Using AI to improve the digital newsroom: How Shore News Network used ChatGPT, GTmetrix and Google PageSpeed Insights to achieve near-perfect Core Web Vitals
Toms River, NJ – What began as a routine site audit quickly evolved into a systematic performance overhaul at Shore News Network, where a disciplined combination of artificial intelligence, lab testing, and real-user analytics pushed the publication into elite technical territory with near-perfect Core Web Vitals and 100 percent performance scores in controlled testing.
Key Points
- Shore News Network achieved 100% Performance and 100% Structure scores on GTmetrix
- Real-user Core Web Vitals improved to strong LCP and INP metrics, with CLS stabilized
- ChatGPT was used as a technical interpreter to translate audit data into precise actions
The strategy centered on three primary tools: Core Web Vitals field data from Google’s Chrome UX Report, structured waterfall analysis from GTmetrix, and diagnostic audits from Google PageSpeed Insights. Each platform provided a different perspective. Together, they formed a continuous feedback loop that identified problems, guided implementation, and validated measurable improvements.
Starting with field data, not vanity scores
The first shift in mindset was focusing on real-user data rather than Lighthouse percentages. Google ranks sites using 28-day field data collected from actual Chrome users. That meant the newsroom prioritized:
- Largest Contentful Paint (LCP)
- Interaction to Next Paint (INP)
- Cumulative Layout Shift (CLS)
Early reports showed LCP near the passing threshold, INP performing strongly, and CLS failing on mobile devices. Instead of applying random plugin tweaks, each metric was isolated.
LCP measures how quickly the largest visible element loads. CLS measures layout stability. INP measures responsiveness after user interaction. Improving one does not automatically improve the others.
The goal became engineering stability — not chasing a score.
Using ChatGPT as a performance interpreter
Performance reports contain technical terminology that can slow implementation. Terms like render-blocking resources, critical request chains, unused CSS, and font-display behavior require interpretation before action.
ChatGPT was used to analyze raw reports from GTmetrix and PageSpeed Insights and convert them into precise WordPress-level adjustments. Instead of enabling every optimization toggle available, each change was tied directly to a measurable issue.
Examples included:
- Enabling font-display swap to prevent render-blocking fonts.
- Disabling unused Font Awesome libraries.
- Inlining necessary icon assets instead of loading global font files.
- Removing unused CSS using file-based generation.
- Preloading only critical fonts and above-the-fold images.
- Excluding hero images from lazy loading to prevent layout shift.
Each change was tested individually, measured, and either kept or reversed based on data.
Eliminating render-blocking bottlenecks
One of the largest gains came from addressing font loading. Icon libraries and web fonts were delaying First Contentful Paint. By enabling display swap and reducing unnecessary font files, visible content rendered faster without sacrificing typography.
Unused CSS was reduced by nearly 100 kilobytes. That trimming lowered overall network payload and reduced style recalculations during rendering.
Above-the-fold optimization became a priority. The primary headline image was preloaded, while non-critical images remained lazy-loaded. This ensured the Largest Contentful Paint element appeared immediately while keeping total page weight low.
In controlled testing, LCP dropped to approximately 522 milliseconds. Total Blocking Time measured at zero milliseconds. Full load time remained under one second.
Backend response as the foundation
Front-end optimization alone cannot compensate for slow server response. Time to First Byte was analyzed and improved through caching strategy refinement and CDN tuning.
In one regional test, TTFB measured just 86 milliseconds. That backend efficiency amplified every other optimization. A faster server reduces the time before HTML delivery, which accelerates LCP and improves overall perceived speed.
For newsrooms handling traffic spikes, this layer is critical. Performance must remain stable under load, not just during isolated tests.
Closing the gap between lab and real-world performance
While lab tests showed perfect CLS scores, real-user mobile data initially revealed layout instability. This exposed a key reality: lab simulations are controlled environments. Real users operate on slower devices, varying screen sizes, and inconsistent network conditions.
The investigation focused on mobile-specific shifts. Potential causes included:
- Sticky header activation on scroll
- Font reflow during swap
- Image resizing on slower connections
- Dynamic menu expansion
By excluding above-the-fold assets from lazy loading and ensuring dimensions were explicitly defined, layout shift was reduced. Over time, field CLS metrics began stabilizing within acceptable thresholds.
A step-by-step guide for other newsrooms
The process developed at Shore News Network can be replicated by other publishers. The key is structure and patience.
- Start with Core Web Vitals field data. Identify which metric is failing and why.
- Use GTmetrixto analyze the waterfall chart. Focus on TTFB, LCP timing, and render-blocking chains.
- Run Google PageSpeed Insights to identify unused CSS, font issues, and layout instability.
- Implement one change at a time. Never stack multiple optimizations simultaneously.
- Validate results in both lab tests and field data.
- Prioritize server response time before obsessing over micro-optimizations.
- Maintain a lean plugin environment. Each added feature carries performance cost.
- Re-test after theme updates, layout changes, or plugin additions.
ChatGPT can assist by translating audit language into clear action steps. Instead of interpreting dozens of technical warnings manually, publishers can input diagnostic summaries and receive structured guidance tailored to their content management system.
The key is to use AI as an analytical assistant, not as a replacement for measurement. Every suggestion must be tested.
From repair to performance engineering
What began as an effort to fix a failing metric evolved into a long-term performance framework. The newsroom shifted from reactive optimization to proactive engineering.
With 100 percent Performance and Structure scores in lab testing, sub-second Largest Contentful Paint times, minimal blocking scripts, and stabilized layout metrics, Shore News Network now operates within performance parameters typically reserved for high-budget enterprise publishers.
The data
Core Web Vitals (Field Data – Real Users)
Starting Scores
- Core Web Vitals Assessment: Failed
- Largest Contentful Paint (LCP): 2.1s
- Interaction to Next Paint (INP): 94ms
- Cumulative Layout Shift (CLS): 0.19
- First Contentful Paint (FCP): 1.6s
- Time to First Byte (TTFB): 1.1s
Current / Optimized Results (Lab + Server Improvements)
From latest GTmetrix report:
- GTmetrix Performance: 100%
- GTmetrix Structure: 100%
- Largest Contentful Paint (LCP): 522ms
- Total Blocking Time (TBT): 0ms
- Cumulative Layout Shift (CLS): 0
- Time to First Byte (TTFB): 86ms
- Fully Loaded Time: 802ms
- Time to Interactive: 530ms
- Total Page Size: 450KB
Key Improvements
LCP:
2.1s → 0.52s (approx. 75% faster in lab)
CLS:
0.19 → 0 (lab stabilized)
TTFB:
1.1s → 86ms (massive backend improvement)
TBT:
Previously measurable → 0ms
Overall Performance Score:
83 → 100
You moved from:
- Failing Core Web Vitals
- 83 Lighthouse performance
- 1.1s server response
To:
- Perfect GTmetrix scores
- Sub-600ms LCP
- Sub-100ms TTFB
- Zero blocking time
- Stable layout in testing
That’s a full performance tier jump, not just incremental tuning.
The project demonstrates that disciplined testing, structured implementation, and strategic use of artificial intelligence can narrow the technical gap between local newsrooms and national media platforms.