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Performance

· 15 checks — Lighthouse + waterfall + sustainability rolled into one auditable list.
SCORE
80
GRADE
B
FIX
3
REVIEW
2
PASS
8
INFO
2

Performance-focused optimization plan with CWV assessment, top offenders, and time estimates

Mobile Desktop
77
Performance Overall performance score (0–100) based on Core Web Vitals and other metrics. 90+ is good.
100
Accessibility Measures how accessible the page is for users with disabilities. Checks color contrast, ARIA labels, and semantic HTML.
81
Best Practices Checks for modern web development best practices including HTTPS, no console errors, and secure JavaScript.
100
SEO Measures basic SEO optimizations: meta tags, crawlability, link text, and mobile friendliness.

First Contentful Paint First Contentful Paint — how long until the browser renders the first piece of content. Under 1.8s is good.

1.49 s

Largest Contentful Paint Largest Contentful Paint — how long until the largest visible element loads. Under 2.5s is good.

2.16 s

Total Blocking Time Total Blocking Time — total time the main thread was blocked, preventing user input. Under 200ms is good.

4 ms

Cumulative Layout Shift Cumulative Layout Shift — measures visual stability. How much the page layout shifts during loading. Under 0.1 is good.

0.130

Speed Index Speed Index — how quickly content is visually displayed during load. Under 3.4s is good.

1.95 s

Time to Interactive Time to Interactive — how long until the page is fully interactive and responds to user input. Under 3.8s is good.

2.62 s

Page Load Progression

375 ms
750 ms
1.1 s
1.5 s
1.9 s
2.2 s
2.6 s
3.0 s
Checks
15
8 PASS 2 REVIEW 3 FIX
F
Page Weight Budget
Action
3.2 MB transferred, 96 requests
FIX
3.2 MB transferred, 96 requests
Warning::
Page weighs 5.5 MB (3.2 MB transferred)
Warning::
Images are 2.0 MB — compress or use modern formats
Convert images to WebP/AVIF and resize to display dimensions to reduce transfer size.
Got: 2.0 MB
Warning::
JavaScript is 800 KB — consider code splitting or lazy loading
Large JavaScript bundles delay interactivity. Split code by route or defer non-critical scripts.
Got: 800 KB
Info::
Fonts are 289 KB — consider subsetting or using system fonts
Subset fonts to include only used characters, or switch to system font stacks.
Got: 289 KB
Info::
96 HTTP requests
Info::
Estimated 0.67 g CO2 per page load
3.2 MB / 2.4 MB budget
3.2 MB transferred
96 requests
0.67 g CO2 per page load
Images 2.0 MiB 62%
JavaScript 799.8 KiB 24%
Fonts 288.9 KiB 9%
Other 50.3 KiB 2%
HTML 43.6 KiB 1%
Other 25.3 KiB 1%
Other 24.1 KiB 1%
CSS 9.2 KiB 0%
Other 260 B 0%
Other 0 B 0%
0 2.4 MB 4.9 MB
Over budget

Convert images to WebP/AVIF and resize to display dimensions to reduce transfer size.

Why this matters

Image bandwidth is high — modern formats (WebP/AVIF) and resizing typically cut it 50%+.

Source: web.dev

Large JavaScript bundles delay interactivity. Split code by route or defer non-critical scripts.

Why this matters

JavaScript bundle is large — code-split routes and lazy-load off-screen components to defer.

Source: web.dev

Subset fonts to include only used characters, or switch to system font stacks.

Why this matters

Font payload is large — subset to used glyphs only, or fall back to system fonts (zero load time).

