LLMoxie: Exploring Agentic AI for Scientific Software Development
📰 ArXiv cs.AI
Learn how LLMoxie's agentic AI platform enables scientific software development with a three-tiered architecture and open-source plugins
Action Steps
- Build a three-tiered architecture for AI-powered software development using LLMoxie's framework
- Configure a LiteLLM/MLflow control plane for authentication and budgeting
- Develop an application augmentation layer for AI coding agents
- Integrate RSE-Plugins ecosystem to encode accumulated RSE knowledge
- Test the Plugin-Agent-Skill hierarchy for scientific Python practices
Who Needs to Know This
Data scientists, software engineers, and researchers can benefit from LLMoxie's capabilities for scientific software development, particularly those working with multi-cloud and on-premise inference
Key Insight
💡 LLMoxie's institutional AI platform enables multi-cloud and on-premise inference for scientific software development
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🚀 LLMoxie: Unlocking agentic AI for scientific software development with a three-tiered architecture and open-source plugins! 🤖
Key Takeaways
Learn how LLMoxie's agentic AI platform enables scientific software development with a three-tiered architecture and open-source plugins
Full Article
Title: LLMoxie: Exploring Agentic AI for Scientific Software Development
Abstract:
arXiv:2607.02703v1 Announce Type: cross Abstract: In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents. Layered on top, an open-source RSE-Plugins ecosystem encodes accumulated RSE knowledge as a Plugin-Agent-Skill hierarchy spanning scientific Python prac
Abstract:
arXiv:2607.02703v1 Announce Type: cross Abstract: In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents. Layered on top, an open-source RSE-Plugins ecosystem encodes accumulated RSE knowledge as a Plugin-Agent-Skill hierarchy spanning scientific Python prac
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