b8183404b8
Lays foundation for unified release versioning across MLSysBook publishable artifacts. Pure additions — no existing builds, configs, or sources are touched. scripts/version/release.py Python CLI with helpers: - compute-id: semver bump from previous tag (patch/minor/major/none/explicit) - compute-hash: deterministic SHA-256 over input directories with per-file index - emit-release: writes releases/<project>-<id>/release.json (canonical artifact) - emit-manifest: writes the build-time manifest the deployable bundles Tier A (citable) emits per-file Merkle index; Tier B (lite) is flat. scripts/version/schema.json JSON Schema for release.json. Validates project/tier/release_id/release_hash + Tier A's files[] index. Used by validators in CI. shared/release/release-pill.html Footer snippet — fetches deployable manifest at runtime, renders "v0.1.0 · Apr 26, 2026" pill. Configured per-project via <meta name="release-manifest"> tag. Silent on any fetch failure. shared/release/release-card.html About-page snippet — fuller release-identity card with click-to-copy hash. Same fetch + meta-tag conventions. shared/release/README.md Operator-facing contract documentation. .github/workflows/_release-prepare.yml Reusable workflow_call. Validates confirm == "PUBLISH", computes new_release_id from previous tag + bump (delegates to release.py for canonical math). Outputs new_release_id/new_tag/previous_* for caller's downstream build and finalize steps. Refuses to re-tag existing releases (citation integrity). Caller workflows still own their build commands and tag/release creation; this only standardizes the input shape and version math.
Figure Audit Automation
This directory contains figure_audit.py, a script designed to automate the visual auditing of figures within the ML Systems textbook.
What it does
The script orchestrates a multimodal audit of every figure across Volume 1 and Volume 2 of the textbook. It ensures that the prose, the captions (fig-cap), and the alt-text (fig-alt) precisely match the content of the fully rendered visual images.
- Discovery: It scans the
book/quarto/contents/directory to identify all.qmdchapters containing figures. - Visual Extraction: It resolves the corresponding published HTML URL for each chapter, parses the HTML, and downloads the exact rendered
<img src="...">and inline<svg>visual assets locally. - Auditing: It dispatches parallel worker tasks via the
geminiCLI. The CLI is given explicit instructions to load the local images visually, compare them directly against the.qmdsource text, and evaluate them based on thefigure-audit-brief.mdrubric. - Reporting: It generates strict, granular YAML output files in
.claude/_reviews/Figure Audit/, detailing any misalignments (e.g., the text claims10^4but the chart shows10^3) along with surgically precise.qmdfix recommendations.
How to use it
Run the script from the repository root:
python3 scripts/figure_audit.py
Pre-requisites
- You must have
geminiCLI installed and authenticated on your local machine. - The script assumes the rendered HTML book is available at
https://harvard-edge.github.io/cs249r_book_dev/...(used purely to scrape the final image variants).
Applying the fixes
Once figure_audit.py finishes running, your .claude/_reviews/Figure Audit/ directory will be populated with .yml files containing proposed_fix entries.
These fixes are written as precise, minimal adjustments targeting the .qmd source files. They can either be applied manually by a human reviewing the YAML reports, or parsed programmatically/agentically to apply the diffs across the workspace.