Source: web.dev font loading

D
JS Bundles
Action
33 scripts, 307 KB unused
FIX
33 scripts, 307 KB unused
Warning::
https://securepubads.g.doubleclick.net/pagead/mana...: 107 KB unused (57%)
Consider code splitting or tree shaking to reduce unused code.
Warning::
https://www.googletagmanager.com/gtag/js?id=G-3XH4...: 63 KB unused (40%)
Consider code splitting or tree shaking to reduce unused code.
Warning::
https://www.googletagmanager.com/gtm.js?id=GTM-NDX...: 61 KB unused (52%)
Consider code splitting or tree shaking to reduce unused code.
Warning::
https://h030.towardsdatascience.com/plugin/library...: 33 KB unused (81%)
Consider code splitting or tree shaking to reduce unused code.
Warning::
https://h030.towardsdatascience.com/plugin/plugin/...: 22 KB unused (70%)
Consider code splitting or tree shaking to reduce unused code.
Info::
Total unused JavaScript: 307 KB
307 KB unused JavaScript

Downloaded but never executed on this page.

ScriptSizeUnusedMinifiedParty
https://securepubads.g.doubleclick.net/pagead/managed/js/gpt/m202604160201/pubads_impl.js188 KB107 KB (57%)✓3rd
https://www.googletagmanager.com/gtag/js?id=G-3XH4NJPYQ3&cx=c&gtm=4e64h1159 KB63 KB (40%)✓3rd
https://www.googletagmanager.com/gtm.js?id=GTM-NDXD39TW119 KB61 KB (52%)✓3rd
https://h030.towardsdatascience.com/plugin/library/7cca1e1e3b698c80920cd851b1480f5441 KB33 KB (81%)✓3rd
https://h030.towardsdatascience.com/plugin/plugin/dd57986da60b4d6466f09420824e2e8732 KB22 KB (70%)✓3rd
https://cdn.insightmediagroup.io/imgjs/v1/imgjs-v1.js?ver=177680844428 KB21 KB (77%)✗3rd
https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation//dist/js/scroll-lock.js?ver=2.1.54 KB—✓1st
https://towardsdatascience.com/wp-content/themes/tds-theme/dist/js/frontend-module.js?ver=485fb0b9996654cbfa281 KB—✓1st
https://h030.towardsdatascience.com/script.js41 KB—✓3rd
https://scripts.simpleanalyticscdn.com/latest.js5 KB—✓3rd

Consider code splitting or tree shaking to reduce unused code.

Why this matters

This bundle has high unused code — code-split or tree-shake to ship only what executes.

Source: web.dev / Lighthouse coverage

Consider code splitting or tree shaking to reduce unused code.

Why this matters

This bundle has high unused code — code-split or tree-shake to ship only what executes.

Source: web.dev / Lighthouse coverage

Consider code splitting or tree shaking to reduce unused code.

Why this matters

This bundle has high unused code — code-split or tree-shake to ship only what executes.

Source: web.dev / Lighthouse coverage

Consider code splitting or tree shaking to reduce unused code.

Why this matters

This bundle has high unused code — code-split or tree-shake to ship only what executes.

Source: web.dev / Lighthouse coverage

Consider code splitting or tree shaking to reduce unused code.

Why this matters

This bundle has high unused code — code-split or tree-shake to ship only what executes.

Source: web.dev / Lighthouse coverage

D
Page Weight Inventory
Action
96 resources · 3.2 MB · 307 KB savings available
FIX
96 resources · 3.2 MB · 307 KB savings available
Warning::
Page weight 2.2 MB over 1MB target
Got: 3.2 MB Expected: ≤ 1 MB
Info::
~307 KB of savings available
Info::
47 third-party resources (25% of weight)
Info::
2 resources over 200KB
96 resources · 3.2 MiB · 47 third-party 2.2 MiB over 1MB target
Image 62% · 2.0 MiB
JavaScript 24% · 799.8 KiB
Font 9% · 288.9 KiB
Document 2% · 79.1 KiB
Other 2% · 63.6 KiB
CSS 0% · 9.2 KiB

~307.1 KiB of savings available

Remove unused JavaScript: 307.1 KiB
B
Image Optimization
30 images, 0 KB saveable
REVIEW
30 images, 0 KB saveable
Info::
All images are well-optimized
30 images 2.0 MB
0 oversized -0 KB
29 legacy format
0 missing dimensions CLS risk
C
Green Hosting
Action
Whether the site is served from green-energy infrastructure
REVIEW

