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The reliable way to measure EmDash’s speed impact is a controlled before-and-after comparison: test the same URL, content, device strategy, browser conditions, and deployment assumptions, then repeat the tests after the EmDash change. Use Lighthouse for repeatable laboratory diagnostics and PageSpeed Insights (PSI) for Lighthouse lab results plus real-user Chrome User Experience Report (CrUX) data when enough traffic exists. Record metric-level results—especially LCP, CLS, INP, FCP, Speed Index, TBT, and server TTFB—instead of treating one Performance score as the answer.
PSI’s field data is a trailing 28-day view, so it cannot demonstrate an immediate post-deployment effect. A same-day change in PSI lab results is useful for debugging; a change in field Core Web Vitals requires time for visitors using the new deployment to enter the collection window.
What a valid EmDash speed comparison looks like
An EmDash comparison is an experiment, not a single score lookup. The before and after runs must differ in the EmDash version or configuration you are evaluating, while the other meaningful inputs stay fixed.
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- URL: Test the same canonical URL before and after. If one page is not representative, define a matched set of URLs and keep that set unchanged.
- Page content: Freeze copy, image files and dimensions, fonts, third-party scripts, analytics, and any widgets.
- Delivery configuration: Record the EmDash release, Astro build, Cloudflare Workers settings, database region, cache policy, and whether the page is static or server-rendered. EmDash documentation describes an Astro- and Cloudflare-oriented architecture, so these deployment details can materially change results.
- Test strategy: Run both Mobile and Desktop in Lighthouse and submit the same URL with both strategies in PSI.
- Timing: Save the deployment identifier and test date. Do not compare a warm-cache result from one version with a cold-cache result from another without labeling that difference.
If an unrelated change cannot be avoided—for example, a new image set or CDN rule—list it beside the result and describe the outcome as a combined-change experiment rather than attributing every difference to EmDash.
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What Lighthouse and PageSpeed Insights actually measure
Lighthouse: controlled lab evidence
Lighthouse runs a page under a defined browser, device strategy, and network simulation. Chrome DevTools Lighthouse is useful for debugging because you can repeat the same procedure, inspect opportunity audits, and save the report or JSON. It is still a variable measurement: page nondeterminism, network conditions, server response, and the test client can move results between runs. One run is weak evidence; use repeated runs and a predeclared summary such as the median.
PageSpeed Insights: lab plus field data
PSI provides Lighthouse lab diagnostics and, when the URL or origin has sufficient traffic, field data from the Chrome User Experience Report. The lab portion can vary between submissions just as a DevTools run can. The field portion describes actual visitors using different devices and networks, so it may disagree with a lab result even when both are correct for their respective populations.
PSI field Core Web Vitals use the 75th percentile over the previous 28-day collection period. That window means a new EmDash deployment will be mixed with visits to the prior version until enough time passes. Do not call a same-day field result proof of a long-term improvement or regression.
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Why the numbers disagree
- Lab versus field: Lighthouse and PSI lab tests use a controlled synthetic setup; CrUX represents real visitors with varied hardware, browsers, networks, and locations.
- Run-to-run variation: Lighthouse documents variability caused by page nondeterminism, network, server, and client conditions. A small change in one run may not be a real product change.
- Different collection windows: PSI field values summarize a trailing 28-day period rather than the moment you click Analyze.
- Server versus browser experience: A server benchmark can report processing or response timing that does not map directly to what a browser measures while rendering and interacting with the page.
Prepare the before-and-after test
Choose a representative page
Select an EmDash URL that matters to users and has a stable layout. A landing page, article template, dashboard route, or other high-traffic page can be appropriate, but state why it represents the deployment. For a site with several templates, create a matched set and test each URL using the same procedure.
Freeze inputs and document the deployment
Before the baseline, write down the exact page state and delivery settings. Include the EmDash release, Astro build identifier, Cloudflare Workers configuration, database region, static or server-rendered mode, cache policy, image and font versions, analytics and third-party script list, and the canonical URL. Keep these notes with the test reports so a later reader can distinguish an EmDash effect from an infrastructure change.
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Use a clean, repeatable browser setup
For DevTools Lighthouse, open the target URL in Chrome and use a clean session. Close unrelated tabs and avoid extensions or activity that can consume CPU or network resources. Select the same Mobile or Desktop strategy for every run. The goal is not to make the lab resemble every visitor; it is to make the before and after comparable.
