All 4 failed the instrument check (duplicate body text or preempt subsections):
- fn-youtube-feedback-loop: restated the surrounding case study body verbatim
- fn-blue-green-deploy-ml: only added colour labels to "instant rollback" claim already in prose
- fn-ab-testing-ml: preempted the dedicated A/B testing subsection ~200 lines later
- fn-alerting-ml-thresholds: duplicated body text on adaptive thresholds (ML Tether = 1)
- Fix rendering: dimensions (e.g. 224×224) use single math span $N\times M$
- Revert multipliers to N$\times$ / N--M$\times$ per LaTeX convention
- Fix malformed $N\times$ M → $N\times M$ across vol1/vol2
- Add revert_times_multipliers.py (one-off) and fix_times_math.py (dimension-only)
- Update book-prose guidelines in .claude/rules (dimension vs multiplier)
Introduces a set of constants to ensure consistency across the book's code and prose.
These constants include:
- Memory capacities, interconnect bandwidths, model sizes
- Useful measures (GiB, GB, second, etc.)
- Formatting tools
- Deployment tiers (cloud, edge, mobile)
Refactors various sections to utilize centralized constants and formatting functions, improving code maintainability and consistency across the book.
Specifically:
- Replaces hardcoded values with constants defined in `mlsys.constants`.
- Uses the `fmt` function for consistent number formatting.
- Removes redundant calculations and string conversions by leveraging existing functions and constants.
- Introduces a `TransformerScaling` namespace to encapsulate transformer scaling logic.
- Adds invariants (guardrails) to ensure calculations match the book's narrative.
- Refactors MNIST example and moves the inference calculation to MNISTInference.
- Integrates responsible AI principles with lifecycle stages.
This reduces code duplication and ensures a unified representation of key parameters and calculations throughout the book.
- Replace recap-style openers (Having established… we now turn to)
- Replace section meta-openers (This section examines/presents…) with concrete openings
- Remove announcement transitions (We will examine, we now turn to)
- Remove Importantly/Most importantly at sentence start
- Remove In summary, bleeding edge, the lesson is clear
- Replace leverage/utilize (verb) with use; keep high-leverage
- Replace building upon with building on; remove as noted there
- Sample fixes: can't→can we not, it's→it is, So,→Thus, (contractions/sentence-openers)
Adds missing citations and clarifies the text in the appendix on machine learning,
specifically around the energy hierarchy and scaling laws. It also updates the
fault tolerance section to include a reference to the backpropagation paper.
- Add git pull --rebase before push so concurrent comment-triggered runs
don't reject each other (only one of three runs had succeeded for PR 1179).
- Manually add @salmanmkc as code contributor to tinytorch and kits
(labs was already added by the single successful run).
- book-prose: allow compound × for simple products; require × alone only when
followed by word/unit; Unicode × only in fig-alt
- Revert split × back to compound (e.g. $3 \times 10^{-4}$)
- data_engineering: 8× A100 → 8$\times$ A100 (LaTeX in table)
- appendix_dam: Python outputs use LaTeX ×
- hw_acceleration: table dimensions use compound math ($4\times4\times4$)
- benchmarking: fix Python equation string
- Fix Pareto diagram: swap A/C so line has positive slope (latency vs accuracy)
- Add missing Image Classification writeup to Standard Benchmark Tasks
- Treat anomaly_detection as binary classification (np.rand(2))
- Convert MLPerf inputs to Tensors + transpose HWC→CHW for TinyTorch models
Fixesharvard-edge/cs249r_book#1196
The GitHub link (github.com/harvard-edge/TinyTorch/blob/main/paper/paper.pdf)
returns 404. Use arxiv.org/pdf/2601.19107 instead.
Fixesharvard-edge/cs249r_book#1198
Ensures cover images in Vol. 2 chapters fill the available width, improving visual presentation across different screen sizes.
Removes duplicate cover image from the introduction chapter.
Corrects a typographical error in Appendix Machine regarding energy ratios.
Refactors the build process to leverage shared output file resolution logic, ensuring consistency across build and debug commands.
Improves validation by streamlining bibliography handling and adding stricter citation matching.
Updates diagram dependencies and adjusts content for clarity and accuracy.
Figures should have caption only in fig-cap attribute, not duplicated
as trailing text. Removed redundant captions from:
- introduction.qmd: fig-loss-vs-n-d, fig-data-scaling-regimes, fig-scaling-regimes
- sustainable_ai.qmd: fig-datacenter-energy-usage
