--- title: "ml-surveys vs awesome-list-of-awesomes" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eugeneyan-ml-surveys-vs-nachimak28-awesome-list-of-awesomes" tools: ["eugeneyan-ml-surveys", "nachimak28-awesome-list-of-awesomes"] --- # ml-surveys vs awesome-list-of-awesomes *GraphCanon updated Aug 22, 2026* ## Verdict Pick ml-surveys if ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems; pick awesome-list-of-awesomes if a directory of curated 'awesome lists' on AI topics like ML, DL, CV. [ml-surveys](https://github.com/eugeneyan/ml-surveys) reports 2.9k GitHub stars, 292 forks, and 2 open issues, last pushed Mar 17, 2023. [awesome-list-of-awesomes](https://github.com/Nachimak28/awesome-list-of-awesomes) has 345 stars, 48 forks, and 1 open issues, last pushed Nov 13, 2023. Figures are from public GitHub metadata via [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys) and [awesome-list-of-awesomes's repository](https://github.com/Nachimak28/awesome-list-of-awesomes). | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | | --- | --- | --- | | Tagline | Survey papers summarizing advances in various AI domains | A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research | | Stars | 2,902 | 345 | | Forks | 292 | 48 | | Open issues | 2 | 1 | | Language | - | - | | Adopt for | ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems. | A directory of curated 'awesome lists' on AI topics like ML, DL, CV. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Computer Vision, Evaluation & Observability, Model Training | Computer Vision, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | | --- | --- | --- | | Days since push | 1254d | 991d | | Open issues (now) | 2 | 1 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/eugeneyan-ml-surveys/trust.md) | [trust report](/tools/nachimak28-awesome-list-of-awesomes/trust.md) | ## Decision facts: ml-surveys - **Adopt for:** ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems. ## Decision facts: awesome-list-of-awesomes - **Adopt for:** A directory of curated 'awesome lists' on AI topics like ML, DL, CV. ## Choose when ### Choose ml-surveys if… - Tags unique to ml-surveys: embeddings, nlp, recommender-system, reinforcement-learning. - When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning - More GitHub stars (2.9k vs 345) - visibility, not fit. ### Choose awesome-list-of-awesomes if… - Tags unique to awesome-list-of-awesomes: data-science, natural-language-processing. - When you need diverse resources covering specific areas in data science and machine learning - More recently updated (last pushed Nov 13, 2023). ## When NOT to use ml-surveys - If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary - In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches ## When NOT to use awesome-list-of-awesomes - If you require the latest updates, as not all linked lists are actively maintained - For deeply curated content on new or niche topics not covered ## Common questions ### What is the difference between ml-surveys and awesome-list-of-awesomes? ml-surveys: Survey papers summarizing advances in various AI domains. awesome-list-of-awesomes: A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research. See the comparison table for live GitHub stats and shared categories. ### When should I choose ml-surveys over awesome-list-of-awesomes? Choose ml-surveys over awesome-list-of-awesomes when Tags unique to ml-surveys: embeddings, nlp, recommender-system, reinforcement-learning; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning; More GitHub stars (2.9k vs 345) - visibility, not fit. ### When should I choose awesome-list-of-awesomes over ml-surveys? Choose awesome-list-of-awesomes over ml-surveys when Tags unique to awesome-list-of-awesomes: data-science, natural-language-processing; When you need diverse resources covering specific areas in data science and machine learning; More recently updated (last pushed Nov 13, 2023). ### When should I avoid ml-surveys? If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches ### When should I avoid awesome-list-of-awesomes? If you require the latest updates, as not all linked lists are actively maintained For deeply curated content on new or niche topics not covered ### Is ml-surveys or awesome-list-of-awesomes more popular on GitHub? ml-surveys has more GitHub stars (2,902 vs 345). Stars measure visibility, not whether either tool fits your constraints. ### Are ml-surveys and awesome-list-of-awesomes open source? Yes - both are open-source projects on GitHub (ml-surveys: MIT, awesome-list-of-awesomes: MIT). ### Where can I find alternatives to ml-surveys or awesome-list-of-awesomes? GraphCanon lists graph-backed alternatives at [ml-surveys alternatives](/tools/eugeneyan-ml-surveys/alternatives) and [awesome-list-of-awesomes alternatives](/tools/nachimak28-awesome-list-of-awesomes/alternatives) ([ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/alternatives.md), [awesome-list-of-awesomes markdown twin](/tools/nachimak28-awesome-list-of-awesomes/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/eugeneyan-ml-surveys-vs-nachimak28-awesome-list-of-awesomes.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ml-surveys or awesome-list-of-awesomes? ml-surveys: Dormant. awesome-list-of-awesomes: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for ml-surveys and awesome-list-of-awesomes? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ml-surveys trust report](/tools/eugeneyan-ml-surveys/trust); [awesome-list-of-awesomes trust report](/tools/nachimak28-awesome-list-of-awesomes/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eugeneyan-ml-surveys`](/api/graphcanon/graph?tool=eugeneyan-ml-surveys) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_