--- title: "awesome-automl-papers vs awesome-list-of-awesomes" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-nachimak28-awesome-list-of-awesomes" tools: ["hibayesian-awesome-automl-papers", "nachimak28-awesome-list-of-awesomes"] --- # awesome-automl-papers vs awesome-list-of-awesomes *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search; pick awesome-list-of-awesomes if a directory of curated 'awesome lists' on AI topics like ML, DL, CV. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) reports 4.2k GitHub stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. [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 [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [awesome-list-of-awesomes's repository](https://github.com/Nachimak28/awesome-list-of-awesomes). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers and resources. | A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research | | Stars | 4,155 | 345 | | Forks | 678 | 48 | | Open issues | 2 | 1 | | Language | - | - | | Adopt for | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | A directory of curated 'awesome lists' on AI topics like ML, DL, CV. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability, Model Training | Computer Vision, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | | --- | --- | --- | | Days since push | 784d | 991d | | Open issues (now) | 2 | 1 | | Full report | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/nachimak28-awesome-list-of-awesomes/trust.md) | ## Decision facts: awesome-automl-papers - **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. ## Decision facts: awesome-list-of-awesomes - **Adopt for:** A directory of curated 'awesome lists' on AI topics like ML, DL, CV. ## Choose when ### Choose awesome-automl-papers if… - License: awesome-automl-papers is Apache-2.0, awesome-list-of-awesomes is MIT. - Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search. - When you need a curated list of academic materials to research or learn about AutoML technologies ### Choose awesome-list-of-awesomes if… - License: awesome-list-of-awesomes is MIT, awesome-automl-papers is Apache-2.0. - Tags unique to awesome-list-of-awesomes: computer-vision, data-science, deep-learning, machine-learning. - Also covers Computer Vision. - When you need diverse resources covering specific areas in data science and machine learning ## When NOT to use awesome-automl-papers - If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources - When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ## 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 awesome-automl-papers and awesome-list-of-awesomes? awesome-automl-papers: A curated list of automated machine learning papers and resources.. 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 awesome-automl-papers over awesome-list-of-awesomes? Choose awesome-automl-papers over awesome-list-of-awesomes when License: awesome-automl-papers is Apache-2.0, awesome-list-of-awesomes is MIT; Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies. ### When should I choose awesome-list-of-awesomes over awesome-automl-papers? Choose awesome-list-of-awesomes over awesome-automl-papers when License: awesome-list-of-awesomes is MIT, awesome-automl-papers is Apache-2.0; Tags unique to awesome-list-of-awesomes: computer-vision, data-science, deep-learning, machine-learning; Also covers Computer Vision; When you need diverse resources covering specific areas in data science and machine learning. ### When should I avoid awesome-automl-papers? If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ### 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 awesome-automl-papers or awesome-list-of-awesomes more popular on GitHub? awesome-automl-papers has more GitHub stars (4,155 vs 345). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-automl-papers and awesome-list-of-awesomes open source? Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, awesome-list-of-awesomes: MIT). ### Where can I find alternatives to awesome-automl-papers or awesome-list-of-awesomes? GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [awesome-list-of-awesomes alternatives](/tools/nachimak28-awesome-list-of-awesomes/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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/hibayesian-awesome-automl-papers-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, awesome-automl-papers or awesome-list-of-awesomes? awesome-automl-papers: 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 awesome-automl-papers and awesome-list-of-awesomes? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust); [awesome-list-of-awesomes trust report](/tools/nachimak28-awesome-list-of-awesomes/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers`](/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers) - 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/_