Send us Fan Mail

Rarely does a story about an AI tool actively resisting instructions end up being the most compelling argument for using that tool. When Claude initially pushed back against adding conditionals for dual booting large legacy Rails applications, the team at FastRuby had to essentially teach it their own hard-won expertise rather than letting it default to general Stack Overflow consensus.

Ernesto Tagwerker from OmbuLabs and FastRuby joins Valentino Stoll and Joe Leo to unpack what that process actually looks like in practice. The conversation covers dual booting (running test suites against multiple Rails versions simultaneously), encoding human experience into AI-usable skills, and the shift toward outcome-based value rather than hourly billing. After more than 60,000 development hours across eight years of Rails upgrades, FastRuby has started consistently beating their project estimates with one human and one AI agent matching two humans' output.

What does it actually mean to keep humans in the loop when AI handles ninety percent of implementation work? Ernesto argues that Ruby's readability makes it especially valuable precisely when humans need to oversee and debug AI-generated code. Genuinely, the point lands well and stays with you.

Tune in for a grounded, honest look at where AI genuinely helps and where it still falls short.

Mentioned in the show:

• Ernesto Tagwerker
• Ernesto Tagwerker on GitHub
• OmbuLabs.ai
• OmbuLabs.ai Open Source AI Projects & Claude Code Skills
• FastRuby.io
• FastRuby.io Team
• FastRuby.io Blog: Articles by Ernesto Tagwerker
• next_rails GitHub Repo
• The Next Rails Gem
• How to Dual Boot Rails
• FastRuby.io Rails Upgrade Methodology as Claude Code Skills
• Claude Code Rails Upgrade Skill
• Claude Code Dual Boot Skill
• Claude Code Rails Load Defaults Skill
• Automated Roadmap to Upgrade Rails
• Ruby Critic
• Skunk
• MetricFu
• RailsBump
• Ruby LLM
• GitHub Scientist
• The Well-Grounded Rubyist
• Minerva's New Journal

Send us Fan Mail

Joe Leo hosts the Ruby AI Podcast with guest Mike Rispoli, discussing Ruby Central’s financial instability, leadership changes, the Ruby Alliance (including Gusto joining), and concerns about fragmentation in the Ruby ecosystem after RailsConf’s end and uncertainty around RubyConf. They compare governance models (benevolent dictator vs committees) and debate centralized package infrastructure versus decentralized approaches amid growing security threats. Rispoli explains Cause of a Kind’s rebrand toward “modernize your software,” focusing on high-security verticals (healthcare, education), migrations, PE-driven remediation, and an on-site “War Room” offering, aiming to avoid work that can be easily “vibe coded.” They cover rising importance of continuous security testing, shifting client expectations, anti-patterns in AI-built products, and Rispoli’s multi-model AI coding workflow (Claude, Kimi, Qwen, Codex/GPT 5.5) plus training engineers in forward-deployed skills via his “Behind Enemy Lines” series.

00:00 Welcome and Guest Intro
00:32 Gusto Joins Ruby Alliance
01:59 Is Ruby Central Ending
03:28 Governance Models Debate
06:46 Conferences and Community Shift
08:15 Centralized Packages vs Git URLs
09:17 Cause of a Kind Rebrand
11:21 Modernization and War Room
14:05 AI Pressure and Agency Strategy
18:25 AI Builds Faster Rails Rewrites
21:40 Security Chaos Monkey Era
25:31 Tooling Diversification
26:50 Testing Alternatives
28:00 GPT 5.5 Workflow
30:48 Switching Model Harnesses
31:50 When to Go Solo
34:28 Vibe Coding Pitfalls
36:16 Marketing First MVP
39:02 Teaching FDE Skills
43:08 Selling Pushback
47:44 Closing and Meetup

Send us Fan Mail

What happens when you treat an AI agent like a co-founder instead of a tool?

In this episode, Valentino and Joe go deep into a real-world experiment: spinning up an autonomous agent using OpenClaw, giving it domains, goals, and just enough guidance to build an actual business. From creating accounts and managing projects to writing code, deploying with Kamal, and even designing its own training curriculum, the agent evolves from confused assistant to something resembling a junior engineer with initiative.

Along the way, they explore the messy reality of agent workflows: memory systems, self-training loops, PR reviews, hallucinated confidence, and the constant tension between autonomy and control. The result? A working product, 15 early users, and a pile of hard-earned lessons about what AI can and definitely cannot do today.

If you’re building with agents, thinking about autonomous systems, or just curious what happens when you let AI run a startup… this one’s for you.

🔗 Show Notes

- Valentino's Minerva Experiment
- Minerva's First Product

Core Tools & Frameworks
- RubyLLM (Carmine Paolino)
- Kamal (Deploy Rails anywhere)
- Tailscale (Secure networking)

Libraries & Infra Mentioned
- ExtraLite (SQLite performance layer)

Learning & Community
- Ruby AI Newsletter (Matt Solt)

Other Mentions
- OpenClaw
- Claude Code
- Action MCP
- Fizzy (37signals)
- Magic Beans (graph-based project management for agents)
- ups.dev (agent status pages project)
- DailyVibe.ai

Books & Resources Referenced
- Practical Object-Oriented Design in Ruby by Sandi Metz
- Programming Ruby (Pickaxe Book)
- The Well-Grounded Rubyist
- Layered Design for Ruby on Rails Applications — Vladimir Dementyev

Cultural Reference
- Wired article on AI-generated band marketing (“Geese”)

00:00 Podcast kickoff
00:40 Geese AI marketing psyop
02:03 Starting an AI band
04:18 Daily Vibe artist generator
06:24 Open Claw origin story
08:52 Domains to business ideas
10:44 Onboarding an AI coworker
13:22 Handholding and action loops
14:10 Shark Tank idea filter
15:32 Training and memory system
20:20 UPS dev agent status pages
22:54 Rails build struggles
24:04 Bootcamp with Ruby books
26:20 Rebuild MVP and open source
27:55 Deploying with EC2
28:38 Locking Down Access
30:06 AI PR Reviews
32:50 Self QA Automation
36:11 Fixing Agent Memory
38:15 Email and Token Costs
40:11 Heartbeats and Delegation
43:01 Customer Discovery Lessons
44:49 Selling Workflow Friction
48:19 Knowledge Base Frameworks
51:37 Open Source Model Future
53:57 Security Agents and Wrap