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feat(ship-systems): render metrics from content/data/benchmarks.json
Ship Systems section previously held six headline benchmark numbers as literal strings in the Tera template. They were honest but static, with no timestamp and no traceability back to the source measurement. This change replaces them with a Zola load_data call over a new JSON file at content/data/benchmarks.json, and adds a visible "Last measured" date line citing the source. Schema: generated_at, source{repo,commit,workflow_run,notes}, metrics{ label,value,unit} per field. Six metrics today: inference_ns, search_latency_ms, index_throughput_mibps, graph_footprint_mb, index_build_100_terms_us, index_build_1000_terms_ms. Adding a metric is append-only. The JSON file is hand-authored from the 2025-11-11 test report and the rust-wasm capability page, with source.commit set to a sentinel "manual-snapshot-2025-11-11". Gitea issue #574 tracks the follow-up work in terraphim-ai to generate this file automatically from Criterion output via a new scripts/update-benchmark-snapshot.sh plus a cross-repo PR workflow. Steps 2 and 3 of the design plan are deferred. Research + design docs live at .docs/research-ship-systems-benchmarks.md and .docs/design-ship-systems-benchmarks.md (gitignored). Verified: build completes in ~200ms, dev-browser confirms all six values render from the data file and <time datetime= "2025-11-11T00:00:00Z"> is set. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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‎content/data/benchmarks.json‎

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{
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"generated_at": "2025-11-11T00:00:00Z",
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"source": {
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"repo": "terraphim/terraphim-ai",
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"commit": "manual-snapshot-2025-11-11",
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"workflow_run": null,
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"notes": "Hand-authored from docs/archive/root/test_benchmark_report_20251111.md and content/capabilities/rust-wasm.md. Will be replaced by automated snapshot once terraphim-ai scripts/update-benchmark-snapshot.sh lands (see Gitea #574)."
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},
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"metrics": {
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"inference_ns": {
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"label": "Inference",
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"value": "5-10",
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"unit": "ns"
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},
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"search_latency_ms": {
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"label": "Search query",
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"value": "< 1",
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"unit": "ms"
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},
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"index_throughput_mibps": {
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"label": "Index throughput",
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"value": "~310",
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"unit": "MiB/s"
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},
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"graph_footprint_mb": {
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"label": "Graph footprint",
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"value": "15-20",
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"unit": "MB"
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},
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"index_build_100_terms_us": {
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"label": "Index build (100 terms)",
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"value": "~301",
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"unit": "\u00b5s"
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},
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"index_build_1000_terms_ms": {
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"label": "Index build (1,000 terms)",
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"value": "~3.66",
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"unit": "ms"
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}
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}
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}

