🤖 Cursor Used AI Agents to Rewrite a React App for 3 Weeks Straight
This research post from Cursor explores what happens when autonomous coding agents run continuously for weeks on real production codebases. One standout experiment was an in-place migration from Solid to React, spanning three weeks and hundreds of thousands of edits, passing CI with minimal human intervention. The team explains why naive multi-agent setups fail, and how splitting agents into planners and workers unlocked real progress. It’s a rare look past AI hype into what large scale React changes might actually look like in practice.

📻 When “Modern React” Overcomplicates a Radio Button
This post takes a close look at how a basic HTML radio button balloons into layers of abstraction when built with shadcn/ui and Radix UI. What starts as <input type="radio"> turns into hundreds of lines of React, ARIA roles, hidden inputs, Tailwind classes, and extra JS, all to recreate behavior browsers already give us for free. The article isn’t anti-library, but it’s a sharp reminder that abstraction has real costs in complexity, performance, and understanding. A great read if you’ve ever wondered how “simple” UI got so complicated in modern React.

⚠️ useOptimistic Won’t Save You in React 19
This article takes a hard look at why optimistic UI remains tricky in React, even with the new useOptimistic hook in React 19. It walks through real race conditions, transition pitfalls, and why naive implementations still break under concurrency. The key takeaway is that useOptimistic helps in specific cases, but it doesn’t magically solve ordering, syncing, or error handling on its own.

🧩 How Turbopack Powers Fast Refresh in Huge React Apps
This deep dive explains how Turbopack achieves near-instant builds by using fine-grained, function level incremental computation instead of coarse file-based caching. Rather than rebuilding everything, Turbopack tracks precise dependencies through “value cells” and only recomputes what actually changed. The post walks through why this approach is hard, how it avoids common caching pitfalls, and why it’s essential for fast React Fast Refresh at scale. A must-read if you’ve wondered why Next.js dev feels so much faster lately.

Quick Links

💸 Without Benchmarking LLMs, You’re Probably Overpaying 5–10×
This article makes a compelling case that public LLM benchmarks are a poor predictor of performance on your actual prompts. By benchmarking real production workloads across 100+ models, the author shows that defaulting to GPT-5 often means paying far more than necessary for comparable quality.

☁️ Astro Is Joining Cloudflare
Cloudflare announced that the team behind Astro is officially joining the company, with Astro remaining open source and framework-agnostic. The post explains why Astro’s content-first, server-first philosophy aligns closely with Cloudflare’s push for a faster, simpler web. It also previews Astro 6, which brings a new Vite-powered dev server and better parity between local development and production runtimes.

📍HTML Is Getting a <geolocation> Element
Chrome 144 introduces a new declarative <geolocation> HTML element that replaces script-triggered location prompts with an explicit, user-initiated control. Instead of imperatively calling the Geolocation API and hoping the browser allows it, developers render an element and listen for location events when the user clicks. The change reduces permission failures, improves recovery from blocked states, and cuts down boilerplate.

🔥 A Practical Guide to Real Performance Work
This is a dense, practical “how to tune software” guide from Jeff Dean and Sanjay Ghemawat that covers what matters most when performance actually matters. It pushes a strong mental model, make sensible fast choices early, use back of the envelope estimates to avoid obvious traps, and then measure everything with real profiling. What to do when profiles are flat, how to reduce allocations, when to change APIs, and where structural wins beat micro-optimizations. If you write anything performance sensitive (or build libraries other people depend on), this is worth bookmarking.

🌀 How a 5-Line Bash Loop Became an AI Programming Movement
This is a firsthand history of “Ralph,” the viral agentic coding loop popularized by Geoff Huntley, and why it works despite looking absurd. The piece traces how simple control loops, aggressive context carving, and declarative specs repeatedly outperform more “sophisticated” agent setups. Along the way, it surfaces hard-earned lessons about overbaking agents, refactoring at scale, and why small, bounded runs beat autonomous chaos.

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In case you missed my video over Christmas, I made this to explore how to use React Query more effectively beyond what you get from simply copying and pasting the official docs. From creating custom and reusable query options to defining global constants for query keys, all the way to prefetching data outside of components, this is the modern way of working with React Query.

React Query is a powerful tool that has fundamentally changed how we manage asynchronous state and data fetching in React. Understanding how to use it effectively is essential for building scalable React applications.