REPOGEO REPORT · LITE
byungsoo-oh/ml-systems-papers
Default branch main · commit b5bdf3ca · scanned 6/16/2026, 10:18:15 PM
GitHub: 622 stars · 45 forks
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface byungsoo-oh/ml-systems-papers, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition the README's opening sentence to clarify its curated nature
Why:
CURRENTPaper list for broad topics in machine learning systems
COPY-PASTE FIXA **curated and organized collection** of essential research papers in machine learning systems, designed to help researchers and engineers quickly find key literature without sifting through generic search results.
- highlicense#2Add a LICENSE file to the repository
Why:
COPY-PASTE FIXCreate a `LICENSE` file in the repository root with the full text of the Creative Commons Attribution 4.0 International License (CC-BY-4.0). A template can be found at `https://creativecommons.org/licenses/by/4.0/legalcode`.
- mediumhomepage#3Set the repository homepage URL
Why:
COPY-PASTE FIXhttps://github.com/byungsoo-oh/ml-systems-papers
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- arXiv.org · recommended 1×
- Google Scholar · recommended 1×
- ACM Digital Library · recommended 1×
- IEEE Xplore Digital Library · recommended 1×
- Microsoft Academic · recommended 1×
- CATEGORY QUERYWhere can I find a comprehensive collection of research papers on machine learning systems?you: not recommendedAI recommended (in order):
- arXiv.org
- Google Scholar
- ACM Digital Library
- IEEE Xplore Digital Library
- Microsoft Academic
- Semantic Scholar
- MLSys Conference Proceedings
AI recommended 7 alternatives but never named byungsoo-oh/ml-systems-papers. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking academic papers on optimizing data pipelines and distributed training for machine learning?you: not recommendedAI recommended (in order):
- TensorFlow
- Horovod
- Ray
- DeepSpeed
- PipeDream
- Apache Spark
- DALI
AI recommended 7 alternatives but never named byungsoo-oh/ml-systems-papers. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of byungsoo-oh/ml-systems-papers?passAI did not name byungsoo-oh/ml-systems-papers — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts byungsoo-oh/ml-systems-papers in production, what risks or prerequisites should they evaluate first?passAI named byungsoo-oh/ml-systems-papers explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo byungsoo-oh/ml-systems-papers solve, and who is the primary audience?passAI did not name byungsoo-oh/ml-systems-papers — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
Drop this badge into the README of byungsoo-oh/ml-systems-papers. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
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byungsoo-oh/ml-systems-papers — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite