TanStack RSCs are supported through three primitive APIs that allow manipulation of RSCs as any other stream of data, compatible with tools like React Query and TanStack Router.
Events are essential inputs to modern front-end systems. But when we mistake reactions for architecture, complexity quietly multiplies. Over time, many front-end architectures have come to resemble chains of reactions rather than models of structure. The result is systems that are expressive, but increasingly difficult to reason about.
Modern web applications are no longer just "sites." They are long-lived, highly interactive systems that span multiple runtimes, global content delivery networks, edge caches, background workers, and increasingly complex data pipelines. They are expected to load instantly, remain responsive under poor network conditions, and degrade gracefully when something goes wrong.
The request for its API val request = Request[IO](Method.POST, uri"/jobs")val api = new AsyncJobApi // this will not compile since AsyncJobApi is not defined yet Minimal implementation to make it green: class AsyncJobApi Red test: The API should return a 202 Accepted response: "POST /jobs returns Accepted" in { val request = Request[IO](Method.POST, uri"/jobs") val api = new AsyncJobApi api.routes.orNotFound.run(request).asserting : response => response.status shouldBe Status.Accepted} Make it green: class AsyncJobApi { val routes: HttpRoutes[IO] = HttpRoutes.of[IO] : case req @ POST -> Root / "jobs" => Accepted()} 5.2 Add headers (Trivial Implementation) Red test: add X-Total-Count and Location headers with job ID (only the assertion is shown)
Frontends are no longer written only for humans. AI tools now actively work inside our codebases. They generate components, suggest refactors, and extend functionality through agents embedded in IDEs like Cursor and Antigravity. These tools aren't just assistants. They participate in development, and they amplify whatever your architecture already gets right or wrong. When boundaries are unclear, AI introduces inconsistencies that compound over time, turning small flaws into brittle systems with real maintenance costs.
This same sense of uncertainty can be triggered in software products. Many digital experiences consist of background tasks, file imports, system updates, and other long-running processes that run quietly and invisibly, leaving users with no indications of progress or feedback. The user initiates an action, like a sync, a publish, or a bulk update, and is responsible for the outcome, while the system does all the work out of sight.
React tutorial: Get started with the React libraryDespite the endless churn of new frameworks, React remains the quintessential reactive engine. This updated guide walks you through the fundamentals of React development, including a This is Spinal Tap variant on the canonical counter application. Sometimes, your components just need to go to 11.
Over the past decade, software development has undergone a massive transformation due to continuous innovations in tools, processors and novel architectures. In the past, most applications were monoliths and then shifted to microservices, and now we find ourselves embracing composability - a paradigm that prioritizes modular, reusable, and flexible software design. Instead of writing separate, tightly coupled applications, developers now compose software using reusable business capabilities that can be plugged into multiple projects. This enables greater scalability, maintainability, and collaboration across teams and organizations. At the heart of this movement is Bit Harmony, a framework designed to make composability a first-class citizen in modern web development.
Vercel, the cloud platform behind Next.js, has released react-best-practices, an open-source repository containing over 40 performance optimization rules for React and Next.js applications. The framework, which encapsulates over a decade of engineering knowledge from Vercel's production codebases, is structured specifically for consumption by AI coding agents and LLMs, though the team notes it is equally valuable for human developers.
The web is full of AI assistants that appear to understand application UIs, user data, and intent. In practice, however, most of these systems operate outside the application itself. When you try to build one from scratch, you quickly run into a core limitation: large language models have no native understanding of your React state, component hierarchy, or business logic.
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