StaffML: Improvement suggestions — MCQ mode, coding questions, recent-papers section, and on-device architecture content

Summary

Feedback from an external user on StaffML (https://mlsysbook.ai/staffml/welcome/), currently described as "similar to LeetCode for MLSys" with concept coverage that is "crystal & clear." The suggestions below are additive enhancements to broaden the platform's usefulness for students and engineers preparing for MLSys interviews.

Suggested improvements

• MCQ answer format — Offer a multiple-choice format alongside the current descriptive answers. Many hiring platforms screen candidates with MCQs, so an MCQ mode would let students and engineers practice in the format they will actually face.
• Hands-on coding questions (Python / C++) — Add coding exercises. Companies frequently ask candidates to implement an optimization in real time during interviews, so live-coding practice would meaningfully increase the platform's value.
• Recent-papers section — Add a small, curated section highlighting recent high-impact papers so users stay current with the field. Example: TurboQuant (Zandieh et al., Google Research; arXiv:2504.19874), which has drawn notable attention.
• On-device deployment & emerging-architecture content — Add Q&A and blog posts on deploying and optimizing newer architectures on-device, e.g. Mixture-of-Experts (MoE) models. Extending beyond core concepts into applied, cutting-edge deployment would broaden the platform's reach.

Notes

The same reviewer also gave positive feedback on the main MLSys book update — the expanded device-deployment and systems-side material was called a valuable, well-documented reference that colleagues working in MLSys had been lacking.

Filed on behalf of feedback received from an external user.