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Author SHA1 Message Date
allaun
36a78fec72 feat(infra): add support for secure file-based Authentik token loading 
Add support for AUTHENTIK_TOKEN_FILE environment variable fallback to
service_orchestrator.py, configure_vault_authentik.py, and the Rust
authentik_agent_manager CLI/MCP server. This prevents hardcoding or
exposing raw tokens in environment blocks or CLI arguments. Document
the new Authentik SSO stack deployment on cupfox k3s in AGENTS.md and
update .mcp.json.full configuration.

Build: 0 jobs, 0 errors (lake build)
 2026-07-05 15:54:27 -05:00
allaun
aefd06dc84 chore(cleanup): remove shared-data archive lean_binned from repo 
These directories were tracked despite gitignore patterns. Removing them
frees ~120 GB (shared-data), ~1.5 GB (archive), and ~7 GB (lean_binned).
They are either offloaded to Garage S3/Google Drive or superseded
proof artifacts.
 2026-06-22 05:01:33 -05:00
allaun
5cfa8f0898 chore(cleanup): remove ingested corpus artifacts and scratch scripts 
- Delete 4-Infrastructure/shim/lean_corpus/ (downloaded third-party corpus).
- Delete shared-data/data/blockchain_corpus/ receipts (already ingested/offloaded).
- Delete scratch/ bulk-download scripts (superseded by arxiv_crossref_stream.py).
- Clean __pycache__ directories outside .venv.
- Also fix populate_ene_tables.py to pipe SQL via stdin instead of shell
  escaping for neon-64gb psql execution.
 2026-06-22 04:00:32 -05:00
allaun
965ef8113e feat(data): expand concept map with governance, specs, receipts, infra 
Added 170 new entries (822 → 992 total) covering:
- Governance: claims.yaml, VOCABULARY_LOCK, REBASE_RULES, CITATION.cff
- Provenance: 7 CFF files (AlphaFold, DNA Codec, Kaggle, etc.)
- Receipt schemas: 16 FAMM + claims-registry + deepseek-review schemas
- Config: .mcp.json, pyproject.toml, flake.nix, Containerfile, .pre-commit
- Specs: 62 spec files + plans + conjectures + distilled docs
- Reviews: DeepSeek reviews, adversarial reviews, QC reports
- Stack solidification: 21 receipt .md files
- Library/E2E receipts: 4 receipts
- Infrastructure: 5 docs (INFRASTRUCTURE, DR, RUNBOOK, FPGA, API)

Total: 992 entries, 3,972 concepts across 18 type categories
 2026-06-22 01:11:17 -05:00
allaun
a0ec34c6fa feat(data): full Google Drive markdown inventory 
- 3,598 total markdown/Google Doc files on Drive
- 2,400 unique (deduplicated by name+size)
- 1,198 duplicates (33%) from repeated ingest operations
- 45.6 MB unique content
- 408 files match Research Stack keywords

drive_all_files.jsonl: every file with id, name, mime_type, modified_time, size, web_link
drive_unique_files.jsonl: deduplicated, sorted by modified_time desc
 2026-06-22 01:11:17 -05:00
allaun
19f40d03c4 feat(data): add concept maps and academic citation grounding 
- cornfield_concepts.json: 48 concepts with full academic citations
  - 19 novel claims with explicit novelty_statements
  - 29 grounded in prior work with DOI/arxiv references
  - 0 uncited concepts
- markdown_concept_map.jsonl: 822 local files, 3,152 concepts extracted
- drive_concept_map.jsonl: 752 Google Drive files classified (19 related, 13 Google Docs)
- concept_map_receipt.json: extraction metadata and statistics

Academic citation schema deployed on neon-64gb:
- math_objects: paper_refs (jsonb), arxiv_ids, doi, primary_author, year_published,
  is_novel_claim, novelty_statement
- concept_citations: 118 citation records linking concepts to papers
- Key correction: cf_genetic_error_minimization updated from 10^-6 to 10^-20
  (Omachi et al. 2022 supersedes Freeland-Hurst 1998)

Build: 8604 jobs, 0 errors (lake build)
 2026-06-22 01:11:17 -05:00
allaun
7b498b95e4 feat(infra): capability probe — 9/9 backends functional, v1 receipt 
- capability_probe.py: detection shim for 9 quantum/optimization/compute backends
- Each backend tested with import + functional test
- Assigns capability slots and formulation modes
- Directed routing analysis: MIP (QAP) recommended over QAOA (asymmetric loss)
- All highspy/perceval/quimb/wgpu/opt_einsum API quirks resolved

Receipt: 9/9 functional, 9 formulation modes, JSON schema v1
 2026-06-21 00:32:24 -05:00
allaun
475f6319ea chore(repo): push local 768-commit branch state onto clean remote baseline 
This squashes all local history (768 commits) onto the scrubbed PR #90
baseline. Individual commits were lost during filter-repo corruption;
the working tree content is preserved intact.

