--- title: "Failed-ML vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kennethleungty-failed-ml-vs-wangrongsheng-awesome-llm-resources" tools: ["kennethleungty-failed-ml", "wangrongsheng-awesome-llm-resources"] --- # Failed-ML vs awesome-LLM-resources *GraphCanon updated Sep 23, 2026* ## Verdict Pick Failed-ML if failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) reports 751 GitHub stars, 52 forks, and 0 open issues, last pushed Jun 14, 2024. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 9.0k stars, 993 forks, and 40 open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [Failed-ML](/tools/kennethleungty-failed-ml.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Compilation of high-profile real-world examples of failed machine learning projects | Summary of the world's best LLM resources. | | Stars | 751 | 8,968 | | Forks | 52 | 993 | | Open issues | 0 | 40 | | Language | - | - | | Adopt for | Failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems. | awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. | | Persona | - | - | | Runtime | - | - | | License | MIT | The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. | | Categories | Evaluation & Observability | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Failed-ML](/tools/kennethleungty-failed-ml.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 830d | 3d | | Open issues (now) | 0 | 40 | | Stars delta | -2 (30d) | +123 (30d) | | Open issues delta | 0 (30d) | +17 (30d) | | Full report | [trust report](/tools/kennethleungty-failed-ml/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: Failed-ML - **Hosting:** self hosted - **Pricing:** freemium - Open source under MIT license but no additional paid features are mentioned. - **Requirements:** Not a software tool that requires installation. Informational repository intended for reading and learning. - **Adopt for:** Failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems. ## Decision facts: awesome-LLM-resources - **Pricing:** freemium - The repository itself is free to use, but some linked resources may require payment or have associated costs. - **Requirements:** The repository does not specify any technical requirements for accessing its content. - **Adopt for:** awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. - **License detail:** The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. ## Choose when ### Choose Failed-ML if… - License: Failed-ML is MIT, awesome-LLM-resources is Apache-2.0. - Pricing: Open source under MIT license but no additional paid features are mentioned.. - Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning.. - Tags unique to Failed-ML: ai, artificial-intelligence, classification, computer-vision. - When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face. ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, Failed-ML is MIT. - Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.. - Requirements: The repository does not specify any technical requirements for accessing its content.. - Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models. - Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Inference & Serving, LLM Frameworks, Model Training. - When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ## When NOT to use Failed-ML - If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes. - When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers. ## When NOT to use awesome-LLM-resources - If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. - When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ## Common questions ### What is the difference between Failed-ML and awesome-LLM-resources? Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. 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 Failed-ML over awesome-LLM-resources? Choose Failed-ML over awesome-LLM-resources when License: Failed-ML is MIT, awesome-LLM-resources is Apache-2.0; Pricing: Open source under MIT license but no additional paid features are mentioned.; Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning.; Tags unique to Failed-ML: ai, artificial-intelligence, classification, computer-vision; When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face. ### When should I choose awesome-LLM-resources over Failed-ML? Choose awesome-LLM-resources over Failed-ML when License: awesome-LLM-resources is Apache-2.0, Failed-ML is MIT; Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Inference & Serving, LLM Frameworks, Model Training; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ### When should I avoid Failed-ML? If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes. When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers. ### When should I avoid awesome-LLM-resources? If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ### Is Failed-ML or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,968 vs 751). Stars measure visibility, not whether either tool fits your constraints. ### Are Failed-ML and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (Failed-ML: MIT, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to Failed-ML or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([Failed-ML markdown twin](/tools/kennethleungty-failed-ml/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/kennethleungty-failed-ml-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, Failed-ML or awesome-LLM-resources? Failed-ML: 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 Failed-ML and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Failed-ML trust report](/tools/kennethleungty-failed-ml/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kennethleungty-failed-ml`](/api/graphcanon/graph?tool=kennethleungty-failed-ml) - 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/_