---
title: "what_are_embeddings vs awesome-LLM-resources"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/veekaybee-what-are-embeddings-vs-wangrongsheng-awesome-llm-resources"
tools: ["veekaybee-what-are-embeddings", "wangrongsheng-awesome-llm-resources"]
---

# what_are_embeddings vs awesome-LLM-resources

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick what_are_embeddings when tags unique to what_are_embeddings: embeddings, machine-learning-algorithms, machine-learning, jupyter notebook; pick awesome-LLM-resources when tags unique to awesome-LLM-resources: llama, mistral, llm, course.

[what_are_embeddings](http://vickiboykis.com/what_are_embeddings/) reports 1.1k GitHub stars, 87 forks, and 0 open issues, last pushed Jan 17, 2026. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.7k stars, 924 forks, and 39 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [what_are_embeddings's repository](https://github.com/veekaybee/what_are_embeddings) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources).

| | [what_are_embeddings](/tools/veekaybee-what-are-embeddings.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) |
| --- | --- | --- |
| Tagline | A deep dive into embeddings starting from fundamentals | 🧑🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the world's best LLM resources. |
| Stars | 1,091 | 8,668 |
| Forks | 87 | 924 |
| Open issues | 0 | 39 |
| Language | Jupyter Notebook | - |
| Adopt for | - | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a |
| Persona | - | - |
| Runtime | - | - |
| License | - | Apache-2.0 |
| Categories | Vector Databases | Vector Databases, AI Agents, LLM Frameworks |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [what_are_embeddings](/tools/veekaybee-what-are-embeddings.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Very active (96%) |
| Days since push | 175d | 1d |
| Open issues (now) | 0 | 39 |
| Full report | [trust report](/tools/veekaybee-what-are-embeddings/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) |

## Decision facts: awesome-LLM-resources

- **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a

## Choose when

### Choose what_are_embeddings if…

- Tags unique to what_are_embeddings: embeddings, machine-learning-algorithms, machine-learning, jupyter notebook.
- Leaner open-issue backlog (0).

### Choose awesome-LLM-resources if…

- Tags unique to awesome-LLM-resources: llama, mistral, llm, course.
- Also covers AI Agents, LLM Frameworks.
- - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.

## When NOT to use what_are_embeddings

- Last GitHub push was 176 days ago (slowing maintenance, Jan 17, 2026). Validate activity before betting a new project on what_are_embeddings.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## When NOT to use awesome-LLM-resources

- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage.
- - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.

## Common questions

### What is the difference between what_are_embeddings and awesome-LLM-resources?

what_are_embeddings: A deep dive into embeddings starting from fundamentals. awesome-LLM-resources: 🧑🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.

### When should I choose what_are_embeddings over awesome-LLM-resources?

Choose what_are_embeddings over awesome-LLM-resources when Tags unique to what_are_embeddings: embeddings, machine-learning-algorithms, machine-learning, jupyter notebook; Leaner open-issue backlog (0).

### When should I choose awesome-LLM-resources over what_are_embeddings?

Choose awesome-LLM-resources over what_are_embeddings when Tags unique to awesome-LLM-resources: llama, mistral, llm, course; Also covers AI Agents, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.

### When should I avoid what_are_embeddings?

Last GitHub push was 176 days ago (slowing maintenance, Jan 17, 2026). Validate activity before betting a new project on what_are_embeddings. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### When should I avoid awesome-LLM-resources?

- Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.

### Is what_are_embeddings or awesome-LLM-resources more popular on GitHub?

awesome-LLM-resources has more GitHub stars (8,668 vs 1,091). Stars measure visibility, not whether either tool fits your constraints.

### Are what_are_embeddings and awesome-LLM-resources open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to what_are_embeddings or awesome-LLM-resources?

GraphCanon lists graph-backed alternatives at [what_are_embeddings alternatives](/tools/veekaybee-what-are-embeddings/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([what_are_embeddings markdown twin](/tools/veekaybee-what-are-embeddings/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/alternatives.md)), 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](/compare/veekaybee-what-are-embeddings-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, what_are_embeddings or awesome-LLM-resources?

what_are_embeddings: 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 what_are_embeddings and awesome-LLM-resources?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [what_are_embeddings trust report](/tools/veekaybee-what-are-embeddings/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=veekaybee-what-are-embeddings`](/api/graphcanon/graph?tool=veekaybee-what-are-embeddings)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_