‎themes/WarpDrive/templates/index.html‎

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</section>
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<!-- ---- SYSTEM READOUTS ---- -->
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{% set bench = load_data(path="content/data/benchmarks.json") %}
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{% set inference = bench.metrics.inference_ns %}
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{% set search_latency = bench.metrics.search_latency_ms %}
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{% set throughput = bench.metrics.index_throughput_mibps %}
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{% set graph_footprint = bench.metrics.graph_footprint_mb %}
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{% set build_100 = bench.metrics.index_build_100_terms_us %}
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{% set build_1000 = bench.metrics.index_build_1000_terms_ms %}
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<section class="section" id="systems" aria-label="System readouts">
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<div class="section-inner">
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<div class="section-overline">
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<div class="system-card-body">
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<p>Rust compiled to native or WebAssembly. Aho-Corasick automata match every pattern in a single pass -- O(n) in text length, independent of pattern count.</p>
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<div class="metric">
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<span class="metric-label">Inference</span>
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<span class="metric-value">5-10 ns</span>
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<span class="metric-label">{{ inference.label }}</span>
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<span class="metric-value">{{ inference.value }} {{ inference.unit }}</span>
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</div>
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<div class="metric">
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<span class="metric-label">Search query</span>
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<span class="metric-value">sub-millisecond</span>
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<span class="metric-label">{{ search_latency.label }}</span>
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<span class="metric-value">{{ search_latency.value }} {{ search_latency.unit }}</span>
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</div>
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<div class="metric">
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<span class="metric-label">Index throughput</span>
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<span class="metric-value">~310 MiB/s</span>
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<span class="metric-label">{{ throughput.label }}</span>
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<span class="metric-value">{{ throughput.value }} {{ throughput.unit }}</span>
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</div>
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</div>
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</div>
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<div class="system-card-body">
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<p>No float vectors, no dense embeddings, no GPU allocation. Rust's zero-cost abstractions, deterministic allocation, graphs held in compressed representations.</p>
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<div class="metric">
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<span class="metric-label">Graph footprint</span>
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<span class="metric-value">15-20 MB</span>
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<span class="metric-label">{{ graph_footprint.label }}</span>
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<span class="metric-value">{{ graph_footprint.value }} {{ graph_footprint.unit }}</span>
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</div>
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<div class="metric">
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<span class="metric-label">Index build (100 terms)</span>
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<span class="metric-value">~301 &micro;s</span>
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<span class="metric-label">{{ build_100.label }}</span>
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<span class="metric-value">{{ build_100.value }} {{ build_100.unit }}</span>
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</div>
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<div class="metric">
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<span class="metric-label">Index build (1,000 terms)</span>
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<span class="metric-value">~3.66 ms</span>
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<span class="metric-label">{{ build_1000.label }}</span>
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<span class="metric-value">{{ build_1000.value }} {{ build_1000.unit }}</span>
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</div>
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</div>
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</div>
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<i class="fa-solid fa-chart-line" aria-hidden="true"></i>
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Measured performance
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</div>
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<div class="indicator-row"><div class="indicator-dot blue"></div><span class="indicator-name">Inference</span><span class="indicator-val">5-10 ns</span></div>
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<div class="indicator-row"><div class="indicator-dot blue"></div><span class="indicator-name">Search latency</span><span class="indicator-val">&lt; 1 ms</span></div>
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<div class="indicator-row"><div class="indicator-dot green"></div><span class="indicator-name">Index 100 terms</span><span class="indicator-val">~301 &micro;s</span></div>
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<div class="indicator-row"><div class="indicator-dot green"></div><span class="indicator-name">Index 1,000 terms</span><span class="indicator-val">~3.66 ms</span></div>
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<div class="indicator-row"><div class="indicator-dot green"></div><span class="indicator-name">Graph RAM</span><span class="indicator-val">15-20 MB</span></div>
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<div class="indicator-row"><div class="indicator-dot blue"></div><span class="indicator-name">{{ inference.label }}</span><span class="indicator-val">{{ inference.value }} {{ inference.unit }}</span></div>
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<div class="indicator-row"><div class="indicator-dot blue"></div><span class="indicator-name">Search latency</span><span class="indicator-val">{{ search_latency.value }} {{ search_latency.unit }}</span></div>
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<div class="indicator-row"><div class="indicator-dot green"></div><span class="indicator-name">Index 100 terms</span><span class="indicator-val">{{ build_100.value }} {{ build_100.unit }}</span></div>
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<div class="indicator-row"><div class="indicator-dot green"></div><span class="indicator-name">Index 1,000 terms</span><span class="indicator-val">{{ build_1000.value }} {{ build_1000.unit }}</span></div>
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<div class="indicator-row"><div class="indicator-dot green"></div><span class="indicator-name">Graph RAM</span><span class="indicator-val">{{ graph_footprint.value }} {{ graph_footprint.unit }}</span></div>
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</div>
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<div class="indicator-panel">
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</div>
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<p class="section-footnote">
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Numbers from Criterion benchmarks in <code class="inline-code">crates/terraphim_automata/benches</code> and
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<a class="inline-link" href="https://docs.terraphim.ai">docs.terraphim.ai</a>. Run them yourself: <code class="inline-code">cargo bench</code>.
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Last measured <time datetime="{{ bench.generated_at }}">{{ bench.generated_at | date(format="%Y-%m-%d") }}</time>
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from Criterion benchmarks in <code class="inline-code">crates/terraphim_automata/benches</code>.
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Run them yourself: <code class="inline-code">cargo bench</code>.
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{% if bench.source.workflow_run %}&middot; <a class="inline-link" href="{{ bench.source.workflow_run }}">View CI run</a>{% endif %}
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</p>
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</div>
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</section>

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