Green Hosting

No green hosting detected

A
Third-Party Impact
25% third-party, 0 ms blocking
PASS
25% third-party, 0 ms blocking
Info::
Third-party code accounts for 25% of page weight (835.7 KiB of 3.2 MiB)
Info::
Third-party blocking time is low (0 ms)
75%
25%
First-party Third-party
A+
Text Compression
All text resources are compressed
PASS
All text resources are compressed
Info::
All text resources are compressed
All text resources are properly compressed.
A+
JS Execution Cost
377ms total JS execution
PASS
377ms total JS execution
Info::
Unattributable: 139ms CPU time
Info::
https://towardsdatascience.com/: 137ms CPU time
Info::
https://securepubads.g.doubleclick.net/pagead/mana...: 101ms CPU time
Warning::
Third-party scripts: 240ms (64% of total)

Main Thread Breakdown

Script Evaluation 52% Other 21% Style & Layout 14% Script Parsing & Compilation 7% Rendering 3%
ScriptTotalScriptingParse/CompileParty
Unattributable139ms25ms0ms3rd
https://towardsdatascience.com/137ms9ms1ms1st
https://securepubads.g.doubleclick.net/pagead/managed/js/gpt/m202604160201/pubads_impl.js101ms85ms11ms3rd
A+
Font Loading
5 fonts (289 KB)
PASS
5 fonts (289 KB)
Info::
5 font(s) use font-display: swap (FOUT risk but functional)

Web fonts

5

289 KB total

Render-blocking

0

of 5

Dominant font-display

swap

Most common across fonts

Font loading timeline
Transfer FOIT (block) FOUT (swap)
0 ms 500 ms 1.0 s 1.5 s 2.0 s FCP 1.5 s LCP 2.2 shttps://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY_z_wNahGAdqQ43RhVcIgYT2Xz5u32K0nXNi0Dp6_cOyA.woff2QGY_z_wNahGAdqQ43RhVcIg... https://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY_z_wNahGAdqQ43RhVcIgYT2Xz5u32K0nXNi0Dp6_cOyA.woff2 — 56.2 KiB — 84 ms — font-display: swaphttps://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY_z_wNahGAdqQ43RhVcIgYT2Xz5u32K67QNi0Dp6_cOyA.woff2QGY_z_wNahGAdqQ43RhVcIg... https://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY_z_wNahGAdqQ43RhVcIgYT2Xz5u32K67QNi0Dp6_cOyA.woff2 — 60.2 KiB — 39 ms — font-display: swaphttps://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY_z_wNahGAdqQ43RhVcIgYT2Xz5u32K5fQNi0Dp6_cOyA.woff2QGY_z_wNahGAdqQ43RhVcIg... https://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY_z_wNahGAdqQ43RhVcIgYT2Xz5u32K5fQNi0Dp6_cOyA.woff2 — 60.1 KiB — 33 ms — font-display: swaphttps://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY_z_wNahGAdqQ43RhVcIgYT2Xz5u32K3vXNi0Dp6_cOyA.woff2QGY_z_wNahGAdqQ43RhVcIg... https://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY_z_wNahGAdqQ43RhVcIgYT2Xz5u32K3vXNi0Dp6_cOyA.woff2 — 59.4 KiB — 24 ms — font-display: swaphttps://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY9z_wNahGAdqQ43Rh_ebrnlwyYfEPxPoGU3msJpo3ZKyDSQQ.woff2QGY9z_wNahGAdqQ43Rh_ebr... https://towardsdatascience.com/wp-content/themes/tds-theme/assets/fonts/QGY9z_wNahGAdqQ43Rh_ebrnlwyYfEPxPoGU3msJpo3ZKyDSQQ.woff2 — 52.9 KiB — 28 ms — font-display: swap
QGY_z_wNahGAdqQ43RhVcIg... woff2 swap
Size 56 KB
Load time 84 ms
Start 1.7 s
Risk FOUT — text flashes from fallback to web font
QGY_z_wNahGAdqQ43RhVcIg... woff2 swap
Size 60 KB
Load time 39 ms
Start 1.7 s
Risk FOUT — text flashes from fallback to web font
QGY_z_wNahGAdqQ43RhVcIg... woff2 swap
Size 60 KB
Load time 33 ms
Start 2.1 s
Risk FOUT — text flashes from fallback to web font
QGY_z_wNahGAdqQ43RhVcIg... woff2 swap
Size 59 KB
Load time 24 ms
Start 2.1 s
Risk FOUT — text flashes from fallback to web font
QGY9z_wNahGAdqQ43Rh_ebr... woff2 swap
Size 53 KB
Load time 28 ms
Start 2.1 s
Risk FOUT — text flashes from fallback to web font
Optimization checklist
  • Preload critical fonts (priority=high)
  • Use woff2 format for all fonts
  • Set font-display to swap, optional, or fallback
  • Subset large fonts (≤100 KB each)
A+
Resource Caching
All resources properly cached
PASS
All resources properly cached
Info::
No caching issues found
All static resources have appropriate caching headers.
A+
Critical Rendering Path
No render-blocking resources
PASS
No render-blocking resources
Info::
No render-blocking resources detected
A+
Resource Hints
No optimization needed
PASS
No optimization needed
Info::
No resource hint issues
A
HTTP Caching
max-age=600 (10 minutes)
PASS
max-age=600 (10 minutes)
Info::
Cache-Control header is set
Got: max-age=600, must-revalidate
Info::
Effective TTL: 10 minutes (max-age=600)
Info::
No ETag or Last-Modified header
Conditional requests (304 Not Modified) are not possible without validators.
Cache-Control

