Open Source Privacy-first SiFR v3

Your Tests Pass. Your UX Breaks.

CI is green. Deploy shipped. But users can't checkout because a promo banner covers the button on mobile.

Capture real rendered UI state โ€” with salience scoring โ€” so LLMs catch regressions that E2E tests miss.

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E2E Tests Say

  • โœ“ Button exists
  • โœ“ Button clickable
  • โœ“ Form submits

All pass. Ship it.

โœ“

Semantic Check Says

  • โš  "Pay" button: salience 95%
  • โš  Promo banner: salience 70%
  • โš  Occlusion detected: 60%

CTA blocked on mobile. Regression.

"We don't ask the model what matters โ€” we tell it."

SiFR pre-weights elements by salience. LLM interprets, not discovers. That's why CSS noise is ignored, but hidden CTAs trigger alerts.

What Tests Miss, This Catches

๐ŸŽฏ

Layout Regressions

  • โ€ข Banner covers checkout button
  • โ€ข Products pushed below the fold
  • โ€ข CMS update breaks grid
  • โ€ข A/B variant hides CTA
๐Ÿ“ฑ

Responsive Breaks

  • โ€ข Desktop OK, mobile broken
  • โ€ข Tablet layout overlap
  • โ€ข Third-party widget covers form
  • โ€ข Chat widget blocks action
๐Ÿ”’

Security Issues

  • โ€ข Defacement detection
  • โ€ข Phishing overlay on login
  • โ€ข Content injection in critical areas
  • โ€ข Unexpected DOM changes

How It Compares

Issue E2E Tests Visual Diff E2LLM
Button covered by banner โœ— Pass โš  Noise โœ“ Alert
Content pushed off-screen โœ— Pass โœ— Pass โœ“ Alert
Mobile-only break โœ— Miss โš  Maybe โœ“ Alert
CSS font change โœ“ Ignore โœ— 500 alerts โœ“ Ignore

How It Works

1

Capture

SiFR extracts DOM with salience scores. Not raw HTML โ€” semantic structure.

2

Interpret

LLM describes functional state. What users can do, not what code says.

3

Compare

Detect meaning changes, not pixel changes. Functional regressions only.

The Loop

1 You ship code
2 E2LLM captures what users actually see
3 LLM spots the problem (with real context)
4 You get actionable fix
5 Repeat

No screenshots. No guessing. Real state โ†’ Real answers.

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100% Local

Nothing leaves your browser. Ever. No tracking, no cloud.

โšก

Token Efficient

Compact JSON. 2KB instead of 10MB HTML. Built for LLM context.

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Playwright Ready

Programmatic API for CI/CD integration. Post-deploy checks.

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LLM Agnostic

Works with Claude, GPT, Grok, Llama. Your choice.

Your tests check if code works.

This checks if users can use it.

Free. Open source. Install in seconds.

Prompts & Examples ยท Curated prompts for QA, accessibility, security checks.

Your tests check if code works.
This checks if users can use it.

Install E2LLM โ†’