Comparison
Failed-ML vs awesome-LLM-resources
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.
Markdown twin · Failed-ML alternatives · awesome-LLM-resources alternatives
GraphCanon updated Sep 23, 2026
8views this month
vs
Trust & integrity
| Signal | Failed-ML | awesome-LLM-resources |
|---|---|---|
| Maintenance | Dormant (830d since push) As of Sep 23, 2026 · github_public_v1 | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 23, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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.
Stars
- Failed-ML
- 751
- awesome-LLM-resources
- 9.0k
Forks
- Failed-ML
- 52
- awesome-LLM-resources
- 993
Open issues
- Failed-ML
- 0
- awesome-LLM-resources
- 40
Language
- Failed-ML
- -
- awesome-LLM-resources
- -
Adopt for
- Failed-ML
- 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
- 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
- Failed-ML
- -
- awesome-LLM-resources
- -
Runtime
- Failed-ML
- -
- awesome-LLM-resources
- -
License
- Failed-ML
- MIT
- awesome-LLM-resources
- The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution.
Last pushed
- Failed-ML
- Jun 14, 2024
- awesome-LLM-resources
- Sep 14, 2026
Categories
- Failed-ML
- Evaluation & Observability
- awesome-LLM-resources
- AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- Failed-ML
- Dormant (18%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- Failed-ML
- 830d
- awesome-LLM-resources
- 3d
Open issues (now)
- Failed-ML
- 0
- awesome-LLM-resources
- 40
Stars delta
- Failed-ML
- -2 (30d)
- awesome-LLM-resources
- +123 (30d)
Open issues delta
- Failed-ML
- 0 (30d)
- awesome-LLM-resources
- +17 (30d)
Full report
- Failed-ML
- Trust report
- awesome-LLM-resources
- Trust report
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.
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.
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 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.
Explore
Failed-ML trust report →awesome-LLM-resources trust report →Evaluation & Observability category →AI Agents category →Computer Vision category →Data & Retrieval category →Developer Tools category →Inference & Serving category →LLM Frameworks category →Model Training category →All comparisonsStack workflowsTrending tools
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (kennethleungty/Failed-ML) · observed Sep 23, 2026
- GitHub forks (kennethleungty/Failed-ML) · observed Sep 23, 2026
- Last push (kennethleungty/Failed-ML) · observed Jun 14, 2024
- License file (MIT) · observed Sep 23, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Sep 14, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: Failed-ML 751 · awesome-LLM-resources 9.0k (synced Sep 23, 2026).
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 and awesome-LLM-resources alternatives (Failed-ML markdown twin, awesome-LLM-resources markdown twin), 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 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; awesome-LLM-resources trust report.