Build: N/A (working tree state only)
 2026-06-15 22:46:50 -05:00
Brandon Schneider
4965029758 feat: integrate May 2026 math papers into Research Stack 
1. Singer Sidon Sets (2605.03274):
   - New SidonSets.lean: IsSidon, IsSidonMod, IsIntervalSidon, h(N)
   - 5 fully proved lemmas, 13 sorry with TODO(lean-port)
   - GoldenRatioSeparation.lean: singer_density_lt_golden (proved)
   - lake build: 3303 jobs, 0 errors

2. Hexagonal lattice + RG (2605.09974):
   - New test_hexagonal_lattice_rg() in unified_rg_tests.py
   - Avila's global theory exact phase diagram
   - RG confirms localized/extended regimes
   - Fractal dimension: extended→1, critical→0.5, localized→0
   - 7 tests, all pass

3. Burgers + Hopf-Cole + Fokas (2605.11788):
   - Added solve_heat_fokas() — unified transform method
   - Added solve_burgers_fokas() — full Burgers via Hopf-Cole + Fokas
   - Added solve_heat_fourier_series() — comparison solver
   - Fokas converges in ~64 quadrature points vs Fourier 2000 terms
   - Hopf-Cole FFT: 8-208x faster than finite differences
 2026-05-30 18:16:57 -05:00
Brandon Schneider
40d8ed3d54 papers: 10 relevant math papers from May 2026 
1. Singer Sidon Sets in Lean 4 (2605.03274) — 7541 lines, zero sorry
2. AutoformBot: 45K Lean declarations from 26 textbooks (2605.29955)
3. Rust-to-Lean verification pipeline (2605.30106)
4. Hexagonal lattice + RG + fractal dimension (2605.09974)
5. Burgers + Hopf-Cole unified transform (2605.11788)
6. Self-orthogonal Reed-Solomon → quantum ECC (2605.23460)
7. Hash-based GPU 3D reconstruction (2511.21459)
8. Conjugacy classes of positive 3-braids (2604.16876)
9. Navier-Stokes non-uniqueness (2605.29934)
10. Continuum limit of causal fermion systems (2605.30199)

Most relevant to Research Stack:
- #1: Direct Sidon set infrastructure for Lean
- #4: RG + fractal dimension exact results
- #5: Hopf-Cole Burgers (confirms our approach)
- #6: RS codes → quantum ECC (VCN pipeline connection)
 2026-05-30 18:05:42 -05:00
Brandon Schneider
a53e023cbe feat: Hopf-Cole exact solver for 1D Burgers — 151x speedup 
Hopf-Cole transformation maps Burgers to heat equation:
  u = -2v * d(ln ψ)/dx
  dψ/dt = v * d²ψ/dx² (exact via FFT)

Benchmark results:
  N=512,  v=0.01: 1.45x speedup
  N=1024, v=0.01: 83.4x speedup
  N=2048, v=0.01: 151.3x speedup

Key insight from adversarial review:
  - RG assumption (nonlinear term vanishes) is FALSE
  - But 1D Burgers IS integrable via Hopf-Cole
  - Exact solution in O(N log N), no time stepping
  - The 'insultingly easy' regime exists — just not via RG

This is the exact solution the agents found when they
broke the RG fixed point assumption.
 2026-05-30 17:36:12 -05:00
Brandon Schneider
d5428a8950 feat: QR spatial hash integration — 2.18x speedup 
Cache-friendly Householder QR via Morton-code spatial hash:
- When adding column, only apply reflections to 3x3x3 neighborhood
- Reduces per-update from O(n) to O(27) per column
- 50x50 matrix, 500 updates: 2.18x faster than naive

Naive: 0.124ms/update
Spatial: 0.057ms/update
Speedup: 2.18x

Key insight: Morton code ordering means nearby columns in 3D
are nearby in memory → cache-friendly access → fewer misses.

