/compare · head-to-head & methodology

How Slop Detector compares

Compare any two domains side by side on the published 2026.10 leaderboard. The live engine is definitions 2026.11 and those rows have not been re-scored. The score is a fixed catalogue evaluated in Chromium.

§ 00interactive domain arena · Slop score · lower is better

Head-to-head fingerprint comparison

matchups:

Loading corpus benchmarks…

§ 01the short version

One fixed yardstick, not a moving model

Most "is this AI?" tools are probabilistic ML classifiers: they output a confidence that something was machine-generated, and that confidence shifts as the model is retrained. Slop Detector works the other way. It is a deterministic fingerprint: a fixed catalogue of CSS and copy tells, each with a fixed weight, evaluated against a page's live computed styles in real headless Chromium. The same page always yields the same score. No model, no randomness, so the output is auditable, reproducible, and safe to gate CI on.

Isometric exploded view of a landing page inspected across five optical DOM strata — **FIG. 01** · Five-layer optical DOM inspection: typography, contrast, layout topology, copy rhetoric, and AEO structured data corpus·2026.10 · engine·2026.11

§ 02methodology comparison

How the approaches differ

Methodology comparison across Slop Detector, ML AI-detectors, and Lighthouse/a11y tools
Dimension	Slop Detector	Typical ML AI-detectors	Generic Lighthouse / a11y
Output	Deterministic 0–100 weighted fingerprint	Probabilistic % confidence	Perf / a11y / SEO scores
Reproducible	Yes, same page, same score	No, drifts with retraining	Mostly
What it measures	AI-design + copy slop tells	Generated-vs-human likelihood	Technical quality, not aesthetics
Engine	Real Chromium, computed styles	Text / pixel models	Real Chromium
Auditable rules	Yes, open catalogue at /api/patterns	Opaque weights	Documented audits
AEO axis (AI-readability)	Yes, built in	No	Partial (SEO only)
License	Open source (MIT)	Usually closed	Mixed
Agent interfaces	API + CLI + MCP	Rare	Rare

§ 03why deterministic matters

What a fixed score buys you

CI gating. Fail a build at --fail-on heavy and trust the threshold won't move under you.
Auditability. Every point in a score traces to a named pattern with a documented weight.
Comparability. Two designs measured on the same fixed yardstick, today and next year.
No false confidence. A high score says "this matches many default-template patterns," not "an AI wrote this." A fingerprint, not a verdict.

§ 04the research behind it

Where the weights come from

The pattern weights derive from Adrian Krebs's April 2026 study of 1,400 Show HN submissions, plus Meng To's default-template observations. The catalogue is versioned (DEFINITIONS_VERSION) and served live at /api/patterns.

§ 05when to use which

Pick by the question you're asking

Slop Detector: a reproducible, explainable read on how many default-template patterns a landing page matches, or to gate design regressions in CI.
An ML AI-detector: a probabilistic "was this generated?" verdict on arbitrary text or images.
Lighthouse / axe: performance, accessibility, and technical SEO. Orthogonal to slop.

§ 06try it

Run a scan

Web: slop-detect.com · CLI: npx slop-detect <url> · MCP: slop-detect-mcp · API: /openapi.json · Source: GitHub