Comparison
best_AI_papers_2022 vs Awesome-LLMOps
Verdict
Pick best_AI_papers_2022 if best AI Papers from 2022 offers video explanations and code links for selected research papers; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · best_AI_papers_2022 alternatives · Awesome-LLMOps alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | best_AI_papers_2022 | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (1016d since push) As of 3w · github_public_v1 | Slowing (91d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 3d · 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_2022
- A curated list of breakthrough AI papers from 2022 with video explanations and code links
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- best_AI_papers_2022
- 3.2k
- Awesome-LLMOps
- 5.9k
Forks
- best_AI_papers_2022
- 197
- Awesome-LLMOps
- 993
Open issues
- best_AI_papers_2022
- 0
- Awesome-LLMOps
- 247
Language
- best_AI_papers_2022
- -
- Awesome-LLMOps
- Shell
Adopt for
- best_AI_papers_2022
- Best AI Papers from 2022 offers video explanations and code links for selected research papers.
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- best_AI_papers_2022
- -
- Awesome-LLMOps
- -
Runtime
- best_AI_papers_2022
- -
- Awesome-LLMOps
- -
License
- best_AI_papers_2022
- MIT
- Awesome-LLMOps
- CC0-1.0
Last pushed
- best_AI_papers_2022
- Oct 18, 2023
- Awesome-LLMOps
- May 21, 2026
Categories
- best_AI_papers_2022
- Evaluation & Observability, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- best_AI_papers_2022
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- best_AI_papers_2022
- 1016d
- Awesome-LLMOps
- 91d
Open issues (now)
- best_AI_papers_2022
- 0
- Awesome-LLMOps
- 247
Stars delta
- best_AI_papers_2022
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- best_AI_papers_2022
- Unknown
- Awesome-LLMOps
- +66 (30d)
Owner type
- best_AI_papers_2022
- User
- Awesome-LLMOps
- Organization
Full report
- best_AI_papers_2022
- Trust report
- Awesome-LLMOps
- Trust report
Choose best_AI_papers_2022 if…
- License: best_AI_papers_2022 is MIT, Awesome-LLMOps is CC0-1.0.
- Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, machine-learning.
- Need to catch up on key innovations in AI from 2022
When NOT to use best_AI_papers_2022
- Looking for real-time updates or post-2022 research findings
- Require detailed technical analysis beyond paper abstracts
Choose Awesome-LLMOps if…
- License: Awesome-LLMOps is CC0-1.0, best_AI_papers_2022 is MIT.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
best_AI_papers_2022 trust report →Awesome-LLMOps trust report →Evaluation & Observability category →Model Training category →Computer Vision category →Data & Retrieval category →Inference & Serving category →LLM Frameworks category →Speech & Audio 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 (louisfb01/best_AI_papers_2022) · observed Jul 31, 2026
- GitHub forks (louisfb01/best_AI_papers_2022) · observed Jul 31, 2026
- Last push (louisfb01/best_AI_papers_2022) · 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 (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: best_AI_papers_2022 3.2k · Awesome-LLMOps 5.9k (synced Jul 31, 2026).
Common questions
- What is the difference between best_AI_papers_2022 and Awesome-LLMOps?
- best_AI_papers_2022: A curated list of breakthrough AI papers from 2022 with video explanations and code links. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose best_AI_papers_2022 over Awesome-LLMOps?
- Choose best_AI_papers_2022 over Awesome-LLMOps when License: best_AI_papers_2022 is MIT, Awesome-LLMOps is CC0-1.0; Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, machine-learning; Need to catch up on key innovations in AI from 2022.
- When should I choose Awesome-LLMOps over best_AI_papers_2022?
- Choose Awesome-LLMOps over best_AI_papers_2022 when License: Awesome-LLMOps is CC0-1.0, best_AI_papers_2022 is MIT; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid best_AI_papers_2022?
- Looking for real-time updates or post-2022 research findings Require detailed technical analysis beyond paper abstracts
- When should I avoid Awesome-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is best_AI_papers_2022 or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 3,187). Stars measure visibility, not whether either tool fits your constraints.
- Are best_AI_papers_2022 and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (best_AI_papers_2022: MIT, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to best_AI_papers_2022 or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at best_AI_papers_2022 alternatives and Awesome-LLMOps alternatives (best_AI_papers_2022 markdown twin, Awesome-LLMOps 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_2022 or Awesome-LLMOps?
- best_AI_papers_2022: Dormant. Awesome-LLMOps: Slowing. 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_2022 and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: best_AI_papers_2022 trust report; Awesome-LLMOps trust report.