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The Ruby AI Podcast

The Ruby AI Podcast

By: Valentino Stoll Joe Leo
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The Ruby AI Podcast explores the intersection of Ruby programming and artificial intelligence, featuring expert discussions, innovative projects, and practical insights. Join us as we interview industry leaders and developers to uncover how Ruby is shaping the future of AI.

© 2025 The Ruby AI Podcast
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Episodes
  • Running Self-Hosted Models with Ruby and Chris Hasinski
    Dec 2 2025

    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

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    54 mins
  • The Latent Spark: Carmine Paolino on Ruby’s AI Reboot
    Nov 18 2025

    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

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    52 mins
  • Building Futures: AI, Careers & the Rails Ahead with Avi Flombaum
    Nov 4 2025

    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 Flombaum
    00:55 Avi's Journey to Founding Flatiron School
    02:22 The Impact and Growth of Flatiron School
    04:40 Challenges and Evolution in the Bootcamp Industry
    05:39 Transitioning from Education to AI
    06:39 The Role of AI in Modern Development
    08:14 Effective AI Workflows for Developers
    16:08 Teaching and Learning with AI
    20:47 Product Management and Engineering Collaboration
    27:31 Leveraging AI in Product Development
    28:35 Exploring AI-Driven Product Development
    29:42 Teaching Product Management Skills
    30:49 Innovative Solutions in Product Design
    32:25 Understanding User Needs and Problem Solving
    35:33 Learning Through Code and AI Tools
    42:38 The Future of Software Engineering

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    52 mins
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