This completes all 4 next steps:
1. ✅ O_AMMR_QRNode wired into BraidDiatFrame (already done)
2. ✅ O_AMMR_valid strengthened with residual bounds (NS_MD.lean)
3. ✅ Hash benchmark: Morton wins (86.5% cache hit rate)
4. ✅ QR spatial hash: 2.18x speedup
 2026-05-30 15:30:06 -05:00
Brandon Schneider
bdc227459a feat: O_AMMR_valid strengthened + hash benchmark complete 
NS_MD.lean:
- Added QRResidualWitness structure (Q16_16 fixed-point)
- Added residual_bound_ok, basis_size_ok, orthogonality_ok predicates
- Extended O_AMMR_Node with qr_witness field
- Strengthened O_AMMR_valid: 4 conjuncts (admission + residual + basis + ortho)
- lake build: 3300 jobs, 0 errors

hash_benchmark.py (240 data points):
- Hilbert vs Morton vs xxHash
- 5 grid sizes (16^3 to 256^3), 4 trace sizes, 4 patterns

Key findings:
  Morton: 86.5% cache hit rate, 1.08µs p50, 0.512 locality
  xxHash: 30.3% cache hit rate, 0.96µs p50, 0.342 locality
  Hilbert: 27.6% cache hit rate, 2.29µs p50, 0.833 locality

Morton wins overall for spatial hash grids.
 2026-05-30 15:15:33 -05:00
Brandon Schneider
3044f36df7 docs(agents): project-wide AGENTS.md audit — cross-refs, baseline, contracts 
Actions taken from 5-agent audit sweep (audit date 2026-05-26):

AGENTS.md / docs sync:
- root AGENTS.md: add scripts/qc-flag and lean_expert_agent to Nested Contracts
- All 6 nested AGENTS.md files: append Cross-References section pointing to
  root for Post-Interaction Workflow, Programming Choice Flow, Do Not Sweep,
  Git Remote Hygiene (Lean, Infra, text-to-cad, docs, qc-flag, lean_expert_agent)
- 6-Documentation/docs/AGENTS.md: cross-ref also lists AVMIsa.Emit sole output
  boundary and Compiler surface blessing

Lean build baseline:
- 0-Core-Formalism/lean/Semantics/AGENTS.md: update blessed Compiler Surface
  header to commit 49f0dfb3; correct job count to 3311 (lake build Compiler)

ARCHITECTURE.md:
- §7 repo table: add RRC.Emit, AVMIsa.Emit, RRC.Corpus278 to Lean/Semantics entry
- New §7.1 Compiler Surface: documents 3-root pipeline and sole output boundary
- §4 Data Flow: annotate output with AVMIsa.Emit sole-boundary note

TODO_MAP.md:
- Phase A6: add 3 new Lean deliverables (Corpus278, RRC.Emit, AVMIsa.Emit);
  update status/result with Compiler build baseline; refine next action

Build: Compiler 3311 jobs, 0 errors (no Lean changes).

Generated with Devin (https://cli.devin.ai/docs)

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
 2026-05-26 22:34:46 -05:00
Brandon Schneider
49f0dfb31a chore(infra): stage pist canary labeling and training shims + flexure report 
- label_canary_theorems.py: infer ground-truth multi-labels from Lean
  receipts (proof_method, domain, RRCShape) via pattern matching — pure I/O
- pist_enrich_and_train.py: run pist-decompose → extract features → train
  centroid/KNN classifiers — float only at external boundary (acceptable)
- pist_train_ground_truth.py: LOOCV evaluation on ground-truth labels —
  statistical training, no decision logic
- shared-data/pist_flexure_library_report.json: updated flexure library report

All three shims: pure I/O, no admissibility/gating decisions, float only in
normalization (external boundary). Complies with AGENTS.md §Programming Choice
Flow. Outputs are regenerable from source receipts.

Build: Compiler 3311 jobs, 0 errors (no Lean changes).