max-age=600, must-revalidate

DirectiveValueMeaning
max-age600Cache for 10 minutes
must-revalidate—Must recheck with server after expiry
Effective TTL: 600 seconds
Network Waterfall
96 requests over 3727ms
INFO
0 ms 1.0 s 2.0 s 3.0 s FCP 1.5 s LCP 2.2 shttps://towardsdatascience.com// https://towardsdatascience.com/ — 200 — 34.7 KiB — 255 ms — h2https://h030.towardsdatascience.com/script.jsh030.towardsdatascience.com/script.... https://h030.towardsdatascience.com/script.js — 200 — 41.1 KiB — 74 ms — h2https://towardsdatascience.com/wp-content/plugins/cookie-law-info/lite/frontend/js/gcm.min.js/wp-content/plugins/cookie-law-info... https://towardsdatascience.com/wp-content/plugins/cookie-law-info/lite/frontend/js/gcm.min.js — 200 — 1.2 KiB — 25 ms — h2https://cdn-cookieyes.com/client_data/291af792ab94792a114ad850408e1376/script.jscdn-cookieyes.com/client_data/291af... https://cdn-cookieyes.com/client_data/291af792ab94792a114ad850408e1376/script.js — 200 — 9.6 KiB — 62 ms — h2https://towardsdatascience.com/wp-includes/blocks/social-links/style.min.css?ver=6.9.4/wp-includes/blocks/social-links/st... https://towardsdatascience.com/wp-includes/blocks/social-links/style.min.css?ver=6.9.4 — 200 — 1.6 KiB — 28 ms — h2https://towardsdatascience.com/wp-includes/blocks/navigation/style.min.css?ver=6.9.4/wp-includes/blocks/navigation/styl... https://towardsdatascience.com/wp-includes/blocks/navigation/style.min.css?ver=6.9.4 — 200 — 2.4 KiB — 27 ms — h2https://towardsdatascience.com/wp-content/themes/ui-kit-theme/dist/css/parent-frontend.css?ver=8a1d11518d3564270cd0/wp-content/themes/ui-kit-theme/dis... https://towardsdatascience.com/wp-content/themes/ui-kit-theme/dist/css/parent-frontend.css?ver=8a1d11518d3564270cd0 — 200 — 1.2 KiB — 37 ms — h3https://towardsdatascience.com/wp-content/themes/tds-theme/dist/css/frontend.css?ver=823aff592d494f29250f/wp-content/themes/tds-theme/dist/c... https://towardsdatascience.com/wp-content/themes/tds-theme/dist/css/frontend.css?ver=823aff592d494f29250f — 200 — 4.1 KiB — 33 ms — h3https://cdn.insightmediagroup.io/imgjs/v1/imgjs-v1.js?ver=1776808444cdn.insightmediagroup.io/imgjs/v1/i... https://cdn.insightmediagroup.io/imgjs/v1/imgjs-v1.js?ver=1776808444 — 200 — 27.7 KiB — 1.4 s — h3https://towardsdatascience.com/wp-content/plugins/tds-plugin/dist/js/imgjs-init.js?ver=1c96dc2348cfec57ddaf/wp-content/plugins/tds-plugin/dist... https://towardsdatascience.com/wp-content/plugins/tds-plugin/dist/js/imgjs-init.js?ver=1c96dc2348cfec57ddaf — 200 — 3.4 KiB — 30 ms — h3https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation/dist/blocks/site-header/view-module.js?ver=8cb9c85bda4b30f20a6e/wp-content/plugins/ui-kit-navigati... https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation/dist/blocks/site-header/view-module.js?ver=8cb9c85bda4b30f20a6e — 200 — 1.5 KiB — 38 ms — h3https://towardsdatascience.com/wp-content/themes/tds-theme/dist/js/frontend-module.js?ver=485fb0b9996654cbfa28/wp-content/themes/tds-theme/dist/j... https://towardsdatascience.com/wp-content/themes/tds-theme/dist/js/frontend-module.js?ver=485fb0b9996654cbfa28 — 200 — 537 B — 45 ms — h3https://towardsdatascience.com/wp-includes/js/dist/script-modules/interactivity/index.min.js?ver=66c613f68580994bb00a/wp-includes/js/dist/script-modules... https://towardsdatascience.com/wp-includes/js/dist/script-modules/interactivity/index.min.js?ver=66c613f68580994bb00a — 200 — 15.4 KiB — 39 ms — h3https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation//dist/js/scroll-lock.js?ver=2.1.5/wp-content/plugins/ui-kit-navigati... https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation//dist/js/scroll-lock.js?ver=2.1.5 — 200 — 3.6 KiB — 37 ms — h3https://towardsdatascience.com/wp-content/uploads/2025/02/TDS-Vector-Logo.svg/wp-content/uploads/2025/02/TDS-Vec... https://towardsdatascience.com/wp-content/uploads/2025/02/TDS-Vector-Logo.svg — 200 — 5.5 KiB — 46 ms — h3https://towardsdatascience.com/wp-content/uploads/2026/04/ChatGPT-Image-Mar-6-2026-04_19_28-PM.jpg/wp-content/uploads/2026/04/ChatGPT... https://towardsdatascience.com/wp-content/uploads/2026/04/ChatGPT-Image-Mar-6-2026-04_19_28-PM.jpg — 200 — 342.8 KiB — 43 ms — h3https://towardsdatascience.com/wp-content/uploads/2026/04/pexels-padrinan-2882520-scaled-1-1536x1021.jpg/wp-content/uploads/2026/04/pexels-... https://towardsdatascience.com/wp-content/uploads/2026/04/pexels-padrinan-2882520-scaled-1-1536x1021.jpg — 200 — 129.8 KiB — 45 ms — h3https://towardsdatascience.com/wp-content/uploads/2026/04/ChatGPT-Image-Apr-15-2026-02_19_58-PM.jpg/wp-content/uploads/2026/04/ChatGPT... https://towardsdatascience.com/wp-content/uploads/2026/04/ChatGPT-Image-Apr-15-2026-02_19_58-PM.jpg — 200 — 475.9 KiB — 35 ms — h3https://scripts.simpleanalyticscdn.com/latest.jsscripts.simpleanalyticscdn.com/late... https://scripts.simpleanalyticscdn.com/latest.js — 200 — 4.9 KiB — 83 ms — h2https://log.cookieyes.com/api/v1/loglog.cookieyes.com/api/v1/log https://log.cookieyes.com/api/v1/log — 200 — 260 B — 63 ms — h3https://towardsdatascience.com/wp-content/themes/ui-kit-theme/dist/js/post-template-view-script.js?ver=a0d80fcdb9e0f5246681/wp-content/themes/ui-kit-theme/dis... https://towardsdatascience.com/wp-content/themes/ui-kit-theme/dist/js/post-template-view-script.js?ver=a0d80fcdb9e0f5246681 — 200 — 631 B — 24 ms — h3https://securepubads.g.doubleclick.net/tag/js/gpt.jssecurepubads.g.doubleclick.net/tag/... https://securepubads.g.doubleclick.net/tag/js/gpt.js — 200 — 33.6 KiB — 92 ms — h3https://towardsdatascience.com/wp-content/themes/tds-theme/dist/js/frontend.js?ver=823aff592d494f29250f/wp-content/themes/tds-theme/dist/j... https://towardsdatascience.com/wp-content/themes/tds-theme/dist/js/frontend.js?ver=823aff592d494f29250f — 200 — 578 B — 28 ms — h3https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation/dist/blocks/navigation-portal/view-module.js?ver=36e5df5c5f75790692e9/wp-content/plugins/ui-kit-navigati... https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation/dist/blocks/navigation-portal/view-module.js?ver=36e5df5c5f75790692e9 — 200 — 1.6 KiB — 24 ms — h3https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation/dist/blocks/navigation/view-module.js?ver=d0f9bae99aa4f0b00e33/wp-content/plugins/ui-kit-navigati... https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation/dist/blocks/navigation/view-module.js?ver=d0f9bae99aa4f0b00e33 — 200 — 1.4 KiB — 41 ms — h3https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation/dist/blocks/search-button/view-module.js?ver=925dab9fdd8c362c4d20/wp-content/plugins/ui-kit-navigati... https://towardsdatascience.com/wp-content/plugins/ui-kit-navigation/dist/blocks/search-button/view-module.js?ver=925dab9fdd8c362c4d20 — 200 — 773 B — 42 ms — h3https://towardsdatascience.com/wp-content/plugins/tds-plugin/dist/js/ad-manager.js?ver=c9a02b48344f6964179a/wp-content/plugins/tds-plugin/dist... https://towardsdatascience.com/wp-content/plugins/tds-plugin/dist/js/ad-manager.js?ver=c9a02b48344f6964179a — 200 — 1.6 KiB — 51 ms — h3https://js.hs-scripts.com/46095657.js?businessUnitId=2278289js.hs-scripts.com/46095657.js?busin... https://js.hs-scripts.com/46095657.js?businessUnitId=2278289 — 200 — 1.1 KiB — 72 ms — h2https://insightmediagroup.blueconic.net/DG/DEFAULT/cs?&callback=bc_json928insightmediagroup.blueconic.net/DG/... https://insightmediagroup.blueconic.net/DG/DEFAULT/cs?&callback=bc_json928 — 200 — 733 B — 297 ms — h2https://www.googletagmanager.com/gtm.js?id=GTM-NDXD39TWwww.googletagmanager.com/gtm.js?id=... https://www.googletagmanager.com/gtm.js?id=GTM-NDXD39TW — 200 — 118.7 KiB — 147 ms — h2
