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Project comparison
LLM Engineers Handbook vs Ray
Compare adoption, momentum, maintenance health, and project basics before choosing which tool to evaluate deeper.
LLM Engineers Handbook
The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices
5.0K
Stars
0
Weekly
34/100
Health
Ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
42.5K
Stars
+72
Weekly
79/100
Health
Quick Take
Adoption
Ray has the larger GitHub footprint with 42.5K stars.
Momentum
Ray is currently growing faster at +72 stars this week.
Maintenance
Ray has the stronger health score at 79/100.
Side-by-side Metrics
| Signal | LLM Engineers Handbook | Ray |
|---|---|---|
| GitHub stars | 5.0K | 42.5K |
| Weekly growth | 0 | +72 |
| Health score | 34 | 79 |
| Contributors | 10 | 1.6K |
| Commits per week | 0.2 | 87.6 |
| Open issues | 31 | 3.5K |
| Language | Python | Python |
| License | MIT | Apache-2.0 |
| Last commit | 20d ago | 15h ago |
| Last release | N/A | ray-2.55.1 |
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