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Philosophy — local-first, bring your own model

TestivAI is built on two convictions about visual regression testing.

1. Detection belongs on your machine

Capture, diff, report, and approve are local operations. Your screenshots and DOM snapshots never need to leave your repo:

  • Baselines live in git — versioned, reviewable, no external storage.
  • The report is a filevisual-report/index.html is self-contained; share it as a CI artifact, open it anywhere.
  • The contract is publicresults.json is a semver-governed schema (status, diff %, region→selector attribution, DOM/style signals) that any tool can consume.
  • No account, ever. There is nothing to sign up for.

Local-first isn't a limitation — it's what makes TestivAI usable inside CI sandboxes, air-gapped environments, and AI-agent loops without provisioning a single secret.

2. The AI layer is your AI

Most visual testing tools bundle a hosted AI service to explain diffs. TestivAI deliberately doesn't. The layered analysis — pixel regions, element attribution ("div.card:nth-of-type(2) shifted +24px"), whole-page shift detection, style-only-change detection, the DOM noise hint — is computed locally and handed to whatever model you already use through the @testivai/mcp server:

  • explain_snapshot gives your agent the layered evidence bundle.
  • The review-visual-changes prompt turns it into a review narrative.
  • Claude Code, Cursor, Copilot, or any MCP client — the reasoning quality scales with your model, and nothing is uploaded to us.

The server ships evidence; the client ships intelligence. That split keeps TestivAI free, private, and permanently un-lock-in-able.

What this means practically

You wantYou get
Visual regression in CIThe CI gate recipe — commit baselines, gate on testivai report --fail-on-diff
PR-based approvalsThe GitHub Action — diff comment + /testivai approve
AI explanations of diffsThe MCP server + your own agent
Fewer false positivesStable baselines guide — stabilized captures, ignore modes, noise auto-pass

Everything above is MIT-licensed and works offline. That's the whole product.