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988: Cloudflares Next.js Slop Fork thumbnail

988: Cloudflares Next.js Slop Fork

Published 18 Mar 2026

Duration: 2832

Cloudflare's vNext, a Vite-based Next.js fork, leveraged AI for rapid prototyping and iterative feedback to address porting challenges, while exploring AI's transformative potential in development workflows and framework transitions.

Episode Description

Wes and Scott talk with Steve Faulkner about vinext, a Vite-powered Next.js fork. They dive into AI coding workflows, agent browsers, code quality, an...

Overview

The podcast discusses the development of vNext, a fork of Next.js onto Vite created by Cloudflare, described as a "slop fork" to emphasize the process of porting the framework rather than the controversy surrounding it. Steve Faulkner, a Cloudflare engineering director, explains the projects motivations, including the challenges of hosting Next.js on non-Node/Vercel environments and the limitations of prior attempts to build custom compilers. The fork leveraged AI for rapid prototyping, enabling quick experimentation with Next.js on Vite through code generation, task planning, and collaboration with tools like OpenCode Opus. Key challenges included adapting Next.jss large test suite to Vites ecosystem while filtering out outdated tests and using AI to guide the alignment of tests with Vites framework. The projects success was marked by a pivotal demo of the app router, despite initial imperfections.

The discussion also highlights AIs growing role in software development, framing it as an "application factor" that accelerates progress when goals are clear but risks amplifying errors. Steve uses AI for managerial tasks like summarizing meetings and tracking workflows, emphasizing iterative refinement over reliance on prompts alone. Technical workflows involved tools like Markdown for planning, OpenCode Opus for coding, and voice-to-text tools for brainstorming. The team prioritized flexibility, acknowledging that while AI-generated code often prioritizes compatibility over quality, iterative refactoring and guardrails like linting and security audits help maintain standards. Challenges included non-determinism in AI outputs, errors in generated code, and the need for manual intervention to resolve issues like infinite loops or context compaction failures. Future directions emphasized AIs potential to reshape development practices, from framework transitions to security testing, while balancing excitement with caution about ethical and practical risks.

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