--- title: "best_AI_papers_2022 vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/louisfb01-best-ai-papers-2022-vs-mlabonne-llm-course" tools: ["louisfb01-best-ai-papers-2022", "mlabonne-llm-course"] --- # best_AI_papers_2022 vs llm-course *GraphCanon updated Sep 21, 2026* ## Verdict Pick best_AI_papers_2022 if best AI Papers from 2022 offers video explanations and code links for selected research papers; pick llm-course if llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. [best_AI_papers_2022](https://www.louisbouchard.ai) reports 3.2k GitHub stars, 195 forks, and 0 open issues, last pushed Oct 18, 2023. [llm-course](https://mlabonne.github.io/blog/) has 83k stars, 9.7k forks, and 90 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [best_AI_papers_2022's repository](https://github.com/louisfb01/best_AI_papers_2022) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [best_AI_papers_2022](/tools/louisfb01-best-ai-papers-2022.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | A curated list of breakthrough AI papers from 2022 with video explanations and code links | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 3,182 | 83,011 | | Forks | 195 | 9,657 | | Open issues | 0 | 90 | | Language | - | - | | Adopt for | Best AI Papers from 2022 offers video explanations and code links for selected research papers. | llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | 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) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1068d | 224d | | Open issues (now) | 0 | 90 | | Stars delta | -5 (30d) | +1.5k (30d) | | Open issues delta | 0 (30d) | +4 (30d) | | Full report | [trust report](/tools/louisfb01-best-ai-papers-2022/trust.md) | [trust report](/tools/mlabonne-llm-course/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: llm-course - **Adopt for:** llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. ## Choose when ### Choose best_AI_papers_2022 if… - License: best_AI_papers_2022 is MIT, llm-course is Apache-2.0. - Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, neural-network. - Need to catch up on key innovations in AI from 2022 ### Choose llm-course if… - License: llm-course is Apache-2.0, best_AI_papers_2022 is MIT. - Tags unique to llm-course: course, large-language-models, llm, roadmap. - Also covers Developer Tools, Inference & Serving, LLM Frameworks. - Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment. ## 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 llm-course - Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. - Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. - Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path. ## Common questions ### What is the difference between best_AI_papers_2022 and llm-course? best_AI_papers_2022: A curated list of breakthrough AI papers from 2022 with video explanations and code links. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose best_AI_papers_2022 over llm-course? Choose best_AI_papers_2022 over llm-course when License: best_AI_papers_2022 is MIT, llm-course is Apache-2.0; Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, neural-network; Need to catch up on key innovations in AI from 2022. ### When should I choose llm-course over best_AI_papers_2022? Choose llm-course over best_AI_papers_2022 when License: llm-course is Apache-2.0, best_AI_papers_2022 is MIT; Tags unique to llm-course: course, large-language-models, llm, roadmap; Also covers Developer Tools, Inference & Serving, LLM Frameworks; Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment. ### 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 llm-course? Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path. ### Is best_AI_papers_2022 or llm-course more popular on GitHub? llm-course has more GitHub stars (83,011 vs 3,182). Stars measure visibility, not whether either tool fits your constraints. ### Are best_AI_papers_2022 and llm-course open source? Yes - both are open-source projects on GitHub (best_AI_papers_2022: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to best_AI_papers_2022 or llm-course? GraphCanon lists graph-backed alternatives at [best_AI_papers_2022 alternatives](/tools/louisfb01-best-ai-papers-2022/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([best_AI_papers_2022 markdown twin](/tools/louisfb01-best-ai-papers-2022/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/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-mlabonne-llm-course.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 llm-course? best_AI_papers_2022: Dormant. llm-course: Slowing. 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 llm-course? 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); [llm-course trust report](/tools/mlabonne-llm-course/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/_