Comparison
best_AI_papers_2021 vs awesome-list-of-awesomes
Verdict
Pick best_AI_papers_2021 if best_AI_papers_2021 offers a curated list of key AI papers published in 2021 with videos, articles, and code examples; pick awesome-list-of-awesomes if a directory of curated 'awesome lists' on AI topics like ML, DL, CV.
Markdown twin · best_AI_papers_2021 alternatives · awesome-list-of-awesomes alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | best_AI_papers_2021 | awesome-list-of-awesomes |
|---|---|---|
| Maintenance | Dormant (1016d since push) As of 2w · github_public_v1 | Dormant (991d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · 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
- best_AI_papers_2021
- A curated list of AI research papers from 2021 with explanations and resources
- awesome-list-of-awesomes
- A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research
Stars
- best_AI_papers_2021
- 2.9k
- awesome-list-of-awesomes
- 345
Forks
- best_AI_papers_2021
- 237
- awesome-list-of-awesomes
- 48
Open issues
- best_AI_papers_2021
- 0
- awesome-list-of-awesomes
- 1
Language
- best_AI_papers_2021
- -
- awesome-list-of-awesomes
- -
Adopt for
- best_AI_papers_2021
- Best_AI_papers_2021 offers a curated list of key AI papers published in 2021 with videos, articles, and code examples.
- awesome-list-of-awesomes
- A directory of curated 'awesome lists' on AI topics like ML, DL, CV.
Persona
- best_AI_papers_2021
- -
- awesome-list-of-awesomes
- -
Runtime
- best_AI_papers_2021
- -
- awesome-list-of-awesomes
- -
License
- best_AI_papers_2021
- The tool is provided under an MIT license, permitting reuse and modification with attribution.
- awesome-list-of-awesomes
- MIT
Last pushed
- best_AI_papers_2021
- Oct 18, 2023
- awesome-list-of-awesomes
- Nov 13, 2023
Categories
- best_AI_papers_2021
- Computer Vision, Data & Retrieval, Model Training
- awesome-list-of-awesomes
- Computer Vision, Evaluation & Observability, Model Training
Trust and health
Days since push
- best_AI_papers_2021
- 1016d
- awesome-list-of-awesomes
- 991d
Open issues (now)
- best_AI_papers_2021
- 0
- awesome-list-of-awesomes
- 1
Full report
- best_AI_papers_2021
- Trust report
- awesome-list-of-awesomes
- Trust report
Choose best_AI_papers_2021 if…
- The code repository does not specify the primary programming language used for the content but contains links to Python-based code samples.
- Tags unique to best_AI_papers_2021: ai, artificial-intelligence, research-paper.
- Also covers Data & Retrieval.
- If you are seeking current insights into AI advancements from 2021, especially on topics such as ethical considerations or governance aspects.
When NOT to use best_AI_papers_2021
- Should not be used if one is looking for historical context beyond AI advances strictly from the period 2021, as it focuses specifically on that time frame.
- Not recommended if comprehensive coverage of AI research topics outside the themes covered in papers published solely in 2021 are needed.
Choose awesome-list-of-awesomes if…
- Tags unique to awesome-list-of-awesomes: data-science, natural-language-processing.
- Also covers Evaluation & Observability.
- When you need diverse resources covering specific areas in data science and machine learning
When NOT to use awesome-list-of-awesomes
- If you require the latest updates, as not all linked lists are actively maintained
- For deeply curated content on new or niche topics not covered
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (louisfb01/best_AI_papers_2021) · observed Jul 31, 2026
- GitHub forks (louisfb01/best_AI_papers_2021) · observed Jul 31, 2026
- Last push (louisfb01/best_AI_papers_2021) · observed Oct 18, 2023
- License file (MIT) · observed Jul 31, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Nachimak28/awesome-list-of-awesomes) · observed Aug 1, 2026
- GitHub forks (Nachimak28/awesome-list-of-awesomes) · observed Aug 1, 2026
- Last push (Nachimak28/awesome-list-of-awesomes) · observed Nov 13, 2023
- License file (MIT) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: best_AI_papers_2021 2.9k · awesome-list-of-awesomes 345 (synced Jul 31, 2026).
Common questions
- What is the difference between best_AI_papers_2021 and awesome-list-of-awesomes?
- best_AI_papers_2021: A curated list of AI research papers from 2021 with explanations and resources. awesome-list-of-awesomes: A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research. See the comparison table for live GitHub stats and shared categories.
- When should I choose best_AI_papers_2021 over awesome-list-of-awesomes?
- Choose best_AI_papers_2021 over awesome-list-of-awesomes when The code repository does not specify the primary programming language used for the content but contains links to Python-based code samples; Tags unique to best_AI_papers_2021: ai, artificial-intelligence, research-paper; Also covers Data & Retrieval; If you are seeking current insights into AI advancements from 2021, especially on topics such as ethical considerations or governance aspects.
- When should I choose awesome-list-of-awesomes over best_AI_papers_2021?
- Choose awesome-list-of-awesomes over best_AI_papers_2021 when Tags unique to awesome-list-of-awesomes: data-science, natural-language-processing; Also covers Evaluation & Observability; When you need diverse resources covering specific areas in data science and machine learning.
- When should I avoid best_AI_papers_2021?
- Should not be used if one is looking for historical context beyond AI advances strictly from the period 2021, as it focuses specifically on that time frame. Not recommended if comprehensive coverage of AI research topics outside the themes covered in papers published solely in 2021 are needed.
- When should I avoid awesome-list-of-awesomes?
- If you require the latest updates, as not all linked lists are actively maintained For deeply curated content on new or niche topics not covered
- Is best_AI_papers_2021 or awesome-list-of-awesomes more popular on GitHub?
- best_AI_papers_2021 has more GitHub stars (2,896 vs 345). Stars measure visibility, not whether either tool fits your constraints.
- Are best_AI_papers_2021 and awesome-list-of-awesomes open source?
- Yes - both are open-source projects on GitHub (best_AI_papers_2021: MIT, awesome-list-of-awesomes: MIT).
- Where can I find alternatives to best_AI_papers_2021 or awesome-list-of-awesomes?
- GraphCanon lists graph-backed alternatives at best_AI_papers_2021 alternatives and awesome-list-of-awesomes alternatives (best_AI_papers_2021 markdown twin, awesome-list-of-awesomes 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, best_AI_papers_2021 or awesome-list-of-awesomes?
- best_AI_papers_2021: Dormant. awesome-list-of-awesomes: Dormant. 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 best_AI_papers_2021 and awesome-list-of-awesomes?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: best_AI_papers_2021 trust report; awesome-list-of-awesomes trust report.