and build a clickable prototype. Now, with v0, the Powered by Zapier team can generate high-fidelity demos in just a few hours. The result: better conversations with partners, faster implementation cycles, and more integrations shipped for end users.

The GPT-5 family of models, released today, is now available through AI Gateway and in production on v0.dev. Thanks to OpenAI, Vercel has been testing these models over the past few weeks in v0, Next.js, AI SDK, and Vercel Sandbox. From our testing, GPT-5 is noticeably better at frontend design than previous models. It generates polished, balanced UIs with clean, composable code. Internally, we’ve already started using GPT-5 for Vercel's in-dashboard Agent and for v0.dev/gpt-5. GPT-5 shows strong performance in agent-based workflows. We have been impressed with it's long-context reasoning and ability to handle multiple tools in parallel have been especially effective in powering Vercel Agent.