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docs: AI/ML-first README rewrite (698 → 170 lines)#180

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pmclSF merged 1 commit into
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docs/readme-rewrite-clean
May 13, 2026
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docs: AI/ML-first README rewrite (698 → 170 lines)#180
pmclSF merged 1 commit into
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docs/readme-rewrite-clean

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@pmclSF pmclSF commented May 13, 2026

Summary

  • Replaces 698-line README with a 170-line, AI/ML-first version
  • Leads with the strategic moat ("CI gate for AI/ML codebases") instead of generic test-tooling positioning
  • Centers a detector table with 11 named, shipping detectors — names verified against `internal/signals/signal_types.go`
  • Cuts 143 lines of not-literal example output (now links to `docs/examples/`)
  • Consolidates three duplicate install sections into one
  • One voice throughout; removes Product Philosophy / Who Uses Terrain / How CI Optimization Emerges manifesto sections (which live in `docs/product/vision.md`)
  • Compresses Tier 1/2/3 status from 60 lines to 6, with link to `docs/release/feature-status.md` for full detail

Why now

The previous README opened with "control plane for your test system" and never explained what made Terrain different from Codecov + Vitest + a JUnit reporter. It welded together three different identities (test tool, AI tool, framework-migration tool), duplicated install + quickstart sections three times, and embedded fake example output with a disclaimer that it wasn't real. An engineer evaluating in 30 seconds couldn't tell what Terrain does.

This rewrite is aligned to the AI/ML-first strategic direction and names the killer-gap detectors that are the actual moat.

Test plan

  • Visual check: render on GitHub looks clean
  • All linked docs paths exist (verified locally against `docs/` tree)
  • All detector names match `internal/signals/signal_types.go` (verified)
  • No phantom detectors named (verified — `train_script_missing_tracker` from boundary edges intentionally omitted since it's not a deployed signal yet)

🤖 Generated with Claude Code

The previous README welded three different identities together (test
control plane, AI testing tool, framework migration helper), duplicated
install + quickstart sections three times, embedded 143 lines of
not-literal example output, and never named the strategic moat. The
opening line ("control plane for your test system") said nothing about
what makes Terrain different from Codecov + Vitest + a JUnit reporter.

This rewrite:
- Leads with the moat: "Terrain is a CI gate for AI/ML codebases"
- Names the thesis: AI not a black box to the codebase, vice versa
- Centers a detector table with 11 named, shipping detectors, names
  verified against internal/signals/signal_types.go
- Cuts the fake CLI output (links to docs/examples/ instead)
- Compresses Tier 1/2/3 status from 60 lines to 6
- Removes "Product Philosophy", "Who Uses Terrain", "How CI Optimization
  Emerges" — they move to docs/product/vision.md, which is linked
- Consolidates three install sections into one
- One voice throughout: direct, technical, no manifesto

Length: 698 → 170 lines. Same coverage of stable capabilities, much
sharper positioning, no phantom detectors named.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
@pmclSF pmclSF merged commit bd98644 into main May 13, 2026
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@pmclSF pmclSF deleted the docs/readme-rewrite-clean branch May 13, 2026 05:23
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[INFO] Terrain — Informational only

Insufficient data to assess change risk confidently.

Metric Value
Changed files 1 (0 source · 0 test)

All clear. No new findings introduced; no protection gaps identified in changed code.

Run terrain compare over time to track posture; this clean state is the bar to hold.


Limitations
  • No coverage artifacts provided; protection gaps reflect missing data, not measured absence. Provide --coverage to improve accuracy.
  • Mixed test cultures reduce cross-framework optimization confidence. Consider standardizing on fewer frameworks.

Generated by Terrain · terrain pr --json for machine-readable output

Targeted Test Results

No tests selected — change affects only non-code files.

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Terrain AI Risk Review

Metric Value
AI surfaces 13
Eval scenarios 17
Impacted scenarios 0
Uncovered surfaces 13

Decision: PASS — AI surfaces are covered.

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