--- title: "best_AI_papers_2022 vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/louisfb01-best-ai-papers-2022-vs-wangrongsheng-awesome-llm-resources" tools: ["louisfb01-best-ai-papers-2022", "wangrongsheng-awesome-llm-resources"] --- # best_AI_papers_2022 vs awesome-LLM-resources *GraphCanon updated Aug 17, 2026* ## Verdict Pick best_AI_papers_2022 if best AI Papers from 2022 offers video explanations and code links for selected research papers; pick awesome-LLM-resources if 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. [best_AI_papers_2022](https://www.louisbouchard.ai) reports 3.2k GitHub stars, 197 forks, and 0 open issues, last pushed Oct 18, 2023. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [best_AI_papers_2022's repository](https://github.com/louisfb01/best_AI_papers_2022) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [best_AI_papers_2022](/tools/louisfb01-best-ai-papers-2022.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | A curated list of breakthrough AI papers from 2022 with video explanations and code links | Summary of the world's best LLM resources. | | Stars | 3,187 | 8,845 | | Forks | 197 | 950 | | Open issues | 0 | 23 | | Language | - | - | | Adopt for | Best AI Papers from 2022 offers video explanations and code links for selected research papers. | 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 | 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_2022](/tools/louisfb01-best-ai-papers-2022.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1016d | 2d | | Open issues (now) | 0 | 23 | | Stars delta | Unknown | +142 (30d) | | Open issues delta | Unknown | -13 (30d) | | Full report | [trust report](/tools/louisfb01-best-ai-papers-2022/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: best_AI_papers_2022 - **Adopt for:** Best AI Papers from 2022 offers video explanations and code links for selected research papers. ## 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_2022 if… - License: best_AI_papers_2022 is MIT, awesome-LLM-resources is Apache-2.0. - Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, machine-learning. - Need to catch up on key innovations in AI from 2022 ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, best_AI_papers_2022 is MIT. - Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models. - Also covers AI Agents, Developer Tools, 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_2022 - Looking for real-time updates or post-2022 research findings - Require detailed technical analysis beyond paper abstracts ## 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_2022 and awesome-LLM-resources? best_AI_papers_2022: A curated list of breakthrough AI papers from 2022 with video explanations and code links. 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_2022 over awesome-LLM-resources? Choose best_AI_papers_2022 over awesome-LLM-resources when License: best_AI_papers_2022 is MIT, awesome-LLM-resources is Apache-2.0; Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, machine-learning; Need to catch up on key innovations in AI from 2022. ### When should I choose awesome-LLM-resources over best_AI_papers_2022? Choose awesome-LLM-resources over best_AI_papers_2022 when License: awesome-LLM-resources is Apache-2.0, best_AI_papers_2022 is MIT; Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Developer Tools, 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_2022? Looking for real-time updates or post-2022 research findings Require detailed technical analysis beyond paper abstracts ### 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_2022 or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 3,187). Stars measure visibility, not whether either tool fits your constraints. ### Are best_AI_papers_2022 and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (best_AI_papers_2022: MIT, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to best_AI_papers_2022 or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [best_AI_papers_2022 alternatives](/tools/louisfb01-best-ai-papers-2022/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([best_AI_papers_2022 markdown twin](/tools/louisfb01-best-ai-papers-2022/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-2022-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_2022 or awesome-LLM-resources? best_AI_papers_2022: 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_2022 and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [best_AI_papers_2022 trust report](/tools/louisfb01-best-ai-papers-2022/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=louisfb01-best-ai-papers-2022`](/api/graphcanon/graph?tool=louisfb01-best-ai-papers-2022) - 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/_