The Ruby AI Podcast

In this episode of the Ruby AI podcast, hosts Valentino Stoll and Joe Leo engage with Evan Phoenix, a seasoned Ruby programmer and CEO of Mirren. The conversation explores Evan's unique name origin, his career trajectory, and the integration of AI in development workflows. They discuss the distinction between real and fake AI in products, the impact of AI on engineering practices, and the future of AI in development tools. Evan shares insights on performance optimization, human-centric AI interactions, and the role of AI in deployment and architecture detection. In this conversation, Joe, Evan Phoenix, and Valentino Stoll discuss the evolving landscape of software development, particularly focusing on the role of AI, automation, and the Ruby programming language. They explore how AI can assist in analyzing code bases, the future of development with ambient agents, and the potential resurgence of monolithic architectures. The discussion also touches on the importance of human-centric design in software, the significance of experimentation, and the unique strengths of Ruby in the current tech environment. The conversation concludes with predictions about the future of small teams in software development and the impact of AI on coding practices.

In this episode of the Ruby AI Podcast, hosts Valentino Stoll and Joe Leowelcome AI and Ruby expert Chris Hasinski. They delve into the benefits andchallenges of self-hosting AI models, including control over model updates, costconsiderations, and the ability to fine-tune models. Chris shares his journeyfrom machine learning at UC Davis to his extensive work in AI and Ruby, touchingupon his contributions to open source projects and the Ruby AI community. Thediscussion also covers the limitations of current LLMs (Large Language Models)in generating Ruby code, the importance of high-quality data for effective AI,and the potential for Ruby to become a strong contender in AI development.Whether you're a Ruby enthusiast or interested in the intersection of AI andsoftware development, this episode offers valuable insights and practicaladvice.00:00 Introduction and Guest Welcome00:31 Why Self-Host Models?01:28 Challenges and Benefits of Self-Hosting03:14 Chris's Background in Machine Learning04:13 Applications Beyond Text06:39 Fine-Tuning Models12:27 Ruby in Machine Learning16:06 Distributed Training and Model Porting18:22 Choosing and Deploying Models25:19 Testing and Data Engineering in Ruby27:56 Database Naming Conventions in Different Languages28:19 Importance of Data Quality for AI18:03 Monitoring Locally Hosted AI Models29:37 Challenges with LLMs and Performance Tracking31:09 Improving Developer Experience in Ruby31:45 Ruby's Ecosystem for Machine Learning32:43 The Need for Investment in Ruby's AI Tools38:25 Challenges with AI Code Generation in Ruby43:35 Future Prospects for Ruby in AI51:26 Conclusion and Final Thoughts

In this episode of the Ruby AI Podcast, hosts Joe Leo and his co-host interview Carmine Paolino, the developer behind Ruby LLM. The discussion covers the significant strides and rapid adoption of Ruby LLM since its release, rooted in Paolino's philosophy of building simple, effective, and adaptable tools. The podcast delves into the nuances of upgrading Ruby LLM, its ever-expanding functionality, and the core principles driving its design. Paolino reflects on the personal motivations and community-driven contributions that have propelled the project to over 3.6 million downloads. Key topics include the philosophy of progressive disclosure, the challenges of multi-agent systems in AI, and innovative ways to manage contexts in LLMs. The episode also touches on improving Ruby’s concurrency handling using Async and Rectors, the future of AI app development in Ruby, and practical advice for developers leveraging AI in their applications.00:00 Introduction and Guest Welcome00:39 Depend Bot Upgrade Concerns01:22 Ruby LLM's Success and Philosophy05:03 Progressive Disclosure and Model Registry08:32 Challenges with Provider Mechanisms16:55 Multi-Agent AI Assisted Development27:09 Understanding Context Limitations in LLMs28:20 Exploring Context Engineering in Ruby LLM29:27 Benchmarking and Evaluation in Ruby LLM30:34 The Role of Agents in Ruby LLM39:09 The Future of AI Apps with Ruby39:58 Async and Ruby: Enhancing Performance45:12 Practical Applications and Challenges49:01 Conclusion and Final Thoughts

In this episode of the Ruby AI Podcast, hosts Valentino Stoll and Joe Leo are joined by Avi Flombaum, the founder of Flatiron School. Avi talks about the origins of Flatiron, the success it achieved, and the educational methods used to teach programming, emphasizing on the importance of understanding code deeply and leveraging AI efficiently. He discusses the challenges and changes in the industry, particularly with the rise of AI, and provides insight into modern workflows and product development. The conversation also touches on the necessity of integrating product thinking into engineering and how automated workflows can improve consistency and efficiency in software creation.00:00 Introduction and Welcoming Avi Flombaum00:55 Avi's Journey to Founding Flatiron School02:22 The Impact and Growth of Flatiron School04:40 Challenges and Evolution in the Bootcamp Industry05:39 Transitioning from Education to AI06:39 The Role of AI in Modern Development08:14 Effective AI Workflows for Developers16:08 Teaching and Learning with AI20:47 Product Management and Engineering Collaboration27:31 Leveraging AI in Product Development28:35 Exploring AI-Driven Product Development29:42 Teaching Product Management Skills30:49 Innovative Solutions in Product Design32:25 Understanding User Needs and Problem Solving35:33 Learning Through Code and AI Tools42:38 The Future of Software Engineering

In this episode of the Ruby AI Podcast, co-hosts Valentino Stoll and Joe Leo engage in a lively discussion with guest Justin Searls. They explore the evolving landscape of software development with agentic AI tools, comparing traditional agile methodologies with emerging AI-driven practices. Justin Searls his experiences with refactoring and the challenges of integrating AI tools into development workflows. The conversation touches on the suitability of AI in coding, philosophical perspectives on reinforcing proper software practices, and the future potential of these technologies. Justin also provides valuable insights on configuring AI tools for better productivity and discusses his personal coping strategies with the frustrations of modern AI capabilities.00:00 Introduction and Hosts Banter00:30 Guest Introduction: Justin Searls03:13 Justin's Career and Conference Talks07:52 The Evolution of Agile and Development Practices16:07 Challenges with AI and Iterative Development27:47 Recalibrating Development Processes28:00 Adoption of Pivotal Labs' Methods28:28 Continuous Integration and Testing29:21 AI in Development: Current State and Challenges30:16 The Role of AI Agents in Development32:17 Frustrations with AI Tools35:03 Philosophical Reflections on AI in Development36:16 Generative vs. Subtractive AI37:06 The Future of AI in Software Development39:27 Balancing Coding Enjoyment and Productivity44:02 Capability vs. Suitability in AI Tools46:35 Prompt Engineering Tips and Tricks52:39 Closing Thoughts and Plugs