This article explains model editing—injecting or updating knowledge in AI models—distinguishes it from post‑training, outlines reliability, generalization and locality metrics, and surveys both parameter‑free (e.g., IKE) and parameter‑based methods such as ROME, hypernetworks, and MEND, highlighting practical challenges.
This article introduces Rome, a next‑generation frontend toolchain that unifies dependency management, code formatting, static type checking, building, and asset handling, and provides step‑by‑step installation, configuration, usage commands, and customization options, helping developers improve efficiency and code quality.
The article details Meituan’s Rome front‑end framework, covering its business and technical background, the engineering ecosystem and evolution path, large‑scale upgrades, IDE‑based development assistance, efficiency and quality improvements, metric collection, real‑world adoption across 1,400+ projects, and future trends such as deeper dev‑chain integration and AI‑assisted coding.
Rome, an experimental JavaScript toolchain announced by Facebook in February 2020, combines a compiler, linter, formatter, bundler, and test runner written in TypeScript, offers a unified CLI with commands such as analyzeDependencies, bundle, lint, and provides detailed usage examples and instructions for building and running the project.
