Comparison
Failed-ML vs awesome-RLHF
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.
Markdown twin · Failed-ML alternatives · awesome-RLHF alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | Failed-ML | awesome-RLHF |
|---|---|---|
| Maintenance | Dormant (777d since push) As of 3w · github_public_v1 | Steady (89d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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-RLHF
- A curated list of reinforcement learning with human feedback resources (continually updated)
Stars
- Failed-ML
- 753
- awesome-RLHF
- 4.4k
Forks
- Failed-ML
- 51
- awesome-RLHF
- 258
Open issues
- Failed-ML
- 0
- awesome-RLHF
- 6
Language
- Failed-ML
- -
- awesome-RLHF
- -
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-RLHF
- 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
- Failed-ML
- -
- awesome-RLHF
- -
Runtime
- Failed-ML
- -
- awesome-RLHF
- -
License
- Failed-ML
- MIT
- awesome-RLHF
- Apache-2.0
Last pushed
- Failed-ML
- Jun 14, 2024
- awesome-RLHF
- May 20, 2026
Categories
- Failed-ML
- Evaluation & Observability
- awesome-RLHF
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- Failed-ML
- Dormant (18%)
- awesome-RLHF
- Steady (60%)
Days since push
- Failed-ML
- 777d
- awesome-RLHF
- 89d
Open issues (now)
- Failed-ML
- 0
- awesome-RLHF
- 6
Stars delta
- Failed-ML
- Unknown
- awesome-RLHF
- +9 (30d)
Open issues delta
- Failed-ML
- Unknown
- awesome-RLHF
- 0 (30d)
Owner type
- Failed-ML
- User
- awesome-RLHF
- Organization
Full report
- Failed-ML
- Trust report
- awesome-RLHF
- Trust report
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.
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-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 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.
Explore
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 Jul 31, 2026
- GitHub forks (kennethleungty/Failed-ML) · observed Jul 31, 2026
- Last push (kennethleungty/Failed-ML) · observed Jun 14, 2024
- License file (MIT) · observed Jul 31, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (opendilab/awesome-RLHF) · observed Aug 17, 2026
- GitHub forks (opendilab/awesome-RLHF) · observed Aug 17, 2026
- Last push (opendilab/awesome-RLHF) · observed May 20, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Failed-ML 753 · awesome-RLHF 4.4k (synced Jul 31, 2026).
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 and awesome-RLHF alternatives (Failed-ML markdown twin, awesome-RLHF 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-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; awesome-RLHF trust report.