1f7ec15f12
feat(lean): add logarithmic viscosity coordinates to NKHodgeFAMM
...
Adds logViscosityRatio, log_viscosity_monotone, and ν_eff_monotone
to Semantics/NKHodgeFAMM.lean section 6b. The adaptive viscosity law
ν_eff = ν₀*(1+μ) is multiplicative in ν₀ and additive in scar density μ;
taking λ = log(ν_eff/ν₀) = log(1+μ) turns the multiplicative feedback into
an additive coordinate. This gives nlinarith a direct handle on viscosity
monotonicity and connects the module to Kritchevsky's "Everything Is
Logarithms" framing (SilverSight CITATION.cff).
Also marks a few pre-existing unused variables with underscores to silence
the linter.
Build: 8316 jobs, 0 errors (lake build Semantics.NKHodgeFAMM)
2026-06-22 01:21:58 -05:00
Brandon Schneider
7eda71868a
refactor(rds): consolidate 14 psycopg2 connect patterns into shared rds_connect module
...
Creates 4-Infrastructure/shim/rds_connect.py with a single connect_rds()
function that resolves connection parameters in priority order:
1. explicit kwargs
2. DATABASE_URL env var (postgres://user:pass@host:port/dbname?sslmode=...)
3. individual RDS_* env vars (RDS_HOST, RDS_PORT, RDS_USER, etc.)
4. built-in defaults
Auth resolution (when password is empty or RDS_IAM=1):
1. RDS_IAM_TOKEN env var (pre-computed)
2. boto3 SDK generate_db_auth_token (preferred)
3. subprocess aws rds generate-db-auth-token (fallback)
4. RDS_PASSWORD env var (non-IAM)
Replaces 8 connection pattern variants across 14 active shims:
- subprocess + RDS_IAM_TOKEN fallback: pist_trace_classify_mcp, joint_classifier,
pist_prove_and_classify, ingest_57_flexures
- boto3 SDK: ene_wiki_body_reingest, ene_migrate_and_tag, dataset_ingest_rds
- subprocess + RDS_PASSWORD: batch_embed_artifacts, sync_wiki_to_rds, seed_flexure_dataset
- RDS_IAM_AUTH: pist_classify
- bashrc parsed: credential_loader
v1.4a benchmark confirmed at 100% after refactor.
2026-05-26 15:09:34 -05:00
Brandon Schneider
bdd9b6284b
feat(pist): pist_trace_classify MCP tool — classify proof traces against 57-theorem flexure library
...
- MCP server: pist-trace-classify (Python, stdio JSON-RPC)
- Accepts trace_path or inline trace_json
- Computes full v2 spectral features from transition matrix
- Queries ene.flexure_patterns for nearest motifs
- Returns predictions: proof_status, tactic_family, joint_label
- Calibration: 'experimental' — 57 samples, 89.5% LOOCV
- Registered as MCP server in opencode.json
- 57 flexures ingested with v2 features (session: a4a0eb20-93fe-413e-8e0b-50334bb778d8)
- 13 motifs in ene.flexure_patterns
2026-05-26 11:23:53 -05:00
