I'm Cole McIntosh, on a mission to push the boundaries of large language models and multi-agent systems. As a contributor to LiteLLM and the creator of the official LangChain Salesforce Integration, I explore innovative AI research and solutions.
Research Focus
- Scalable, production-grade multi-agent architectures
- High-performance LLM optimization at scale
- Vector search and similarity optimization
- chain-of-thought-reranking: an approach to optimizing large language model (LLM) responses by extracting, reranking, and refining their internal chain-of-thought (CoT).
- langchain-salesforce: Langchain integration for Salesforce CRM, providing tools for SOQL queries, object schema inspection, and CRUD operations through Langchain's framework.
- vector-vault: a high-performance vector similarity search engine with LSH (Locality-Sensitive Hashing) optimization, written in Go.
- pycuda-numpy-vector-ops: accelerating NumPy vector operations with PyCUDA in Jupyter notebooks.
- logit-control: control LLM token generation by directly manipulating logits to enforce structured outputs, demonstrated using Hugging Face Transformers and Qwen2.5-0.5B.
- social-media-agent-system: a sophisticated multi-agent system combining specialized AI agents to create engaging social media content, leveraging a research agent for gathering accurate, up-to-date information.
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