Beyond Chat: Phoenix Tests, Ruby Agents & the AI Tipping Point

May 28, 2025

The hosts dive into the vibrant Ruby AI community, revealing exciting projects like Phoenix that enhance software development. They discuss the challenges and unique advantages Ruby presents for AI integration, contrasting it with other languages. The conversation shifts to the overlooked potential of AI beyond chatbots, emphasizing the need for adaptability in coding practices. Listeners will learn about measuring side effects in interconnected systems and how emerging tools can lead to creative solutions in the evolving landscape of AI-powered applications.

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INSIGHT

Ruby Fits AI Ergonomics

Ruby's syntax and meta-programming make it especially ergonomic for building AI tools and DSLs.
Valentino and Joe see Ruby as a natural fit for creative, developer-friendly AI products.

ANECDOTE

Building Tools Because You Can

Valentino recalls a Rubyist who built a high-performance HTTP server simply because he could and it fit his use case.
They use this story to illustrate Ruby developers' tendency to craft custom tools that match their needs.

ADVICE

Favor Ruby-Native Tooling

Prefer Ruby-native libraries when building Ruby-first products instead of porting Python ports.
Choose tools that are well-maintained and designed for Ruby to avoid brittle rewrites.

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Valentino Stoll and co-host Joe Leo kick off The Ruby AI Podcast with a candid deep-dive into what it really takes to ship AI-powered products in Ruby today. From the origin story of Joe’s test-writing automation platform Phoenix to the surge of new Ruby-first agent libraries, the duo explore why the community is approaching a tipping point, how to escape “chat-bot-only” thinking, and where reactive, evaluation-driven tooling is headed next. Along the way they trade war stories about semver mishaps, code-review “LLM tells,” and the projects, meet-ups, and conferences that keep the Ruby-AI scene buzzing.

Takeaways

The Ruby AI community is growing and offers valuable networking opportunities.
Ruby's syntax is well-suited for AI applications, making it a fun choice for developers.
Generative AI tools can increase productivity but also add cognitive burden to developers.
The integration of AI tools in Ruby applications presents unique challenges.
Developers are relearning how to program with the advent of generative AI.
AI frameworks are evolving, and Ruby developers need to stay updated.
The importance of evaluating AI tools and their effectiveness in real-world applications.
Ruby's flexibility allows for creative solutions in AI development.
The future of AI in software development will require continuous adaptation.
Emerging AI frameworks in Ruby are promising but require careful evaluation.

Referenced In The Show

Phoenix by DefMethod – https://www.phoenix.love/
OpenAI Ruby SDK – https://github.com/openai/openai-ruby
Sublayer – https://github.com/sublayerapp/sublayer
CrewAI – https://github.com/crewAIInc/crewAI
Active Agent – https://github.com/activeagents/activeagent
Raix – https://github.com/OlympiaAI/raix
Shopify Roast – https://github.com/Shopify/roast
LangChain.rb – https://github.com/patterns-ai-core/langchainrb
Hugging Face smolagents – https://huggingface.co/docs/smolagents/index
Building Code Agents with Hugging Face smolagents – https://www.deeplearning.ai/short-courses/building-code-agents-with-hugging-face-smolagents/
V's side project, NowReading.dev – https://nowreading.dev