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Historical Roots 30 EN
All2,400 frozen pre-fix snapshots have accessible root-tree metadata. This extends the previous53-repository sample to every selected snapshot.2,230 contain root license-name candidates;170 require nested-path inspection. This is not completed license-text review or model evaluation.
Following the precommitted plan, recompute experiment29's selection from pinned projections. Verify2,400 entries and membership SHA-256 fa58c4e51ae307963eb587813e49adf70d987dcb4893f6ee1aa6050bc6b80a5a, then query each exact base_commit root tree. Retain every selected request, including the53 long inputs.
| Item | Result |
|---|---|
| Target / accessible snapshots | 2,400 / 2,400 |
| Unavailable snapshots | 0 |
| Snapshots with root license candidates | 2,230 |
| Snapshots needing nested-path inspection | 170 |
| Metadata response bytes | 14,752,218 |
| Deduplicated candidate objects | 143 |
The170 are163 matplotlib/matplotlib snapshots and7 tokio-rs/axum snapshots. Further inspection of previously sampled roots shows a LICENSE directory in Matplotlib and a multi-package layout in Axum. Absence of a matching root file does not mean absence of a license. Nested lookup is a subsequent step.
The LICENSE/COPYING/NOTICE filename rule is unchanged from experiment29.143 counts distinct (repository, path, blob ID) tuples, not143 license types. Object references and occurrence counts stay in a private local manifest. The public repository aggregates contain no individual task or commit IDs.
Two workers own separate input/result slots. Bound requests to30 seconds, responses to2MiB and error capture to4KiB; space each worker's request starts by at least250ms. Existing GitHub CLI authentication handles credentials. Credentials and raw errors never enter public results.
Only validated successful responses enter the cache through a temporary file and atomic rename. Reject incomplete trees, invalid object types/modes/IDs, duplicate paths and oversized responses. Do not replace failures with easier tasks. Reuse the original53 sample responses. This cache is source metadata, not license clearance.
An --offline replay makes no GitHub requests. Missing cache entries remain unavailable observations. All2,400 entries were present here; the aggregate and private candidate manifest are byte-identical to the collection run.
go run ./cmd/riido-rootcoverage --out .cache/historical-roots-new
# After collection, reproduce into a new directory without network requests.
go run ./cmd/riido-rootcoverage --offline --out .cache/historical-roots-offline-newPinned projections are required. Output directories must be new. Keep candidates.json and the source cache local; do not upload them to Git/HF.
Injected-response tests cover cache reuse, failure non-persistence, malformed identities and bounded output. Test candidate deduplication and per-repository success/failure/missing-candidate accounting. Full Go race tests, vet and formatting passed; the original53-sample report still reproduces. An actual2,400-entry offline run under the race detector also matches the original aggregate.
One cache-only M4 Pro CPU replay took0.93s wall,0.44s user,0.10s sys and32,440,320 bytes peak RSS (about30.9MiB). This does not measure the entire initial network collection, model inference or GPU costs.14.8MB of metadata does not estimate full source or container storage.
The result establishes root-list access at every selected commit. No code or license contents were downloaded and recursive catalogs were not checked. Candidate files still require text and third-party-condition review. LicensesReviewed and ProductionReady remainfalse.
Next inspect nested license paths for170 snapshots, verify deduplicated candidate contents and document source conditions. Long-input handling and actual2,400-task evaluation remain pending. No training, inference, new HF release, default activation or use of existing final reserves occurred.
Follow-up: experiment 31 resolves the 170 nested layouts and verifies candidate text for all 2,400 snapshots. Complete legal review and model evaluation remain separate.
riidolaya is experimental. No demonstrated Codex cost savings; repository selection is preview-only.
riidolaya는 실험 단계입니다. Codex 절감은 미입증이며 저장소 선택은 preview입니다.
English README · 한국어 README · Versioned Wiki sources / Wiki 원본
이전 연구 안내 펼치기 / Earlier research
- 실제 이어 학습 V3 · 한국어 · Actual V3 training · English
- 진행·완료 보고·질문100건 결과 KO · 100-case results EN
- 라벨·이모지·상태 힌트 KO · Content/state hints EN
- 개발 진행·완료 보고·질문 pilot KO · Development pilot EN
- 작은 검증 기록 KO · Compact evidence EN
- 현재 개발 자료 35개·다음 학습 조건 · 35-row data and next training gates
- 개발용 학습 자료 2개 · Two qualified training requests
- 첫 두 과제 실제 관측 · First two actual observations
- 전체 사용법 KO · Full usage EN
- 작은 동작 힌트 KO · Small hints EN
- 설명 보강 비교76 · Caption diagnostic76
- Go 압축 가중치78 · Packed Go weights78
- 공개 함수 동작 검증 · Public function verification
- 다음 학습 자료 준비 · Training data preparation
- 동작에서 학습 정답·실제 학습79 · Grounded labels and actual fit79
- 소스 동작 관찰 · Source observations
- 저장 크기와 RAM · Storage and RAM
- 라이선스 · Licensing
- 시점별 기록 원문 / Chronological history
- 전체 연구 링크 / Full research index
- 문서 목록 / Documentation