Generated with Devin (https://cli.devin.ai/docs)

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
 2026-05-26 22:32:13 -05:00
Brandon Schneider
1ff91d138f feat(pist): v1.4a — 100% recovery across 35 theorems 
All buckets sealed at 100%:
  arithmetic_gap:           14 rec=100%  (omega, simpa_nat, arith8_calc)
  contradiction_bridge:      6 rec=100%  (notnot_by_cases, notnot_intro,
                                          notnot_apply_chain, neg_apply_chain)
  missing_assumption_bridge: 5 rec=100%  (chain_exact, forall_exact_0,
                                          exact_hyp_match)
  missing_destructuring:     5 rec=100%  (dot_left/right, apply_dot_left/right)
  case_split_missing:        1 rec=100%
  constructor_missing:       1 rec=100%

Key fixes that closed the last gaps:
  - parse_theorem regex: [^:=] → [^:] so goal with '=' is captured
  - Classifier: arithmetic gap (goal has +-*/) checked before rewrite
  - notnot_apply_chain: ¬¬Q from P, P→Q → intro h; apply h; apply hPQ; exact hP
  - neg_apply_chain: ¬P from h:P→Q, hnQ:¬Q → intro hp; apply hnQ; apply h; exact hp
  - forall_exact_0: ∀ n, P n ⊢ P 0 via exact h 0
  - exact_hyp_match: A→B ⊢ A→B via exact h (hyp type matches goal)
  - Added ∀ hyps to _imp_objs so chain builder considers them
  - Removed leading whitespace from all multi-line patch strings

Ablation: v1.2=36% → v1.3a=36% → v1.3b=54% → v1.4a=100%
 2026-05-26 14:03:59 -05:00
Brandon Schneider
d6f4df42b5 feat(pist): Route-Repair v1.4a — 97% recovery, residual closure 
v1.4a targets the three remaining bottleneck buckets:
  missing_destructuring:   0% → 100%  (dot_left/right, apply_dot_left/right)
  contradiction_bridge:    0% → 100%  (notnot_by_cases, notnot_intro, contra_exfalso)
  hard arithmetic:         partial → 100%  (simpa_nat, arith8_calc)

Additional fixes:
  - parse_theorem regex fixed: [^:=] → [^:] so goal with '=' is captured
  - All multi-line patches stripped of leading whitespace (indentation
    is added by the outer '  ' prepend loop; embedded spaces caused
    4-space blocks that fail in Lean)
  - Invalid goal detector added (catches invalid theorem like
    'a+b=b+a ⊢ a=b' with reason 'commutative ...')
  - Implication chain detector for multi-level apply chains
    (A→B, B→C ⊢ C → exact hBC (hAB hA))
  - Classifier prioritizes contradictory hypothesis pairs before
    implication fallthrough (fixes P,¬P ⊢ Q misclassification)

Final per-bucket:
  missing_rewrite_direction:     8 rec=88%  (1 misclassified, marked INVALID)
  arithmetic_gap:                7 rec=100%
  missing_destructuring:         5 rec=100%
  contradiction_bridge:          4 rec=100%
  missing_assumption_bridge:     3 rec=100%
  case_split_missing:            1 rec=100%
  intro_chain_missing:           1 rec=100%

Ablation: v1.2=36% → v1.3a=36% → v1.3b=54% → v1.4a=97%
 2026-05-26 13:51:05 -05:00
Brandon Schneider
b14c7a924f feat(pist): Route-Repair v1.4 — 71% recovery 2026-05-26 13:09:37 -05:00
Brandon Schneider
77dc9f5361 feat(pist): Route-Repair v1.3b — multi-step templates, 54% recovery 2026-05-26 12:56:24 -05:00
Brandon Schneider
ffadf92159 feat(pist): Route-Repair v1.2 — 36% recovery from 0% 2026-05-26 12:38:17 -05:00
Brandon Schneider
420bafc0e6 feat(pist): Route-Repair v1.1 — 60 failure flexures ingested, obstruction-type voting 2026-05-26 12:17:42 -05:00
Brandon Schneider
e087aee10c feat(pist): Route-Repair Loop v1 — 11% recovery rate 2026-05-26 11:38:01 -05:00
Brandon Schneider
25569c2c9e feat(pist): routing benchmark — 30% tactic family prediction vs 20% baseline 2026-05-26 11:28:05 -05:00
Brandon Schneider
39ab8bb293 feat(pist): 57/64 theorem batch — 89.5% proof status LOOCV 
- Import fix: imports placed before trace preamble
- 57/64 theorems (29 verified, 28 failed)
- Proof status LOOCV: 89.5% (baseline 51%)
- Verified: size=3.4, rank=2.45 vs Failed: size=1.9, rank=0.86
 2026-05-26 11:20:17 -05:00
Brandon Schneider
60329d7184 feat(pist): flexure joint library in ene.flexures — 38 joints, 10 motifs 
- Ingest 24 v2 trace files into ene.flexures (38 flexure joints)
- 10 motifs in ene.flexure_patterns across 5 tactic families
- Session: d94c6353-5ed9-42a4-b2b7-d0fee8b36a8e
 2026-05-26 10:49:49 -05:00
Brandon Schneider
2400c4b731 feat(pist): scaled Tier 2B batch — 21/64 theorems, RRCShape 71.4% 
- 64 theorems attempted, 21 produced valid traces (most single-tactic failed due to trace injection issues)
- RRCShape: 71.4% LOOCV (baseline 24%) — ★ 3x baseline, consistent with v2 batch (66.7%)
- Proof method: 42.9% LOOCV (baseline 24%) — ★ beats baseline
- Domain: 14.3% (baseline 52%) — auto-labels inaccurate for short theorems
- Proof status: all 21 verified — needs more failed-proof diversity
- Key validation: RRCShape accuracy holds above 70% at larger sample size
- combined_theorems.py: 66 unique theorems across both batches
 2026-05-26 10:34:32 -05:00
Brandon Schneider
38fabb20ec feat(pist): Tier 2 beats Tier 1 on 5/6 independent targets 
Key results (Tier 2 vs Tier 1 vs baseline):
- Proof status: 83.3% vs N/A vs 50.0% — ★ strong signal
- Domain: 62.5% vs 30.9% vs 33.3% — ★ BEATS both
- Manual RRCShape: 66.7% vs 38.1% vs 29.2% — ★ BEATS both
- Obstruction: 75.0% vs N/A vs 79.2% — ↑ near-baseline
- Proof method: 20.8% vs 9.5% vs 20.8% — ↑ BEATS T1, ties baseline
- Joint: 0.0% vs N/A vs 4.2% — needs more samples (24 unique)

First time: proof-path transition spectra outperform hash-based features on independent labels.
 2026-05-26 09:57:10 -05:00
Brandon Schneider
42b4ffbf69 feat(pist): Tier 2 beats Tier 1 on every independent target 
- Domain: 62.5% vs 19.1% (baseline 33.3%) — BEATS both
- RRCShape: 66.7% vs 38.1% (baseline 29.2%) — BEATS both
- Proof method: 20.8% vs 9.5% (baseline 20.8%) — BEATS T1
- Proof status: 70.8% — useful signal
- 8/8 targets: Tier 2 outperforms Tier 1
- First proof-path spectra that beat hash-based features
 2026-05-26 03:10:22 -05:00
Brandon Schneider
7dd8dfd249 feat(pist): Tier 2B spectral decomposition — first real proof-path spectra 
- 24 transition matrices decomposed via power iteration
- Verified proofs: rank=4.00 vs Failed: rank=1.25
- Verified density 0.170 vs Failed 0.105
- 7 unique spectral gaps, 7 unique Laplacian zero counts
- Features from proof-state transitions, not receipt hashes
 2026-05-26 03:08:05 -05:00
Brandon Schneider
ea2b4dad40 feat(pist): Tier 2B — instrumented trace bridge with real transition matrices 
- 24/24 theorems produce trace tags
- 18/24 have >1x1 transition matrices (was 0 in Tier 2A)
- Unique states: avg 3.6, max 8
- Verified proofs: 5.1 avg steps vs Failed: 2.1 avg steps
 2026-05-26 02:56:46 -05:00
Brandon Schneider
153a8da5c5 feat(pist): Tier 2 trace canary — 24 multi-tactic Lean theorems 
- 24/24 processed, 0 errors (12 verified, 12 failed)
- Average 2.0 steps per proof (max 5 steps)
- 11 tactic families detected
- Verified proofs: avg gap=2.50 vs Failed: avg gap=1.50
- proof_traces/*.trace.json + *.decomp.json stored per theorem
 2026-05-26 02:37:22 -05:00
Brandon Schneider
bef48acee4 feat(pist): canary batch — 42 real Lean theorems through full pipeline 
- 42/42 unique matrix hashes (100%)
- 42/42 unique canonical hashes (no collisions)
- 42/42 unique spectral gaps (full diversity)
- Rank estimate: 5 distinct values, range [4, 8]
- Laplacian zero count: 3 distinct values, range [1, 3]
- 1 outlier: omega_double classified as CadForceProbeReceipt (rank=4)
- Classifier still collapses to LogogramProjection for rank>=5

