The Ruby AI Podcast

Valentino Stoll, Joe Leo

0.0（0）
テクノロジー
アップデート：月2回

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.

2日前

From Writing Code To Orchestrating It, Agentic Development with Ben Scofield

In this episode of the Ruby AI Podcast, hosts Valentino Stoll and Joe Leo are joined by Ben Schofield, an accomplished author, open source contributor, and Ruby enthusiast. The discussion starts with thoughts on the upcoming RubyConf and the unique experience of conferences hosted in Las Vegas. Ben shares his recent experiences with Bento and the impact of layoffs. The conversation delves deep into the nature of expertise, exploring questions around achieving world-class performance and domain-specific skills. The hosts explore the goals of software development, the role of AI in coding, and the importance of intentionality in using agents. They also touch on the concept of default settings in development, the nuances of staff engineering, and strategies for training future staff engineers. The discussion concludes with ideas for improving the onboarding and training of engineers in the evolving landscape of AI tools. Mentioned in this episode: RubyConf 2026 (Las Vegas)RailsConf (context/history)O’Reilly (RailsConf partner mentioned historically)Bento (Ben’s recent company)Gusto (host context)Artificial Ruby / Ruby x AI NYC meetupsAgentic coding & toolingClaude Code docsClaude Code + MCPBooks, papers, and ideasC. Thi Nguyen (background)Games: Agency as Art (Oxford)Ezra Klein Show episode (Nguyen)Malcolm Gladwell, OutliersAndy Hunt, Pragmatic Thinking and Learning (Refactor Your Wetware)Ericsson et al. (1993) deliberate practice (DOI)Macnamara & Maitra replication (2019) (DOI)David Epstein, RangeWill Larson, Staff EngineerRobert Cialdini, Influence resourcesDHH on conceptual compressionChad Fowler, The Phoenix Architecture (Leaflet)Quote referenced (“How can I know what I think till I see what I say?”)Ruby/Rails primitives referenced in Valentino's experimentsRuby method_missingRuby define_methodRails rescue_fromValentino's experimental Ruby project (“Chaos to the Rescue”) that uses LLMs + runtime method definition

53分
1月27日

New Year, New Ruby: Agents, Wishes, and a Calm Ruby 4

Ruby turns 30, Ruby 4 quietly ships, and the AI tooling arms race shows signs of maturity. Valentino and Joe unpack what stability really means for a language in its third decade, debate agent-driven development, AI “slop,” binary distribution, and whether open source incentives are breaking down—or simply evolving. Mentioned In The Show A grab-bag of tools, projects, and references Valentino & Joe brought up. Ruby & Core Ecosystem Ruby Gets A Fresh Look — Official Ruby programming language site (news, downloads, docs) now with a great new look. Ruby Kaigi — Ruby’s flagship conference (talks, schedules, archives). Bundler — Ruby dependency manager used across the ecosystem.AI Coding Tools Claude Code — Anthropic’s CLI coding assistant workflow discussed heavily in the episode.OpenAI Codex — OpenAI’s coding agent/tooling referenced as an alternative workflow. Ruby Web Frameworks & Architecture Rails Framework — Ruby on Rails, referenced as the default baseline for many apps.Jumpstart Rails — Rails starter kits/templates mentioned as a “pick a Rails” approach.Roda Framework — Jeremy Evans’ web toolkit (lighter than Rails, bigger than Sinatra).dry-rb Suite — Ruby gems for functional-ish architecture and explicit business logic.Trailblazer — High-level architecture for operations, workflows, and domain logic.Quality, Testing, and Practice Better Specs — Community-curated RSpec guidelines mentioned as a spec style target.Datadog — Error monitoring referenced in the “well-defined bug + stack trace” workflow.Open Source Sustainability GitHub Sponsors — Sponsorship mechanism discussed as one (partial) monetization path.People Mentioned Sandi Metz — Referenced as the “code whisperer” ideal for idiomatic Ruby guidance.

51分
1月6日

Real vs. Fake AI with Evan Phoenix

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.

