--- title: "llm-course vs awesome-hacking-lists" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-taielab-awesome-hacking-lists" tools: ["mlabonne-llm-course", "taielab-awesome-hacking-lists"] --- # llm-course vs awesome-hacking-lists *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-course when requirements: Course materials are available in Colab notebooks; access requires a Google account; pick awesome-hacking-lists when tags unique to awesome-hacking-lists: agents, ai, aiagent, awesome-list. [llm-course](https://mlabonne.github.io/blog/) reports 81k GitHub stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. [awesome-hacking-lists](https://github.com/taielab/awesome-hacking-lists) has 1.4k stars, 264 forks, and 2 open issues, last pushed Dec 4, 2025. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [awesome-hacking-lists's repository](https://github.com/taielab/awesome-hacking-lists). | | [llm-course](/tools/mlabonne-llm-course.md) | [awesome-hacking-lists](/tools/taielab-awesome-hacking-lists.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | A curated collection of top-tier penetration testing tools and productivity utilities across multiple domains. Join us to explore, contribute, and enhance your hacking toolkit! | | Stars | 80,839 | 1,362 | | Forks | 9,421 | 264 | | Open issues | 84 | 2 | | Language | - | - | | 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 | Apache-2.0 | - | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | AI Agents, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [awesome-hacking-lists](/tools/taielab-awesome-hacking-lists.md) | | --- | --- | --- | | Days since push | 155d | 219d | | Open issues (now) | 84 | 2 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/taielab-awesome-hacking-lists/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 llm-course if… - 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 ### Choose awesome-hacking-lists if… - Tags unique to awesome-hacking-lists: agents, ai, aiagent, awesome-list. - Also covers AI Agents. - Leaner open-issue backlog (2). ## 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 ## When NOT to use awesome-hacking-lists - Last GitHub push was 220 days ago (slowing maintenance, Dec 4, 2025). Validate activity before betting a new project on awesome-hacking-lists. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - 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. ## Common questions ### What is the difference between llm-course and awesome-hacking-lists? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. awesome-hacking-lists: A curated collection of top-tier penetration testing tools and productivity utilities across multiple domains. Join us to explore, contribute, and enhance your hacking toolkit!. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over awesome-hacking-lists? Choose llm-course over awesome-hacking-lists when 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 choose awesome-hacking-lists over llm-course? Choose awesome-hacking-lists over llm-course when Tags unique to awesome-hacking-lists: agents, ai, aiagent, awesome-list; Also covers AI Agents; Leaner open-issue backlog (2). ### 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 ### When should I avoid awesome-hacking-lists? Last GitHub push was 220 days ago (slowing maintenance, Dec 4, 2025). Validate activity before betting a new project on awesome-hacking-lists. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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. ### Is llm-course or awesome-hacking-lists more popular on GitHub? llm-course has more GitHub stars (80,839 vs 1,362). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and awesome-hacking-lists open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm-course or awesome-hacking-lists? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [awesome-hacking-lists alternatives](/tools/taielab-awesome-hacking-lists/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [awesome-hacking-lists markdown twin](/tools/taielab-awesome-hacking-lists/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/mlabonne-llm-course-vs-taielab-awesome-hacking-lists.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-course or awesome-hacking-lists? llm-course: Slowing. awesome-hacking-lists: 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 llm-course and awesome-hacking-lists? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [awesome-hacking-lists trust report](/tools/taielab-awesome-hacking-lists/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mlabonne-llm-course`](/api/graphcanon/graph?tool=mlabonne-llm-course) - 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/_