If there’s one thing that characterizes driving in any major city, it’s the constant stop-and-go as traffic lights change and as cars and trucks merge and separate and turn and park. This constant stopping and starting is extremely inefficient, driving…
What if data could help predict a patient’s prognosis, streamline hospital operations, or optimize human resources in medicine? A book fresh off the shelves, “The Analytics Edge in Healthcare,” shows that this is already happening, and demonstrates how to scale it. …
The Model Communication Protocol (MCP) is an emerging open standard that allows AI agents to interact with external services through a uniform interface. Instead of writing custom integrations for each API, an MCP server exposes a set of tools that…
Building machine learning models is an undertaking which is now within everyone’s reach. Source link
LLMs built on Transformer architectures face significant scaling challenges due to their quadratic complexity in sequence length when processing long-context inputs. Methods like Linear Attention models, State Space Models like Mamba, Linear RNNs like DeltaNet, and RWKV solve this problem.…
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities across diverse tasks, with Reinforcement Learning (RL) serving as a crucial mechanism for refining their deep thinking abilities. While RL techniques have shown particular success in mathematical reasoning and coding domains…
Before MCP, LLMs relied on ad-hoc, model-specific integrations to access external tools. Approaches like ReAct interleave chain-of-thought reasoning with explicit function calls, while Toolformer trains the model to learn when and how to invoke APIs. Libraries such as LangChain and…
This post is divided into three parts; they are: • Query Expansion and Reformulation • Hybrid Retrieval: Dense and Sparse Methods • Multi-Stage Retrieval with Re-ranking One of the challenges in RAG systems is that the user’s query might not…
RAG has proven effective in enhancing the factual accuracy of LLMs by grounding their outputs in external, relevant information. However, most existing RAG implementations are limited to text-based corpora, which restricts their applicability to real-world scenarios where queries may require…
In today’s fast-paced financial landscape, leveraging specialized AI agents to handle discrete aspects of analysis is key to delivering timely, accurate insights. Agno’s lightweight, model-agnostic framework empowers developers to rapidly spin up purpose-built agents, such as our Finance Agent for…
