--- title: "best_AI_papers_2023 vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/louisfb01-best-ai-papers-2023-vs-wangrongsheng-awesome-llm-resources" tools: ["louisfb01-best-ai-papers-2023", "wangrongsheng-awesome-llm-resources"] --- # best_AI_papers_2023 vs awesome-LLM-resources *GraphCanon updated Jul 12, 2026* ## Verdict Pick best_AI_papers_2023 when license: best_AI_papers_2023 is MIT, awesome-LLM-resources is Apache-2.0; pick awesome-LLM-resources when license: awesome-LLM-resources is Apache-2.0, best_AI_papers_2023 is MIT. [best_AI_papers_2023](https://github.com/louisfb01/best_AI_papers_2023) reports 251 GitHub stars, 23 forks, and 0 open issues, last pushed Dec 24, 2023. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.7k stars, 924 forks, and 39 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [best_AI_papers_2023's repository](https://github.com/louisfb01/best_AI_papers_2023) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [best_AI_papers_2023](/tools/louisfb01-best-ai-papers-2023.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | A curated list of the latest breakthroughs in AI (in 2023) by release date with a clear video explanation, link to a more in-depth article, and code. | Summary of the world's best LLM resources. | | Stars | 251 | 8,668 | | Forks | 23 | 924 | | Open issues | 0 | 39 | | Language | - | - | | Adopt for | - | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Computer Vision, Developer Tools, Evaluation & Observability, Model Training | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [best_AI_papers_2023](/tools/louisfb01-best-ai-papers-2023.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 929d | 1d | | Open issues (now) | 0 | 39 | | Full report | [trust report](/tools/louisfb01-best-ai-papers-2023/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### Choose best_AI_papers_2023 if… - License: best_AI_papers_2023 is MIT, awesome-LLM-resources is Apache-2.0. - Tags unique to best_AI_papers_2023: ai, artificial-intelligence, computer-vision, machine-learning. - Also covers Computer Vision. ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, best_AI_papers_2023 is MIT. - Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models. - Also covers AI Agents, Inference & Serving, LLM Frameworks. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## When NOT to use best_AI_papers_2023 - Last GitHub push was 930 days ago (dormant maintenance, Dec 24, 2023). Validate activity before betting a new project on best_AI_papers_2023. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use awesome-LLM-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between best_AI_papers_2023 and awesome-LLM-resources? best_AI_papers_2023: A curated list of the latest breakthroughs in AI (in 2023) by release date with a clear video explanation, link to a more in-depth article, and code.. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose best_AI_papers_2023 over awesome-LLM-resources? Choose best_AI_papers_2023 over awesome-LLM-resources when License: best_AI_papers_2023 is MIT, awesome-LLM-resources is Apache-2.0; Tags unique to best_AI_papers_2023: ai, artificial-intelligence, computer-vision, machine-learning; Also covers Computer Vision. ### When should I choose awesome-LLM-resources over best_AI_papers_2023? Choose awesome-LLM-resources over best_AI_papers_2023 when License: awesome-LLM-resources is Apache-2.0, best_AI_papers_2023 is MIT; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers AI Agents, Inference & Serving, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### When should I avoid best_AI_papers_2023? Last GitHub push was 930 days ago (dormant maintenance, Dec 24, 2023). Validate activity before betting a new project on best_AI_papers_2023. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid awesome-LLM-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is best_AI_papers_2023 or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,668 vs 251). Stars measure visibility, not whether either tool fits your constraints. ### Are best_AI_papers_2023 and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (best_AI_papers_2023: MIT, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to best_AI_papers_2023 or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [best_AI_papers_2023 alternatives](/tools/louisfb01-best-ai-papers-2023/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([best_AI_papers_2023 markdown twin](/tools/louisfb01-best-ai-papers-2023/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/louisfb01-best-ai-papers-2023-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, best_AI_papers_2023 or awesome-LLM-resources? best_AI_papers_2023: Dormant. awesome-LLM-resources: Very active. 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 best_AI_papers_2023 and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [best_AI_papers_2023 trust report](/tools/louisfb01-best-ai-papers-2023/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=louisfb01-best-ai-papers-2023`](/api/graphcanon/graph?tool=louisfb01-best-ai-papers-2023) - 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/_