add README landing page

README.md

@@ -1,21 +1,48 @@
 ---
-language: [en, ru]
+language:
+  - en
+  - ru
 license: mit
-tags: [ai-text-detection, reproducibility, bilingual, adversarial-robustness, calibration]
+tags:
+  - ai-text-detection
+  - reproducibility
+  - bilingual
+  - adversarial-robustness
+  - calibration
 ---
 
 # ContentOS — Reproducible Bilingual AI-Text-Detection Ensemble
 
-**Pre-print v1.0 (2026-04-27)** · Canonical hosting under Humanswith.ai org.
+**Pre-print v1.0 (2026-04-27)**
+
+This repository contains the open pre-print and supporting artifacts for
+ContentOS, a reproducible English+Russian AI-text-detection ensemble.
 
 ## Authors and affiliation
 
-- **Gregory Shevchenko** — author, founder of Humanswith.ai
+- **Gregory Shevchenko** — author, Humanswith.ai (founder)
 - **Humanswith.ai team** — methodology, calibration, evaluation infrastructure
 
-Personal mirror: https://huggingface.co/datasets/gshevchenko/contentos-preprint
+ContentOS is a Humanswith.ai product. This preprint is published under
+the author's personal HuggingFace account; the supporting code repository
+is maintained under the organization account (see "Code repository" below).
+
+- Author profile: https://huggingface.co/gshevchenko
+- Organization: https://humanswith.ai
+- Contact for collaboration: open a Discussion on this dataset
+
+## Code repository
+
+The evaluation scripts, regression test suite, and atomic-swap deploy
+tooling will be released at:
 
-## Headline numbers (v1.11 production)
+- **`github.com/humanswith-ai/contentos-benchmark`** (public, planned within ~2 weeks following the v1.12 RU recalibration chain)
+
+Until then, the methodology in `REPRODUCIBILITY.md` is sufficient for
+independent re-implementation. For early access, please open a Discussion
+on this dataset.
+
+## Headline numbers (v1.11 production calibration)
 
 | Metric | EN | RU |
 |---|---|---|
@@ -26,31 +53,38 @@ Personal mirror: https://huggingface.co/datasets/gshevchenko/contentos-preprint
 
 ## Files
 
-- `paper.pdf` / `paper.html` / `paper.md` — pre-print (~6,000 words, 9 sections + 5 appendices)
+- `paper.pdf` — full pre-print (~6,000 words, 9 sections + 5 appendices)
+- `paper.html` — self-contained HTML version with embedded figures
+- `paper.md` — source markdown
 - `figures/` — 4 figures (PNG + SVG)
-- `REPRODUCIBILITY.md` — methodology, sufficient for independent re-implementation
+- `REPRODUCIBILITY.md` — open methodology, how to reproduce in 90 minutes
 
-## Code repository
+## Reproducibility
+
+The full methodology and calibration corpus description are documented in
+`REPRODUCIBILITY.md`, which is sufficient for independent re-implementation
+of the ensemble.
 
-The evaluation scripts and regression test suite will be released at
-**`github.com/humanswith-ai/contentos-benchmark`** within ~2 weeks following
-the v1.12 RU recalibration chain. Until then, `REPRODUCIBILITY.md` is the
-canonical methodology reference.
+A public mirror with the evaluation scripts (`eval_ensemble_corpus.py`,
+8 pinned regression tests, atomic-swap deploy with 30-second rollback)
+will be released within ~2 weeks following the v1.12 RU recalibration
+chain. Target reproduction infrastructure: Hetzner CX43 (8 vCPU, no GPU,
+~€14/month) or equivalent.
 
-For early access: open a Discussion on this dataset.
+For early access before the public mirror, please open a discussion on
+this dataset.
 
 ## Cite as
 
 ```bibtex
 @misc{contentos2026,
   title={ContentOS: A Reproducible Bilingual AI-Text-Detection Ensemble with Adversarial Robustness Evaluation},
-  author={Shevchenko, Gregory and Humanswith.ai team},
+  author={Humanswith.ai team},
   year={2026},
-  url={https://huggingface.co/datasets/Humanswith-ai/contentos-preprint},
+  url={https://huggingface.co/datasets/gshevchenko/contentos-preprint},
 }
 ```
 
 ## License
 
-MIT for code, methodology, and corpus aggregation. Underlying data sources
-retain their original licenses (HC3, AINL-Eval-2025, ai-text-detection-pile).
+MIT for code, methodology, and corpus aggregation. Underlying data sources retain their original licenses (HC3, AINL-Eval-2025, ai-text-detection-pile).