AI systems builder and multimedia artist connecting language, executable structure, and human experience.
Evidence edition: September 22, 2026. These are dated findings, not live counters.
| Dimension | Result | Evidence and scope |
|---|---|---|
| Comparative activity | Top 1% on 3 measured axes | Push volume 0.029%, push-active days 0.230%, PR openings 0.800%; upper-tail positions in the observed public dataset, October 2025–June 2026. |
| Annual participation | 35,896 contributions · 318 active days | Trailing-year contribution-calendar snapshot, September 22, 2026. |
| External acceptance | 6 accepted contributions across 6 independent upstream projects | Authored PRs with recorded merge identifiers; the expanded six-project record supersedes the earlier four-project selection. |
| Released music | 5 releases · 63 tracks · 4:10:51 of audio | ETCETER4 catalog; sum of displayed durations, including a remix collection. |
| Narrative corpus | 28 studies · 8 categories · 141 algorithm descriptions | Repository-reported corpus dimensions; not an independent enumeration or execution of all algorithms. |
| Correctness repair | 1,501-row minimum restored where 9 rows passed | Accepted Dagster-project predicate fix, with 1,492 undersized row-count cases reproduced locally. |
| Technical concentration | 88 Python-primary repositories | 168 classified non-fork ecosystem repositories; 13 primary-language labels in the dated manifest. |
The push-volume and active-day comparison populations each contain 17,775,327 accounts; the PR-opening population contains 2,267,625 accounts. These include automated accounts and observed public events only. The three metrics are related, not independent probabilities; they are not multiplied into a composite rank.
Full statistical evidence · Original achievement ledger · Whole-practice atlas
| Upstream contribution | Merged |
|---|---|
| DataDog/guarddog #703 | 2026-06-08 |
| dbt-labs/dbt-mcp #669 | 2026-03-27 |
| jairus-m/dagster-sdlc #22 | 2026-03-27 |
| PrefectHQ/fastmcp #3662 | 2026-03-28 |
| primeinc/github-stars #39 | 2026-05-10 |
| temporalio/sdk-python #1385 | 2026-04-07 |
The recruiter-originated top-1% Python committer finding remains separately attributed in the achievement ledger. None of these newer comparisons erases or relabels that history.
184 canonical AI skills, all passing native structural validation. The inventory excludes 692 SKILL.md paths outside the canonical roots; it does not count generated copies as additional skills.
28 studies · 141 algorithm records · 135 axioms · 246 diagnostic questions. All 141 algorithm records contain the five required descriptive fields. These quantities were enumerated from the pinned source, upgrading the earlier README-reported counts.
Narrative core/CLI/API tests: 324 passed, 2 skipped. Local execution, separately scoped from record completeness; the skipped optional-provider tests and environment are disclosed in the report.
Source census and execution evidence · Audience stat blocks
This new edition complements the dated September 22 findings; it does not relabel activity as commits, corpus completeness as efficacy, or maintained third-party material as sole authorship.
Explore the Distinction Atlas: nine documented areas spanning systems engineering, instruction design, computational representation, literary infrastructure and released music, with audience-specific pathways for applied AI, creative technology, learning and research.
The atlas is a dated selection. For the expanded statistical and six-project upstream record, use the highlights above and STATISTICS.md. Its source registry and method preserve the evidence behind its connections. Original four-project findings remain valid historical selections, not a ceiling on subsequent achievements.
The measurement roadmap defines the next distinct evidence passes: engineering depth, teaching scale, creative reception, and client outcomes. Each pass has a named measurement, primary inputs, deduplication rules, and an explicit completion test. Unmeasured outcomes are not presented as achievements.
LAVREA discovers, measures, and presents evidence-backed distinctions in this body of work. The achievement comes first, with its scope beside it and detailed proof one click away.
Facts live in dated evidence records. Derived comparisons retain their cohort, dates, actual results and calculation. Historical third-party findings retain their attribution. A failed later reconstruction does not erase an earlier finding, and missing context is not disproof.
The front-page statistics are generated from the two pinned evidence records, not copied into another manually maintained table. This prevents a newly documented accomplishment from remaining hidden in an appendix while the public summary keeps advertising an older count.
python scripts/render_highlights.py --check
python scripts/render_highlights.py
python -m unittest discover -s tests -p 'test_public_highlights.py' -v
python scripts/derive_multi_axis_statistics.pyThese commands are offline. They verify source pins, regenerate text, and reproduce arithmetic; they do not fetch new evidence or run a scheduled discovery process. Refreshes add reviewed, dated evidence and update the source pins deliberately, without rewriting earlier observations.
python scripts/build_distinction_atlas.py --check
python scripts/build_distinction_atlas.py --audience applied-ai
python scripts/build_distinction_atlas.py --audience creative-technology
python scripts/build_distinction_atlas.py --audience learning-design
python scripts/build_distinction_atlas.py --audience research
python -m unittest discover -s tests -p 'test_distinction_atlas.py' -vThese reports use the reviewed public evidence in their dated atlas edition. Each pathway connects documented capabilities to a proposed use, demonstration and outcome measure.
python scripts/verify_achievements.py
python -m unittest discover -s tests -p test_achievement_evidence.py -vThe evidence file includes the SQL and replay URLs for independent remeasurement. Field definitions and comparison boundaries are in METHODOLOGY.md.
Generated API profile and cards — separate dated measurement scope
The generated profile and metrics contain the API renderer's own timestamp and visible-repository scope. Those differ from the frozen September 22 ecosystem manifest; do not mix the counts or label the frozen findings as freshly measured.
An issue titled arena: your-login asks CI to compute the bounded API fields for another public account and update LEADERBOARD.md. Ordering that table by activity is not a quality ranking or the population comparison reported above.
Use this repository as a template or fork it, then enable Actions. A personal copy defaults to its repository owner; an organization-owned copy should set LAUREA_LOGIN. The legacy canonical organvm/laurea workflow target remains 4444J99. Optionally add a LAUREA_TOKEN secret for restricted contribution counts and private organization visibility.
pip install -e '.[test]'
laurea run --login YOUR_LOGIN
laurea axes
python -m pytest tests -qLAVREA has zero runtime dependencies. The frozen Anthony-specific records and this showcase remain subject-specific; running API cards for a different account does not transfer these distinctions to that account.
MIT.