diff --git a/.amplifier/AGENTS.md b/.amplifier/AGENTS.md
index 2e58766..ed180cc 100644
--- a/.amplifier/AGENTS.md
+++ b/.amplifier/AGENTS.md
@@ -244,3 +244,43 @@ Key numbers for receipts:
 - 50k modules in memory as summaries: ~100MB (fits L3 cache)
 - ABS delivery: ~50% CDN bandwidth saved per deploy
 - 50k modules, 10k DAU, 1% weekly churn: saves ~47 GB/week
+
+## Rage Series: Author's Motivation
+
+The real story behind rage is not "I rewrote lage in Rust." It's: "I've wanted
+to combine lage's developer ergonomics with BuildXL's correctness at scale for
+years, and I finally got to do it." Lage gives you the dev experience. BuildXL
+gives you the guarantees. They've never existed in the same tool. Rage is the
+synthesis Ken has been thinking about since he co-created lage. Frame the series
+around that tension: ergonomics vs. correctness, and how you get both.
+
+## Cloudpack: Benchmarking With Synthetic Data
+
+Cloudpack benchmarks against synthetic codebases generated from observed
+attributes of real shipping code. The pipeline:
+
+1. **Observe**: `wundler-bench` scans a real repo and produces a `BenchProfile`
+   JSON capturing its statistical fingerprint: file counts by extension,
+   average file sizes, directory distribution, package structure.
+
+2. **Generate**: `corpus_gen` creates a deterministic synthetic codebase matching
+   the profile. Same seed = byte-identical output. TypeScript modules are
+   generated in three variants (barrel, intermediate, leaf) with realistic
+   import structures, side effects, and ambient refs. JSON configs, markdown
+   docs, and fallback files fill out the shape.
+
+3. **Verify**: `corpus_verify` confirms the generated corpus matches the profile
+   within tolerances (default: 2% file count, 5% byte/line count).
+
+4. **Benchmark**: Run the pipeline against the synthetic corpus at various
+   scales (100, 1k, 5k, 10k, 50k modules) and measure.
+
+This approach solves the "we can't share the real codebase" problem. The
+synthetic corpus has the same statistical properties (file sizes, dependency
+fan-out, side-effect density) as the real Teams codebase without containing
+any proprietary code. The benchmarks are reproducible, scalable, and public.
+
+This is worth a blog post on its own: "How We Benchmark a Bundler Without
+Sharing the Codebase." The profile schema, the archetype generators (barrel
+vs intermediate vs leaf modules), the determinism guarantees (same seed =
+identical bytes), and the conformance checks are all interesting.