Articles
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Jan 17, 2025 |
fintechweekly.com | Rosalia Mazza |Melissa Eggleston |Operations Officer |Aayush Mittal
Artificial Intelligence is no longer a fancy guest in the world of banking; it’s become the VIP, shaking up every corner of the industry. From humble beginnings as a support tool for back-office efficiency, AI now sits at the boardroom table, influencing strategies, reshaping services, and even reimagining how banks interact with you and your money. Let’s dive deep into this tech-fueled metamorphosis—because AI in banking isn’t just an upgrade; it’s a seismic shift.
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Dec 10, 2024 |
unite.ai | Aayush Mittal
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. Hosts: AI applications that initiate connections (e.g., Claude Desktop).
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Nov 25, 2024 |
unite.ai | Aayush Mittal
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 in Python, focusing on their relevance in AI and LLM-based systems.
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Nov 20, 2024 |
unite.ai | Aayush Mittal
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, and compliance emerge.
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Nov 14, 2024 |
unite.ai | Aayush Mittal
Code Implementation: Putting LLM-as-a-Judge into ActionThis section will guide you through setting up and implementing the LLM-as-a-Judge framework using Python and Hugging Face. From setting up your LLM client to processing data and running evaluations, this section will cover the entire pipeline. To use an LLM as an evaluator, we first need to configure it for evaluation tasks.
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We have been experimenting on integrating AI at @screener_in. I'm really happy with the quality of outputs we are getting now (after multiple experiments over last few months). For eg - I saw the GM Brew has large investments, I wanted to quickly understand what these are: https://t.co/FN5JFUvNNl
Read this excellent letter by Warren Buffett in 2003 - America’s Growing Trade Deficit Is Selling the Nation - https://t.co/X5S4mhWctX This is a must read!
RT @faltoo: Thanks everyone for your calls, tweets, support and wishes😊. We are back! Faster and stronger than before. We hadn't received…