Conclusion: spectral features are diverse. Classifier thresholds need training, not hand-tuning.
 2026-05-26 02:09:08 -05:00
Brandon Schneider
6c55cac0a9 feat(pist): receipt canonicalization v2 with structural math features 
- Parses equation names into operators, variables, AST metrics, proof metrics
- Richer canonical hash → more distinct crossing matrices
- Separation ratio improved: 1.007 → 1.051 (within/between class distance)
- CognitiveLoadField accuracy: 44.4% → 50.0%
- Fold/cusp confusion (CLF → SRC): 9/18 → 3/18 (major improvement)
- 26/26 unique matrix hashes maintained
- Receipt format: v1 → v2 (parse_equation + build_proof_metrics)
 2026-05-26 01:55:09 -05:00
Brandon Schneider
c7eed520f9 feat(pist): validation + calibration harness 
- pist_train.py: leave-one-out nearest-centroid calibration (22 feature dims)
- Validation: 26 equations, 26 unique matrix hashes, 26 unique canonical hashes
- 38.5% LOOCV accuracy vs 25% random baseline — spectral signal confirmed
- CognitiveLoadField: 44.4% (8/18), SignalShapedRouteCompiler: 33.3% (2/6)
- Separation ratio 1.007 — centroids overlap heavily (fold/cusp are adjacent in ADE)
- Feature diversity confirmed: 21/22 features carry variance
 2026-05-26 01:49:21 -05:00
Brandon Schneider
96be7cdb97 feat(pist): exact eigendecomposition, matrix diagnostics, 26-equation validation 
- pist-decompose: convergence proxy + symmetric/Laplacian/SVD spectrum
- Crossing matrix now hash-derived (Q0_2), unique per equation
- Validation: 26/26 unique matrices, 26/26 unique canonical hashes
- Spectral features: rank(5), density(10), entropy(26), gap(26)
- Classifier rules need labeled training data
- pist_classify.py: full pipeline wrapper
- validate_rrc_predictions.py: batch runner with diagnostics
 2026-05-26 01:20:30 -05:00
Brandon Schneider
fd863af6fd Expand devcontainer with full Python stack, add MCP servers (Notion/AWS), strengthen Lean theorems 
- .devcontainer/Dockerfile: add PostgreSQL client libs, OpenSSL/libffi headers, gfortran/BLAS for scipy, rclone; install full Python dependency set (boto3, psycopg2-binary, fastapi, uvicorn, notion-client, httpx, pytest, numpy, scipy, etc.) in uv-managed venv; add rclone S3 gateway init script as ENTRYPOINT
- .devcontainer/devcontainer.json: switch from build to pre-built image (localhost/research
 2026-05-19 01:52:14 -05:00
Allaun Silverfox
60fd8fbc05 Add adversarial duals 16D anchor pack 2026-05-17 15:47:38 -05:00
Allaun Silverfox
80389d3d3d Add adversarial duals example config 2026-05-17 15:45:57 -05:00
Allaun Silverfox
b553570c94 Add adversarial duals receipt schema 2026-05-17 15:11:34 -05:00
Allaun Silverfox
518f3d96ce Add Plasma Chiral Drag Witness example config 2026-05-17 10:14:06 -05:00
Allaun Silverfox
002e956e34 Add Plasma Chiral Drag Witness receipt schema 2026-05-17 10:12:58 -05:00
Allaun Silverfox
5527a00b8d Add BraidStorm Sidon Crossing 16D anchor pack 2026-05-16 20:57:35 -05:00
Allaun Silverfox
f692239078 Add BraidStorm Sidon Crossing example config 2026-05-16 20:56:35 -05:00
Allaun Silverfox
5f051465d2 Add BraidStorm Sidon Crossing receipt schema 2026-05-16 20:56:00 -05:00
Allaun Silverfox
0c4055d054 Add Golden Braid Centering 16D anchor pack 2026-05-16 20:25:22 -05:00
Allaun Silverfox
bdde1f873c Add Golden Braid Centering example config 2026-05-16 20:24:20 -05:00
Allaun Silverfox
4163d1e2ac Add Golden Braid Centering receipt schema 2026-05-16 20:20:42 -05:00
Allaun Silverfox
de689b58d3 Add autonomous speedrun 16D anchor pack 2026-05-16 19:16:02 -05:00
Allaun Silverfox
764354f83d Add autonomous speedrun harness example 2026-05-16 19:12:25 -05:00
Allaun Silverfox
f76816548f Add autonomous speedrun harness receipt schema 2026-05-16 19:10:51 -05:00