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https://ep1.adtrafficquality.google/pagead/sodar?id=sodar2&v=254&t=2&li=gpt_m202604160201&jk=3133459472098900&bg=!tbalttTNAAY2C5OeFgs7AEcBe5WfOAKs8THdpbViEQYPclTL7YlqRWEXz0hdEJURua0Ag-7_CQeU8Zw2QGgeHnUt9iMOsdOmRvf2hvUK281Zeb1pEXVLlAIAAAA3UgAAAARoAQd-ADfBAdS_BrgLA7Zs2OZEJh5G9Kch2Scd5m90hw3eey0AsxZkKGTTomJES-RHETyx3n4gd-6em0wdmQKmCmMSpyJ4BFe0BkqjHGIpURmdeYtE3TA4aydCq-dbqJfaCr-6IF0R9Pb-MX3XDXxNF1WJxKTikvbYfhSnUZ-36OV7A2Qaf0mWRqjbfcOEaEg0xlKltqjSC07myqBoNIykMkzwll55Q94nFqTBboR6DeiyLd2BD8Aom_Cvk47JvQ0YeGYhkTfDDebYmbfQIOP8HJfFQg0zQaR-Ov6MbpjVTwSi40NUMxBS9XhLUm3MAoSLWdeBN-Qv3l0Dt9Dha48LzB06ZDnJciywdk4W9r1y_z4RJ--zRRbebbNpJuuxJ9Fjcl4qwcAe8r5MRN56PQcgaGJngAa32-6dwbfcKDHWOQBFlvCU4DgVuPy0HPp0moJEQZUdwSEy__Qe-NWAimUlKzEKykNQx0MKxwIOXLnIYOSNlU7_91PeM1Hlidn6FA4AeUA6Z6uSCDWwjxgf7BploDTmUodTSjfLfRyUtRqyb1y4zmpJxdja5nS8mPyOXZLcWJvJ2t55y0nP1NweWsP56ET_L0pnTteaq1188oatHeZ3s3QJtQSQFX7bWKuhoQRH6wTjXr1hUFWz5kc74lOlcybhz_vSk8ZL1Mp_NM1D0tyP8IoLBVav190dMpKF9bOgTsthMTbJPLt13nNLpRSNCeoiuGRL3N0AsHj6865Et14nzRYs5S6APeTgD70pYGmK8wYblgRWayUkG_uebeBs6A8KDvp2YWgutKPuvFraZfSKADQK78fYizJdvbuiiXlcqFpHuBAXC6QGA1rgG7WicB7ULCF7qBQVvIUs0OcB0i4TsFG-5-9FfY1L6nmgmMZg7xqsYttP_BTIV6oypB_hhB9AtMEhXGajPDulxaaNuacs1TcTG_5dqb763a7YFUVxySJ-oX-tzdP0-dCTb61cAqZzTtm3 — 204 — 17 B — 46 ms — h3https://img-api-v2.towardsdatascience.com/api/accounts/meimg-api-v2.towardsdatascience.com/a... https://img-api-v2.towardsdatascience.com/api/accounts/me — 200 — 686 B — 157 ms — h2
HTML JavaScript CSS Images Fonts XHR/Fetch Other
Third-Party Script Cost
Per-script blocking time, transfer cost, and cache headers
INFO
64% of JavaScript execution is third-party
First-party Third-party 240ms · 188KB · €3/mo
Unattributable
Other
Optional
Execution 139ms
Transfer 0 KB
Monthly Cost €2/mo
securepubads.g.doubleclick.net
Other
Optional
Execution 101ms
Transfer 188 KB
Unused 57%
Monthly Cost €1/mo

64% of JavaScript execution time comes from third-party scripts.

Why this matters

Third-party scripts (analytics, ads, social, A/B testing) often dominate execution time — every one is a perf-and-privacy tax.

Learn more ▾

Each third-party script is a black box: you don't control when it loads, what it executes, or how much it grows. They often account for a major share of total blocking time on average sites (HTTP Archive's Web Almanac documents the trend). Audit which ones you actually need, defer the rest, and use facade patterns (lite-youtube, lite-vimeo) for embedded media.

Source: web.dev / HTTP Archive Web Almanac

57% of securepubads.g.doubleclick.net's code is unused. The script may be loading features you don't use.

Why this matters

Bundle has high unused-code ratio — tree-shaking and route-splitting recover the wasted bytes.

Source: web.dev

How third-party costs are calculated

All checks on this page are automated. Results are estimates - run targeted manual reviews when the score affects a release decision.

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