Run the Lighthouse baseline
- Open the representative EmDash URL in Chrome.
- Open Chrome DevTools and select the Lighthouse panel.
- Choose the Mobile strategy and run the Performance audit. Save the report URL or exported JSON.
- Repeat the audit with the Desktop strategy and save that report as well.
- Repeat each strategy several times under the same conditions. Record the individual values and calculate a median, or another summary chosen before looking at the after results.
For every run, capture the Performance score, First Contentful Paint (FCP), Largest Contentful Paint (LCP), Speed Index, Cumulative Layout Shift (CLS), Total Blocking Time (TBT), and the major opportunity audits. Also record the browser strategy, test date, URL, deployment identifier, and any unusual event such as a timeout or failed third-party request.
What to do with repeated runs
Do not select the fastest run because it makes the change look better. Keep all valid runs, remove a run only when you can document a test failure, and apply the same rule before and after deployment. The median is often easier to explain than a best or worst run because it reduces the influence of an unusually busy network or a transient server delay.
Run the PageSpeed Insights baseline
- Open PageSpeed Insights and submit the same canonical URL used for Lighthouse.
- Capture the Mobile report, including the report date, Lighthouse lab score, metric values, opportunities, and the Core Web Vitals assessment.
- Capture a separate Desktop report with the same fields.
- Save the report URLs or screenshots and note whether PSI displayed CrUX field data for the URL or origin.
- Record the field-data collection period shown by PSI. It should be treated as a trailing 28-day window, not as a point-in-time test.
PSI lab results provide a second view of synthetic performance. They are useful for confirming an issue, but they are not required to match the DevTools run numerically because the execution environment and timing can differ.
Deploy the EmDash change without contaminating the experiment
Deploy the EmDash version or configuration being evaluated after the baseline is complete. Keep content, image dimensions, fonts, scripts, CDN settings, cache behavior, and database placement unchanged wherever possible. If the deployment necessarily changes one of these, write down the change and report the result as the effect of the complete deployment bundle.
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Record the exact release and build identifiers. For an Astro and Cloudflare-oriented deployment, also record Worker settings, the database region, and whether the route is static or server-rendered. A server-rendering decision can alter response timing and the browser’s rendering schedule even when the visible page is identical.
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- Run the same number of Lighthouse Mobile audits in the same clean-session procedure.
- Run the same number of Lighthouse Desktop audits.
- Calculate the same summary statistic used for the baseline.
- Submit the same URL to PSI for Mobile and Desktop and save the same fields.
- Check that content, assets, scripts, URL redirects, and cache assumptions still match the baseline.
For each metric, report both the absolute delta and the percentage delta. For a metric where lower is better, such as LCP or TBT, an improvement is a negative numeric difference; for a score where higher is better, an improvement is positive. State the direction explicitly so a reader does not mistake a larger time value for an improvement.
Metrics to record and how to read them
| Metric | What it tells you | Where to record it |
|---|---|---|
| Performance score | A composite Lighthouse context signal; useful for a quick summary but insufficient to explain the change. | Lighthouse lab and PSI lab, separately for Mobile and Desktop. |
| FCP | When the first visible page content is painted. | Lighthouse and PSI lab runs. |
| LCP | When the largest visible content element is rendered; one of the Core Web Vitals. | Lighthouse lab and PSI field data when available. |
| CLS | How much unexpected layout movement occurs; one of the Core Web Vitals. | Lighthouse lab and PSI field data when available. |
| INP | How quickly the page responds to user interactions; one of the Core Web Vitals in field reporting. | PSI field data when available; include lab diagnostics that explain interaction work. |
| Speed Index | How quickly visible content is populated during loading. | Lighthouse lab and PSI lab runs. |
| TBT | Main-thread blocking during the lab load, useful for diagnosing script and CPU work. | Lighthouse lab and PSI lab runs. |
| TTFB or other server timing | Time spent waiting for the server response; it is a server measurement, not a substitute for browser experience metrics. | A separate server-timing row in the results. |
Track Mobile and Desktop separately. A change can improve one strategy and regress the other because CPU, viewport, network simulation, and layout behavior differ.
