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[GH-ISSUE #1792] StaffML: Improvement suggestions — MCQ mode, coding questions, recent-papers section, and on-device architecture content #27935

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opened 2026-06-20 11:56:56 -05:00 by GiteaMirror · 1 comment
GiteaMirror commented 2026-06-20 11:56:56 -05:00
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Originally created by @profvjreddi on GitHub (May 21, 2026).
Original GitHub issue: https://github.com/harvard-edge/cs249r_book/issues/1792

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.

Originally created by @profvjreddi on GitHub (May 21, 2026). Original GitHub issue: https://github.com/harvard-edge/cs249r_book/issues/1792 ## 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.*
GiteaMirror added the area: staffmltype: improvement labels 2026-06-20 11:56:57 -05:00
GiteaMirror commented 2026-06-20 11:56:58 -05:00
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@Shashank-Tripathi-07 commented on GitHub (May 24, 2026):

Highly agreed sir, we can add a MCQ section in StaffML with multiple psychological plays (to avoid making the MCQs predictable, I've seen this same in JEE Advance 🥲) .

The in-place coding questions can be built for small questions covering technical things like build a quick neural network using Numpy, quite similar to the type of questions deep-ML has https://www.deep-ml.com/problems . Along with the calculation part of the usual questions can be moved to the same type to make the interface standardized for all questions to reduce backend complexity.

Recent papers can be pulled from Alphaxiv, paperswithcode areas and can be quoted with their official link to their papers, reducing manual intervention and work load, allowing full automation for the team.

On-device deployment and Emerging Architecture content can remain manual for you or other technical staff to write blogs or explanatory videos, that will allow the required attention needed for newer things that come up. A lot of noise and signal is there in the new frontiers and your work will be needed.

Can do all of it as you want and can be deployed at whatever deadline you choose sir 🫡

<!-- gh-comment-id:4528955690 --> @Shashank-Tripathi-07 commented on GitHub (May 24, 2026): Highly agreed sir, we can add a MCQ section in StaffML with multiple psychological plays (to avoid making the MCQs predictable, I've seen this same in JEE Advance 🥲) . The in-place coding questions can be built for small questions covering technical things like build a quick neural network using Numpy, quite similar to the type of questions deep-ML has [https://www.deep-ml.com/problems](url) . Along with the calculation part of the usual questions can be moved to the same type to make the interface standardized for all questions to reduce backend complexity. Recent papers can be pulled from Alphaxiv, paperswithcode areas and can be quoted with their official link to their papers, reducing manual intervention and work load, allowing full automation for the team. On-device deployment and Emerging Architecture content can remain manual for you or other technical staff to write blogs or explanatory videos, that will allow the required attention needed for newer things that come up. A lot of noise and signal is there in the new frontiers and your work will be needed. --- Can do all of it as you want and can be deployed at whatever deadline you choose sir 🫡
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