Run Multiple AI Coding Agents in Parallel with Container-Use from Dagger Understanding the Target Audience The target audience for the topic of running multiple AI coding agents in parallel using container-use from Dagger primarily consists of developers, team leads, and project managers in tech organizations. These individuals are likely engaged in software development, particularly in… →

«`html CURE: A Reinforcement Learning Framework for Co-Evolving Code and Unit Test Generation in LLMs Introduction Large Language Models (LLMs) have shown substantial improvements in reasoning and precision through reinforcement learning (RL) and test-time scaling techniques. Despite outperforming traditional unit test generation methods, most existing approaches such as O1-Coder and UTGEN require supervision from ground-truth… →

«`html Develop a Multi-Tool AI Agent with Secure Python Execution using Riza and Gemini In this tutorial, we’ll harness Riza’s secure Python execution as the cornerstone of a powerful, tool-augmented AI agent in Google Colab. We will begin with seamless API key management through Colab secrets, environment variables, or hidden prompts to configure your Riza… →

How Do LLMs Really Reason? A Framework to Separate Logic from Knowledge Understanding the Target Audience The target audience for this content primarily comprises AI researchers, business managers, and professionals in fields such as healthcare and finance who are interested in the functioning and evaluation of large language models (LLMs). These readers are typically involved… →