Use score bands as context, not as the verdict
Google’s documented Lighthouse score bands are 90–100 for good, 50–89 for needs improvement, and below 50 for poor. These bands help readers interpret a score, but they do not explain why the score moved or whether real visitors improved. A two-point score increase with a worse LCP may be a poor trade, while a small score movement accompanied by a meaningful LCP or INP improvement may matter to users.
Your comparison should therefore lead with metric deltas and the test conditions. Put the composite score beside those measurements rather than replacing them.
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Keep server timing separate from browser metrics
TTFB can help identify a slow origin, Worker, database request, cache miss, or rendering path. It should be reported in its own row and labeled with the measurement method and conditions. Do not combine TTFB with LCP, CLS, INP, or TBT into one “speed” number.
An independent SHIFT64 benchmark published April 15, 2026, reported a site-specific comparison of server processing and page-test outcomes. That experiment should not be treated as a universal EmDash result: server-processing differences and PageSpeed or Lighthouse outcomes are not interchangeable measurements.
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When Lighthouse improves consistently
If repeated Mobile and Desktop medians improve in the same direction and the page inputs stayed fixed, you have good lab evidence that the tested deployment changed loading or execution behavior. Identify which metric moved and use the opportunity audits to explain the likely mechanism. Do not generalize from one URL to every EmDash page unless the same template and assets were tested.
When Lighthouse is mixed
A faster FCP with unchanged or slower LCP can mean the first paint improved while the main content still waits on an image, font, data request, or script. Lower TBT with unchanged INP does not prove that real interactions improved, because INP is primarily evaluated from field data. A score that moves while individual metrics remain within normal run-to-run variation is not strong evidence of a product change.
When PSI lab and field disagree
Use the lab report to debug the current deployment and the field report to understand the experience of real visitors over its collection window. A good lab result with poor field LCP, INP, or CLS can indicate that visitors use slower devices, different networks, or routes and assets not represented by the synthetic setup. A poor lab result with acceptable field data may mean the lab conditions are harsher than the traffic mix or that the field window still contains older and newer deployments.
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When the field result has not changed yet
Check the collection period before drawing a conclusion. PSI field data is based on the previous 28 days and uses the 75th percentile for the Core Web Vitals assessment. Recheck after the new deployment has accumulated enough traffic to represent the intended audience. Until then, describe the Lighthouse result as lab evidence and avoid claiming a settled real-user improvement.
Build a results record readers can audit
Keep one row per measurement and retain the report URL or exported file. A useful record includes:
- URL and page-template description.
- Before or after deployment identifier, EmDash release, and Astro build.
- Cloudflare Workers settings, database region, cache policy, and static or server-rendered mode.
- Mobile or Desktop strategy and browser/test date.
- Lighthouse Performance score, FCP, LCP, Speed Index, CLS, and TBT for every run and the chosen summary.
- PSI lab values for Mobile and Desktop, report date, and opportunities.
- PSI field LCP, INP, and CLS at the displayed 75th percentile, with the trailing collection period.
- Separate server TTFB measurement and its collection method.
- Any deviation from the frozen content, scripts, assets, cache, or network assumptions.
Present the final comparison with columns for before, after, absolute difference, percentage difference, and evidence type. Label lab values as repeated runs and field values as a trailing CrUX window. If a field metric is unavailable because the URL or origin lacks sufficient traffic, omit that field from the comparison rather than treating its absence as a zero.
A practical decision framework
- Confirm the experiment: Verify that the URL, page inputs, deployment conditions, and test strategies match.
- Validate lab consistency: Compare repeated Lighthouse summaries, not a single best run.
- Find the user-facing change: Examine LCP, CLS, and INP first, then FCP, Speed Index, and TBT for diagnosis.
- Separate causes: Review TTFB and server timing independently from browser metrics.
- Check real-user evidence: Read PSI’s CrUX window and percentile, noting that it lags deployment.
- State the scope: Attribute the result only to the tested URL, template, and unchanged conditions. If several variables moved, report a combined-change outcome.
This process answers both “How much faster or slower is my EmDash site?” and “Does EmDash improve Core Web Vitals?” without confusing a synthetic score, a server benchmark, and real-user experience. The strongest claim is the one your recorded conditions and repeated metric-level evidence can support.
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