Engineering: AI

Two buzzwords circulating in the developer ecosystem today are MCP and A2A. Model Context Protocol (MCP) has been around since November 2024. Google released Agent2Agent (A2A) in April 2025 with an extensive list of technology partners. Developers can use the MCP and A2A open standards to provide context to models for building AI applications. MCP

This article has an accompanying GitHub Repository containing runnable samples: adk-samples In recent years, the development of autonomous agents—software entities capable of reasoning, planning, and taking actions on behalf of users—has moved from research labs into real-world applications. These AI agents are rapidly becoming central to building intelligent systems, whether through task automation, information retrieval,

If you are used to using the ChatGPT or Claude chat interfaces, you might think that these models have incredible memories, able to maintain knowledge of the conversation across multiple interactions. You're being tricked. The reality is that LLMs like Claude's have no inherent memory between calls. Each API request is stateless by design. When

Real-time language translation has become essential for global applications, communities, and businesses to break down language barriers and foster inclusive environments. In this article, I’ll explain how to implement real-time language translation in Stream applications using large language models (LLMs). By the end, you will understand how to: Authenticate users using a Stream token. Create

AI chatbots have become a familiar feature in many modern applications. Yet, practical questions remain: “How should we integrate a chatbot into our service?” To explore this question, I launched an experimental project: a sommelier chatbot. Wine is a domain rich with variety and unfamiliar terms—perfect for testing how helpful an AI assistant can be

The OpenAI Agents SDK for Python provides developers with the building blocks to implement two agentic solutions for AI applications. You can create text-generation agents, allowing users to get responses from text prompts. Additionally, you can build voice agents using the SDK. To create your first agent with the OpenAI Agents SDK, get started here.

An interesting quirk of large language models (LLMs) is that they aren’t all the same. ChatGPT tends to be better for analysis, but in the words of Paul Graham, “writes like a kid doing an assignment.” Claude is a much better writer but loves a little bit of hallucination. All other models have their strong

Large Language Models, although trained on large amounts of data, have limited domain-specific knowledge. This limitation makes them less effective for customer support chatbots or similar apps that require specific data. Retrieval Augmented Generation (RAG) solves this concern by allowing LLMs access to external knowledge sources for a more accurate response generation. This means that