1時間2分
2025/12/02

Running Self-Hosted Models with Ruby and Chris Hasinski

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

54分
2025/11/18

The Latent Spark: Carmine Paolino on Ruby’s AI Reboot

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

52分
2025/11/04

Building Futures: AI, Careers & the Rails Ahead with Avi Flombaum

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

52分
2025/10/21

The TLDR of AI Dev: Real Workflows with Justin Searls

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 Banter 00:30 Guest Introduction: Justin Searls 03:13 Justin's Career and Conference Talks 07:52 The Evolution of Agile and Development Practices 16:07 Challenges with AI and Iterative Development 27:47 Recalibrating Development Processes 28:00 Adoption of Pivotal Labs' Methods 28:28 Continuous Integration and Testing 29:21 AI in Development: Current State and Challenges 30:16 The Role of AI Agents in Development 32:17 Frustrations with AI Tools 35:03 Philosophical Reflections on AI in Development 36:16 Generative vs. Subtractive AI 37:06 The Future of AI in Software Development 39:27 Balancing Coding Enjoyment and Productivity 44:02 Capability vs. Suitability in AI Tools 46:35 Prompt Engineering Tips and Tricks 52:39 Closing Thoughts and Plugs

55分
2025/10/07

Real-World Ruby AI: Practical Systems That Work

In this episode of the Ruby AI Podcast, co-hosts Joe Leo and Valentino Stoll, alongside guest Amanda Bizzinotto from Ombu Labs, delve into the ongoing controversy within the Ruby community involving Ruby Central, Shopify, and Bundler/Ruby Gems. While both Valentino and Amanda share their perspectives on the situation, the conversation swiftly transitions into Amanda's journey and current work in AI and machine learning at Ombu Labs. The episode highlights various AI initiatives, including the creation of an AI bot to streamline internal processes, automated Rails upgrade roadmaps, and multi-agent architectures aimed at enhancing efficiency in Rails projects. Amanda also discusses the challenges of integrating AI in consultancy services and shares some insights on the tools and strategies used at Ombu Labs. The podcast concludes with exciting updates about Amanda's recent work, Joe's announcements on upcoming projects including Phoenix's public release, and Valentino's discovery of a new user interface for Claude Swarm. 00:00 Introduction and Welcome 00:26 Ruby Community Controversy 04:37 Amanda's AI Journey 08:45 AI in Business and Consultancy 16:24 AI-Powered Tools and Applications 23:09 Managing Knowledge Base Updates 24:42 Prompting Strategies and Agentic Workflows 26:02 Understanding Workflows vs. Agents 28:37 Observability in AI Systems 29:06 Advanced Prompting Techniques 31:08 Multi-Agent Architectures 34:32 Ruby AI Gems and Libraries 37:09 Exciting Announcements and Future Plans 41:44 Conclusion and Final Thoughts Mentioned In The Show: AI for Rails upgrades: FastRuby automated roadmapPGVector and Neighbor gemGuardrails.ai for hallucination control (https://www.guardrailsai.com)Microsoft Presidio for PII strippingObservability with LangFuse (https://www.langfuse.com)Prompting engineering techniquesChain-of-Thought, ReAct pattern article ActiveAgentLangChain.rbDSPy.rbPhoenix AI upgrade assistant public beta Oct 15 eventOmbu Labs roadmap tool live nowSwarm UI for Claude Swarm by Parruda Ombu Labs – https://ombulabs.comArtificial Ruby NYC meetup – https://artificialruby.aiShopify Claude Swarm project – https://github.com/shopify/claude-swarm

42分

すべて見る（16）

予告編

Trailer: The Ruby AI Podcast

In this episode of the Ruby AI podcast, hosts Landon and Valentino discuss the exciting developments in the Ruby AI community. They explore three key gems: Ruby OpenAI, Raix, and Langchain.rb, highlighting their features, use cases, and the importance of evaluation methodologies like RAGAS in AI systems. The conversation emphasizes the collaborative spirit of the Ruby AI community and the potential for innovation in AI applications using Ruby. Takeaways The Ruby AI community is vibrant and growing.Ruby OpenAI is essential for integrating OpenAI's capabilities.Raix gem offers an object-oriented approach to AI in Ruby.Langchain RB normalizes AI provider integrations in Ruby.RAGAS provides a framework for evaluating AI outputs.Community engagement is crucial for Ruby AI's growth.Documentation is key for developers using these gems.Collaboration among developers enhances innovation.AI systems require robust evaluation methodologies.Ruby is at the forefront of AI development.Sound Bites "This is gonna be really exciting.""Join the Ruby AI Builders Discord.""There's so much cool stuff out there."

シーズン1、エピソード1

•

15分

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.

クリエイター

Valentino Stoll, Joe Leo
配信期間

25年 - 26年
エピソード

16
制限指定

不適切な内容を含む
Copyright

© 2026 The Ruby AI Podcast
番組のWebサイト

The Ruby AI Podcast

マネージメント

マネージメント

アップデート：毎週