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 Leo
welcome AI and Ruby expert Chris Hasinski. They delve into the benefits and
challenges of self-hosting AI models, including control over model updates, cost
considerations, and the ability to fine-tune models. Chris shares his journey
from machine learning at UC Davis to his extensive work in AI and Ruby, touching
upon his contributions to open source projects and the Ruby AI community. The
discussion 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 and
software development, this episode offers valuable insights and practical
advice.

00:00 Introduction and Guest Welcome
00:31 Why Self-Host Models?
01:28 Challenges and Benefits of Self-Hosting
03:14 Chris's Background in Machine Learning
04:13 Applications Beyond Text
06:39 Fine-Tuning Models
12:27 Ruby in Machine Learning
16:06 Distributed Training and Model Porting
18:22 Choosing and Deploying Models
25:19 Testing and Data Engineering in Ruby
27:56 Database Naming Conventions in Different Languages
28:19 Importance of Data Quality for AI
18:03 Monitoring Locally Hosted AI Models
29:37 Challenges with LLMs and Performance Tracking
31:09 Improving Developer Experience in Ruby
31:45 Ruby's Ecosystem for Machine Learning
32:43 The Need for Investment in Ruby's AI Tools
38:25 Challenges with AI Code Generation in Ruby
43:35 Future Prospects for Ruby in AI
51:26 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 Welcome
00:39 Depend Bot Upgrade Concerns
01:22 Ruby LLM's Success and Philosophy
05:03 Progressive Disclosure and Model Registry
08:32 Challenges with Provider Mechanisms
16:55 Multi-Agent AI Assisted Development
27:09 Understanding Context Limitations in LLMs
28:20 Exploring Context Engineering in Ruby LLM
29:27 Benchmarking and Evaluation in Ruby LLM
30:34 The Role of Agents in Ruby LLM
39:09 The Future of AI Apps with Ruby
39:58 Async and Ruby: Enhancing Performance
45:12 Practical Applications and Challenges
49:01 Conclusion and Final Thoughts