Trust dashboard
Every verifiable claim across the portfolio, in one place. Each entry carries an explicit epistemic status — proved, empirically benchmarked, shipped, or in-progress — and a source you can click through to. Status is per-claim, not per-project: a claim can be shipped (its artifact exists and runs) while the parent project is still in development. Each row shows the parent project's status next to the claim so the relationship is unambiguous. Filter below; what you can't filter into, we don't claim.
32 claims across 10 projects · Aggregated at build time from each project's frontmatter.
Mathematically or formally proved. Typically a deterministic invariant.
Three-runtime byte-symmetric Ed25519 over JCS bytes (Python ↔ Node ↔ browser)
Canonical round-trip reconstruct(parse(canonical_tome(S))) == S — 0.00% drift on every CI run
Measured under a reproducible harness; numbers cite the corpus and command.
Slider fact preservation median 1.000 (p10 0.769 long n=16 / 0.818 short n=8) — measured; same-commit replay receipt pending (bench-hardening T2/T3)
Extraction F1 1.000 on seed_v1; precision 1.000, F1 0.762 on seed_v2
T1 iterated round-trip K=10 stability — STABLE on all three corpora (seed_v1, seed_v2, seed_long_paragraphs)
The named artifact (test, doc, code path) exists in the repo today and is runnable. Per-claim, not per-project — a claim can be shipped while the parent project is still in development.
Storage plane (ClickHouse + DuckDB) with parity tests
tests/acceptance/test_ac_6_storage_*.py
Ingest plane (OTel GenAI + OpenInference normalizer, OTLP server, PII redaction, bounded buffer, body offload at 64 KiB)
tests/acceptance/test_ac_5_wire.py, test_ac_7_ingest.py
SPEC.md v1.0.0 locked; every commit cites a spec section
SPEC.md + commit message convention
MCP-native queryability across observability primitives (FastMCP Streamable HTTP, §9.2 read surface + gated score_observation write)
packages/hfao/mcp_server/ + tests/acceptance/test_ac_9_mcp.py (v0.5.0, SPEC §9)
Closed eval-trace loop (traces ↔ datasets ↔ scores ↔ monitors), with causal attribution, cost rollups, and retention
packages/hfao/compute/ + tests/acceptance/test_ac_8_causal.py, test_ac_8_eval.py, test_ac_8_cost_monitor.py (v0.5.0, SPEC §8)
Stage 2 counterfactual replay — driver-pluggable, Tier-1 frameworks (LangGraph / OpenAI Agents SDK / Claude Agent SDK)
packages/hfao/compute/causal/counterfactual.py + tests/acceptance/test_ac_8_counterfactual.py (post-v0.5.0 main, SPEC §16 Q-20)
Proactive anomaly surfacing (Insight schema + AnomalyEngine) and rule-based insight routing (subscriptions)
packages/hfao/compute/anomaly.py, schema/insights.py, compute/routing.py + tests/acceptance/test_ac_8_insights.py, test_ac_8_routing.py (post-v0.5.0 main, SPEC §16 Q-18/Q-19)
Exact flat vector store with persisted, rebuildable index artifacts (save/load/merge/split), plus an approximate HNSW backend for retrieval at scale
src/core/VectorStore.ts (persisted exact flat artifacts), src/utils/IndexManager.ts (flat + hnswlib-node HNSW, graceful flat fallback), src/core/HierarchicalRetriever.ts (per-trace HNSW ANN above a 2,000-item threshold); tests/core/VectorStore.test.ts
Hierarchical bucket memory with a multi-strategy retriever
src/core/{Bucket,MemoryManager,HierarchicalRetriever}.ts; tests/core/{Bucket,HierarchicalRetriever}.test.ts
Automatic prompt/output categorization with keyword, vector-similarity, and adaptive strategies
src/categorization/PromptCategorizer.ts + strategies/
Multi-level summarization via OpenAI embeddings/completions
src/summarization/SummarizationEngine.ts
Negation-aware retrieval (Widdows orthogonal negation)
src/core/NegationAwareRetrieval.ts; tests/core/NegationAwareRetrieval.test.ts
Pluggable storage tiers — local disk and Google Drive behind a common provider interface
src/providers/{StorageProvider,LocalStorageProvider,GoogleDriveProvider}.ts
Continuous integration on Node 20 and 22 (build + test)
.github/workflows CI; Jest suite under tests/
Render receipt format sum.render_receipt.v1 (Ed25519 / JCS / detached JWS) — verifier in three runtimes
Transform substrate (sum.transform_receipt.v1 + registry: slider / extract / compose) — 20-fixture cross-runtime K-matrix locks accept + reject across Python ↔ Node ↔ browser; T4 source-chain binding, T5 ShareableRender, T6 multi-school extract shipped in v0.7.0
sum verify --explain layered output (sum.verify_explained.v1) — seven per-dimension checks each tagged with epistemic_status; truth-of-content always not_asserted (test-locked invariant)
Compliance validators across six regimes (EU AI Act Art. 12 / GDPR Art. 30 / HIPAA §164.312(b) / ISO 27001 A.8.15 / SOC 2 CC 7.2 / PCI DSS v4.0 Req 10) — sum compliance check emits sum.compliance_report.v1
Stated as a target. Not yet a verifiable claim — included for transparency.
Public deployment URL for the arena UI + live-match viewer, with match history surface reachable to non-author visitors
Body — 'What's Needed For This Entry To Tighten'
Independently auditable compliance posture (HIPAA / SOC 2 / equivalent), or a referenceable pilot deployment with a real clinical partner
Body — 'When This Entry Becomes Substantive'
Deployed behind Cloudflare Access; worker test suite (31 K-matrix + 4 A-matrix files) and engine math/improvement tests pass on local runs — no CI yet, so not independently verifiable
README / PROOF_BOUNDARY.md; workers/api/test; engine/test_*.py (local-only, no .github/workflows)
Public (unauthenticated) demo URL, or read-access source, so the implementation can be inspected directly
Body — 'What's Built vs. What's Next'
Public source repository linked via githubUrl, or a public demo URL exercising the template-adaptation flow
Body — 'What's Needed For This Entry To Tighten'
Independent code/infrastructure audit confirming the no-PHI-persistence guarantee end-to-end against a deployed artifact (the guarantee is currently enforced by a hermetic CI test against the source, but nothing is deployed and no third-party audit exists yet)
Body — Compliance Posture, gating audit step
Published evaluation of CPT E/M coding accuracy against a reference set, methodology and dataset class disclosed (non-patient-identifying)
Body — 'What's Needed For This Entry To Tighten'
Reproducible automated test suite enforcing privacy claims (no high-entropy identifiers, no full URLs / referrers / UA strings in tracked events) end-to-end on the deployed worker, with linkable results — or a referenceable third-party privacy audit
Body — 'When This Entry Becomes Substantive'
Public source repository linked via githubUrl, or a downloadable usable public artifact (release binary, package, or runnable distribution)
Body — 'What's Needed For This Entry To Tighten'