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View All on GitHubAI Summary: Optimize the JavaScript code of an AEM project to improve the INP (Interaction to Next Paint) Core Web Vitals metric on mobile devices by at least 100ms. This involves identifying and removing unused code, implementing code splitting, and potentially using `async` or `defer` attributes to improve loading performance. The solution must adhere to AEM Edge Delivery Services patterns and conventions, maintaining compatibility with the Franklin/Helix pipeline.
AI Summary: Optimize the JavaScript code of an AEM project to improve its INP (Interaction to Next Paint) score by at least 100ms on desktop. This involves identifying and removing unused JavaScript code, implementing tree shaking, code splitting, and potentially adding `async` or `defer` attributes to scripts. The optimization should adhere to AEM Edge Delivery Services patterns and conventions, maintaining compatibility with the Franklin/Helix pipeline and considering both author and publish environments.
AI Summary: Optimize the Core Web Vitals (CWV) Large Contentful Paint (LCP) metric for a mobile device on an AEM Edge Delivery Services project. This involves identifying and deferring non-critical third-party JavaScript scripts, specifically moving `auth0-spa-js.production.js` to a delayed loading phase. The optimization should aim for a 300-500ms reduction in LCP, adhering to AEM EDS patterns and conventions while maintaining compatibility with the Franklin/Helix pipeline.
AI Summary: Optimize the JavaScript execution time on a mobile device for an AEM Edge Delivery project to improve the INP (Interaction to Next Paint) metric by 150-200ms. This involves analyzing existing JavaScript code in specified files, refactoring non-critical logic using `window.requestIdleCallback`, and potentially implementing code splitting, lazy loading, or removing unused code. The solution must adhere to AEM EDS patterns and maintain compatibility with the Franklin/Helix pipeline.
AI Summary: Optimize images on an AEM website to improve the Largest Contentful Paint (LCP) metric. This involves analyzing image usage across HTML, CSS, and JavaScript, implementing responsive images with `srcset`, adding `loading="eager"` and `fetchpriority="high"` attributes where appropriate, converting to modern formats like WebP or AVIF if possible, and ensuring proper width/height attributes. The changes must adhere to AEM Edge Delivery Services patterns and maintain compatibility with the Franklin/Helix pipeline.
AI Summary: Optimize the LCP (Largest Contentful Paint) performance of an AEM Edge Delivery Services website by preloading critical CSS resources, specifically `styles.css`, potentially involving identifying and extracting critical CSS, implementing lazy loading for non-critical styles, and removing unused CSS. The solution should adhere to AEM EDS patterns, Franklin/Helix pipeline compatibility, and maintain functionality in both author and publish environments.
AI Summary: Optimize the JavaScript loading on an AEM Edge Delivery website to improve the Interaction to Next Paint (INP) metric by at least 200ms on desktop. This involves analyzing existing JavaScript files, identifying render-blocking scripts, implementing code splitting, lazy loading, and deferring non-critical scripts while adhering to AEM EDS patterns and conventions. The goal is to create a pull request with before/after performance metrics.