Comparison
awesome-LLM-resources vs DS-1000
Verdict
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; pick DS-1000 if the DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc.
Markdown twin · awesome-LLM-resources alternatives · DS-1000 alternatives
GraphCanon updated Oct 8, 2026
6views this month
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Trust & integrity
| Signal | awesome-LLM-resources | DS-1000 |
|---|---|---|
| Maintenance | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 | Dormant (707d since push) As of Oct 8, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 8, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 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
- awesome-LLM-resources
- Summary of the world's best LLM resources.
- DS-1000
- Benchmark and code for evaluating large language models on data science tasks
Stars
- awesome-LLM-resources
- 9.0k
- DS-1000
- 278
Forks
- awesome-LLM-resources
- 993
- DS-1000
- 31
Open issues
- awesome-LLM-resources
- 40
- DS-1000
- 2
Language
- awesome-LLM-resources
- -
- DS-1000
- Python
Adopt for
- 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.
- DS-1000
- The DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc.
Persona
- awesome-LLM-resources
- -
- DS-1000
- -
Runtime
- awesome-LLM-resources
- -
- DS-1000
- -
License
- awesome-LLM-resources
- The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution.
- DS-1000
- CC-BY-SA-4.0
Last pushed
- awesome-LLM-resources
- Sep 14, 2026
- DS-1000
- Oct 30, 2024
Categories
- awesome-LLM-resources
- AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- DS-1000
- Data & Retrieval, Model Training
Trust and health
Maintenance
- awesome-LLM-resources
- Very active (96%)
- DS-1000
- Dormant (18%)
Days since push
- awesome-LLM-resources
- 3d
- DS-1000
- 707d
Open issues (now)
- awesome-LLM-resources
- 40
- DS-1000
- 2
Stars delta
- awesome-LLM-resources
- +123 (30d)
- DS-1000
- +2 (30d)
Open issues delta
- awesome-LLM-resources
- +17 (30d)
- DS-1000
- 0 (30d)
Owner type
- awesome-LLM-resources
- User
- DS-1000
- Organization
Full report
- awesome-LLM-resources
- Trust report
- DS-1000
- Trust report
Choose awesome-LLM-resources if…
- License: awesome-LLM-resources is Apache-2.0, DS-1000 is CC-BY-SA-4.0.
- 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, llama.
- Also covers AI Agents, Computer Vision, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks.
- 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.
Choose DS-1000 if…
- License: DS-1000 is CC-BY-SA-4.0, awesome-LLM-resources is Apache-2.0.
- Tags unique to DS-1000: benchmark, code-generation, data-science, semantic-parsing.
- When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries.
When NOT to use DS-1000
- Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python.
- It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks.
Explore
awesome-LLM-resources trust report →DS-1000 trust report →AI Agents category →Computer Vision category →Data & Retrieval category →Developer Tools category →Evaluation & Observability 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 (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 (xlang-ai/DS-1000) · observed Oct 8, 2026
- GitHub forks (xlang-ai/DS-1000) · observed Oct 8, 2026
- Last push (xlang-ai/DS-1000) · observed Oct 30, 2024
- License file (CC-BY-SA-4.0) · observed Oct 8, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-LLM-resources 9.0k · DS-1000 278 (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-LLM-resources and DS-1000?
- awesome-LLM-resources: Summary of the world's best LLM resources.. DS-1000: Benchmark and code for evaluating large language models on data science tasks. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-LLM-resources over DS-1000?
- Choose awesome-LLM-resources over DS-1000 when License: awesome-LLM-resources is Apache-2.0, DS-1000 is CC-BY-SA-4.0; 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, llama; Also covers AI Agents, Computer Vision, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
- When should I choose DS-1000 over awesome-LLM-resources?
- Choose DS-1000 over awesome-LLM-resources when License: DS-1000 is CC-BY-SA-4.0, awesome-LLM-resources is Apache-2.0; Tags unique to DS-1000: benchmark, code-generation, data-science, semantic-parsing; When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries.
- 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.
- When should I avoid DS-1000?
- Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python. It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks.
- Is awesome-LLM-resources or DS-1000 more popular on GitHub?
- awesome-LLM-resources has more GitHub stars (8,968 vs 278). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-LLM-resources and DS-1000 open source?
- Yes - both are open-source projects on GitHub (awesome-LLM-resources: Apache-2.0, DS-1000: CC-BY-SA-4.0).
- Where can I find alternatives to awesome-LLM-resources or DS-1000?
- GraphCanon lists graph-backed alternatives at awesome-LLM-resources alternatives and DS-1000 alternatives (awesome-LLM-resources markdown twin, DS-1000 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, awesome-LLM-resources or DS-1000?
- awesome-LLM-resources: Very active. DS-1000: 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 awesome-LLM-resources and DS-1000?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-LLM-resources trust report; DS-1000 trust report.