Author: Aayush Mittal

Imagine this: you have built an AI app with an incredible idea, but it struggles to deliver because running large language models (LLMs) feels like trying to host a concert with a cassette player. The potential is there, but the performance? Lacking. This is where inference APIs for open LLMs come in. These services are

Anthropic’s Model Context Protocol (MCP) is an open-source protocol that enables secure, two-way communication between AI assistants and data sources like databases, APIs, and enterprise tools. By adopting a client-server architecture, MCP standardizes the way AI models interact with external data, eliminating the need for custom integrations for each new data source. Key Components of

As AI engineers, crafting clean, efficient, and maintainable code is critical, especially when building complex systems. Design patterns are reusable solutions to common problems in software design. For AI and large language model (LLM) engineers, design patterns help build robust, scalable, and maintainable systems that handle complex workflows efficiently. This article dives into design patterns

The growth of autonomous agents by foundation models (FMs) like Large Language Models (LLMs) has reform how we solve complex, multi-step problems. These agents perform tasks ranging from customer support to software engineering, navigating intricate workflows that combine reasoning, tool use, and memory. However, as these systems grow in capability and complexity, challenges in observability, reliability

The LLM-as-a-Judge framework is a scalable, automated alternative to human evaluations, which are often costly, slow, and limited by the volume of responses they can feasibly assess. By using an LLM to assess the outputs of another LLM, teams can efficiently track accuracy, relevance, tone, and adherence to specific guidelines in a consistent and replicable

Microsoft Research introduced AutoGen in September 2023 as an open-source Python framework for building AI agents capable of complex, multi-agent collaboration. AutoGen has already gained traction among researchers, developers, and organizations, with over 290 contributors on GitHub and nearly 900,000 downloads as of May 2024. Building on this success, Microsoft unveiled AutoGen Studio, a low-code

On October 17, 2024, Microsoft announced BitNet.cpp, an inference framework designed to run 1-bit quantized Large Language Models (LLMs). BitNet.cpp is a significant progress in Gen AI, enabling the deployment of 1-bit LLMs efficiently on standard CPUs, without requiring expensive GPUs. This development democratizes access to LLMs, making them available on a wide range of

The race to dominate the enterprise AI space is accelerating with some major news recently. OpenAI’s ChatGPT now boasts over 200 million weekly active users , a increase from 100 million just a year ago. This incredible growth shows the increasing reliance on AI tools in enterprise settings for tasks such as customer support, content

In the constantly evolving field of molecular biology, one of the most challenging tasks has been designing proteins that can effectively bind to specific targets, such as viral proteins, cancer markers, or immune system components. These protein binders are crucial tools in drug discovery, disease treatment, diagnostics, and biotechnology. Traditional methods of creating these protein

As the demand for large language models (LLMs) continues to rise, ensuring fast, efficient, and scalable inference has become more crucial than ever. NVIDIA’s TensorRT-LLM steps in to address this challenge by providing a set of powerful tools and optimizations specifically designed for LLM inference. TensorRT-LLM offers an impressive array of performance improvements, such as