Comparison
Hands-On-Large-Language-Models vs awesome-LLM-resources
Verdict
Pick Hands-On-Large-Language-Models if hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵; 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.
Markdown twin · Hands-On-Large-Language-Models alternatives · awesome-LLM-resources alternatives
GraphCanon updated Sep 20, 2026
17views this month
vs
Trust & integrity
| Signal | Hands-On-Large-Language-Models | awesome-LLM-resources |
|---|---|---|
| Maintenance | Slowing (146d since push) As of Sep 18, 2026 · github_public_v1 | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 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
- Hands-On-Large-Language-Models
- Code for the book 'Hands-On Large Language Models'
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- Hands-On-Large-Language-Models
- 29k
- awesome-LLM-resources
- 9.0k
Forks
- Hands-On-Large-Language-Models
- 6.7k
- awesome-LLM-resources
- 993
Open issues
- Hands-On-Large-Language-Models
- 40
- awesome-LLM-resources
- 40
Language
- Hands-On-Large-Language-Models
- Jupyter Notebook
- awesome-LLM-resources
- -
Adopt for
- Hands-On-Large-Language-Models
- Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵
- 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
- Hands-On-Large-Language-Models
- -
- awesome-LLM-resources
- -
Runtime
- Hands-On-Large-Language-Models
- -
- awesome-LLM-resources
- -
License
- Hands-On-Large-Language-Models
- Apache-2.0
- awesome-LLM-resources
- The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution.
Last pushed
- Hands-On-Large-Language-Models
- Apr 24, 2026
- awesome-LLM-resources
- Sep 14, 2026
Categories
- Hands-On-Large-Language-Models
- Developer Tools, LLM Frameworks
- awesome-LLM-resources
- AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- Hands-On-Large-Language-Models
- Slowing (36%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- Hands-On-Large-Language-Models
- 146d
- awesome-LLM-resources
- 3d
Stars delta
- Hands-On-Large-Language-Models
- +910 (30d)
- awesome-LLM-resources
- +123 (30d)
Open issues delta
- Hands-On-Large-Language-Models
- +2 (30d)
- awesome-LLM-resources
- +17 (30d)
Owner type
- Hands-On-Large-Language-Models
- Organization
- awesome-LLM-resources
- User
OSV dependency advisories
- Hands-On-Large-Language-Models
- Published findings
- awesome-LLM-resources
- No lockfile (source not queried)
Full report
- Hands-On-Large-Language-Models
- Trust report
- awesome-LLM-resources
- Trust report
Typed relationship
Hands-On-Large-Language-Models alternative awesome-LLM-resourcesBoth compile comprehensive sets of LLM-related resources, though with slightly different focuses.
Choose Hands-On-Large-Language-Models if…
- Both compile comprehensive sets of LLM-related resources, though with slightly different focuses.
- Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, llms, oreilly, oreilly-books.
- Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'.
When NOT to use Hands-On-Large-Language-Models
- Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning.
- Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM.
- If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit.
Choose awesome-LLM-resources if…
- 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..
- Both compile comprehensive sets of LLM-related resources, though with slightly different focuses.
- Tags unique to awesome-LLM-resources: awesome-list, course, llama, mistral.
- Also covers AI Agents, Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, 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
Hands-On-Large-Language-Models trust report →awesome-LLM-resources trust report →Developer Tools category →LLM Frameworks category →AI Agents category →Computer Vision category →Data & Retrieval category →Evaluation & Observability category →Inference & Serving 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 (HandsOnLLM/Hands-On-Large-Language-Models) · observed Sep 20, 2026
- GitHub forks (HandsOnLLM/Hands-On-Large-Language-Models) · observed Sep 20, 2026
- Last push (HandsOnLLM/Hands-On-Large-Language-Models) · observed Apr 24, 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 (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: Hands-On-Large-Language-Models 29k · awesome-LLM-resources 9.0k (synced Sep 20, 2026).
Common questions
- What is the difference between Hands-On-Large-Language-Models and awesome-LLM-resources?
- Hands-On-Large-Language-Models: Code for the book 'Hands-On Large Language Models'. 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 Hands-On-Large-Language-Models over awesome-LLM-resources?
- Choose Hands-On-Large-Language-Models over awesome-LLM-resources when Both compile comprehensive sets of LLM-related resources, though with slightly different focuses; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, llms, oreilly, oreilly-books; Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'.
- When should I choose awesome-LLM-resources over Hands-On-Large-Language-Models?
- Choose awesome-LLM-resources over Hands-On-Large-Language-Models when 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.; Both compile comprehensive sets of LLM-related resources, though with slightly different focuses; Tags unique to awesome-LLM-resources: awesome-list, course, llama, mistral; Also covers AI Agents, Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, 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 Hands-On-Large-Language-Models?
- Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit.
- 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 Hands-On-Large-Language-Models or awesome-LLM-resources more popular on GitHub?
- Hands-On-Large-Language-Models has more GitHub stars (29,162 vs 8,968). Stars measure visibility, not whether either tool fits your constraints.
- Are Hands-On-Large-Language-Models and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, awesome-LLM-resources: Apache-2.0).
- Where can I find alternatives to Hands-On-Large-Language-Models or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at Hands-On-Large-Language-Models alternatives and awesome-LLM-resources alternatives (Hands-On-Large-Language-Models 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, Hands-On-Large-Language-Models or awesome-LLM-resources?
- Hands-On-Large-Language-Models: Slowing. 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 Hands-On-Large-Language-Models and awesome-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Hands-On-Large-Language-Models trust report; awesome-LLM-resources trust report.