--- title: "Failed-ML vs awesome-RLHF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kennethleungty-failed-ml-vs-opendilab-awesome-rlhf" tools: ["kennethleungty-failed-ml", "opendilab-awesome-rlhf"] --- # Failed-ML vs awesome-RLHF *GraphCanon updated Aug 17, 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-RLHF if awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) reports 753 GitHub stars, 51 forks, and 0 open issues, last pushed Jun 14, 2024. [awesome-RLHF](https://github.com/opendilab/awesome-RLHF) has 4.4k stars, 258 forks, and 6 open issues, last pushed May 20, 2026. Figures are from public GitHub metadata via [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML) and [awesome-RLHF's repository](https://github.com/opendilab/awesome-RLHF). | | [Failed-ML](/tools/kennethleungty-failed-ml.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Tagline | Compilation of high-profile real-world examples of failed machine learning projects | A curated list of reinforcement learning with human feedback resources (continually updated) | | Stars | 753 | 4,422 | | Forks | 51 | 258 | | Open issues | 0 | 6 | | 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-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Failed-ML](/tools/kennethleungty-failed-ml.md) | [awesome-RLHF](/tools/opendilab-awesome-rlhf.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 777d | 89d | | Open issues (now) | 0 | 6 | | Stars delta | Unknown | +9 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/kennethleungty-failed-ml/trust.md) | [trust report](/tools/opendilab-awesome-rlhf/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-RLHF - **Adopt for:** awesome-RLHF is a curated resource list focusing on reinforcement learning with human feedback (RLHF), which is crucial for refining large language models through interactive training methods. ## Choose when ### Choose Failed-ML if… - License: Failed-ML is MIT, awesome-RLHF 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-RLHF if… - License: awesome-RLHF is Apache-2.0, Failed-ML is MIT. - Tags unique to awesome-RLHF: depth-reinforcement-learning, human-feedback, large language models, reinforcement-learning. - Also covers Model Training. - When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ## 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-RLHF - If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ## Common questions ### What is the difference between Failed-ML and awesome-RLHF? Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. awesome-RLHF: A curated list of reinforcement learning with human feedback resources (continually updated). See the comparison table for live GitHub stats and shared categories. ### When should I choose Failed-ML over awesome-RLHF? Choose Failed-ML over awesome-RLHF when License: Failed-ML is MIT, awesome-RLHF 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-RLHF over Failed-ML? Choose awesome-RLHF over Failed-ML when License: awesome-RLHF is Apache-2.0, Failed-ML is MIT; Tags unique to awesome-RLHF: depth-reinforcement-learning, human-feedback, large language models, reinforcement-learning; Also covers Model Training; When you are specifically interested in the resources that pertain to enhancing reinforcement learning algorithms with human feedback for developing advanced AI systems. ### 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-RLHF? If your focus is exclusively on generic deep-learning or reinforcement-learning resources without the aspect of integrating human feedback into the training process. ### Is Failed-ML or awesome-RLHF more popular on GitHub? awesome-RLHF has more GitHub stars (4,422 vs 753). Stars measure visibility, not whether either tool fits your constraints. ### Are Failed-ML and awesome-RLHF open source? Yes - both are open-source projects on GitHub (Failed-ML: MIT, awesome-RLHF: Apache-2.0). ### Where can I find alternatives to Failed-ML or awesome-RLHF? GraphCanon lists graph-backed alternatives at [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) and [awesome-RLHF alternatives](/tools/opendilab-awesome-rlhf/alternatives) ([Failed-ML markdown twin](/tools/kennethleungty-failed-ml/alternatives.md), [awesome-RLHF markdown twin](/tools/opendilab-awesome-rlhf/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-opendilab-awesome-rlhf.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-RLHF? Failed-ML: Dormant. awesome-RLHF: Steady. 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-RLHF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Failed-ML trust report](/tools/kennethleungty-failed-ml/trust); [awesome-RLHF trust report](/tools/opendilab-awesome-rlhf/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/_