--- title: "obsidian-llm-wiki-local vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kytmanov-obsidian-llm-wiki-local-vs-mlabonne-llm-course" tools: ["kytmanov-obsidian-llm-wiki-local", "mlabonne-llm-course"] --- # obsidian-llm-wiki-local vs llm-course *GraphCanon updated Jul 15, 2026* ## Verdict Pick obsidian-llm-wiki-local when license: obsidian-llm-wiki-local is MIT, llm-course is Apache-2.0; pick llm-course when license: llm-course is Apache-2.0, obsidian-llm-wiki-local is MIT. [obsidian-llm-wiki-local](https://github.com/kytmanov/obsidian-llm-wiki-local) reports 771 GitHub stars, 122 forks, and 2 open issues, last pushed May 26, 2026. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 85 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [obsidian-llm-wiki-local's repository](https://github.com/kytmanov/obsidian-llm-wiki-local) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [obsidian-llm-wiki-local](/tools/kytmanov-obsidian-llm-wiki-local.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Karpathy’s LLM Wiki, 100% local with Ollama. Drop Markdown notes → AI extracts concepts → your Obsidian wiki auto-links and grows. Zero sharing. Your notes stay yours. | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 771 | 80,904 | | Forks | 122 | 9,424 | | Open issues | 2 | 85 | | Language | Python | - | | Adopt for | - | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Data & Retrieval, Inference & Serving, LLM Frameworks | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [obsidian-llm-wiki-local](/tools/kytmanov-obsidian-llm-wiki-local.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 50d | 159d | | Open issues (now) | 2 | 85 | | Full report | [trust report](/tools/kytmanov-obsidian-llm-wiki-local/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose obsidian-llm-wiki-local if… - License: obsidian-llm-wiki-local is MIT, llm-course is Apache-2.0. - Tags unique to obsidian-llm-wiki-local: git-based-wiki, karpathy, knowledge-base, llm-knowledge-base. - Also covers Data & Retrieval. ### Choose llm-course if… - License: llm-course is Apache-2.0, obsidian-llm-wiki-local is MIT. - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning. - Also covers Evaluation & Observability, Model Training. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use obsidian-llm-wiki-local - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## Common questions ### What is the difference between obsidian-llm-wiki-local and llm-course? obsidian-llm-wiki-local: Karpathy’s LLM Wiki, 100% local with Ollama. Drop Markdown notes → AI extracts concepts → your Obsidian wiki auto-links and grows. Zero sharing. Your notes stay yours.. 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 obsidian-llm-wiki-local over llm-course? Choose obsidian-llm-wiki-local over llm-course when License: obsidian-llm-wiki-local is MIT, llm-course is Apache-2.0; Tags unique to obsidian-llm-wiki-local: git-based-wiki, karpathy, knowledge-base, llm-knowledge-base; Also covers Data & Retrieval. ### When should I choose llm-course over obsidian-llm-wiki-local? Choose llm-course over obsidian-llm-wiki-local when License: llm-course is Apache-2.0, obsidian-llm-wiki-local is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning; Also covers Evaluation & Observability, Model Training; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid obsidian-llm-wiki-local? Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### Is obsidian-llm-wiki-local or llm-course more popular on GitHub? llm-course has more GitHub stars (80,904 vs 771). Stars measure visibility, not whether either tool fits your constraints. ### Are obsidian-llm-wiki-local and llm-course open source? Yes - both are open-source projects on GitHub (obsidian-llm-wiki-local: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to obsidian-llm-wiki-local or llm-course? GraphCanon lists graph-backed alternatives at [obsidian-llm-wiki-local alternatives](/tools/kytmanov-obsidian-llm-wiki-local/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([obsidian-llm-wiki-local markdown twin](/tools/kytmanov-obsidian-llm-wiki-local/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/kytmanov-obsidian-llm-wiki-local-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, obsidian-llm-wiki-local or llm-course? obsidian-llm-wiki-local: Steady. 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 obsidian-llm-wiki-local and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [obsidian-llm-wiki-local trust report](/tools/kytmanov-obsidian-llm-wiki-local/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kytmanov-obsidian-llm-wiki-local`](/api/graphcanon/graph?tool=kytmanov-obsidian-llm-wiki-local) - 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/_