Innovating Development: The Future of GitHub Agents and AI in Rails

Minerva Magic: OpenClaw, Agent Status Pages, and Training an AI Coworker in Ruby on Rails

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 [https://codenamev.substack.com/p/i-handed-an-ai-agent-27-domains-and] - Minerva's First Product [https://ups.dev] Core Tools & Frameworks - RubyLLM [https://github.com/crmne/ruby_llm] (Carmine Paolino) - Kamal [https://kamal-deploy.org] (Deploy Rails anywhere) - Tailscale [https://tailscale.com] (Secure networking) Libraries & Infra Mentioned - ExtraLite [https://github.com/digital-fabric/extralite] (SQLite performance layer) Learning & Community - Ruby AI Newsletter [https://rubyai.beehiiv.com/] (Matt Solt) Other Mentions - OpenClaw [https://openclaw.ai/] - Claude Code [https://claude.com/product/claude-code] - Action MCP [https://github.com/seuros/action_mcp] - Fizzy [https://fizzy.do] (37signals) - Magic Beans [https://github.com/henriquebastos/beans] (graph-based project management for agents) - ups.dev [https://ups.dev] (agent status pages project) - DailyVibe.ai [https://dailyvibe.ai] Books & Resources Referenced - Practical Object-Oriented Design in Ruby [https://www.poodr.com/] by Sandi Metz - Programming Ruby [https://pragprog.com/titles/ruby/programming-ruby-2nd-edition/] (Pickaxe Book) - The Well-Grounded Rubyist [https://www.manning.com/books/the-well-grounded-rubyist] - Layered Design for Ruby on Rails Applications [https://evilmartians.com/products/layered-design-book] — Vladimir Dementyev Cultural Reference - Wired article on AI-generated band marketing [https://www.wired.com/story/geese-chaotic-good-marketing-industry-plant/] (“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

You Can’t Vibe-Code Trust: Scaling AI Safely with Bekki Freeman

Valentino Stoll and co-host Joe Leo open the Ruby Podcast noting OpenAI is winding down its SOA video app and discuss the broader difficulty of building AI businesses. Guest Bekki Freeman, staff software engineer at Caribou Financial and organizer of Rocky Mountain Ruby, shares conference details (Boulder, Colorado at eTown, September 28–29; CFP opening soon; tickets after the schedule). The conversation focuses on safely scaling AI use in an 8-year Rails monolith: preparing messy codebases with dead code and metaprogramming, strengthening test harnesses and coverage, improving documentation, and being explicit about desired patterns rather than copying existing bad ones. They discuss PR review bottlenecks from increased AI-generated PRs, ideas like specialized AI review agents, stronger RuboCop rules, pairing/mobbing, and remote knowledge-sharing practices, plus security cautions and what AI may and may not replace (tech-debt work vs “taste”). 00:00 Sora Shutdown News 00:57 AI Hype Reality Check 01:43 Meet Bekki Freeman 02:00 Rocky Mountain Ruby Update 04:32 AI Meets Legacy Rails 07:22 Prep Codebase for AI 10:06 Patterns Versus Best Practices 12:37 Testing Strategy and TDD 16:45 PR Review Bottlenecks 19:27 Specialized Review Agents 21:31 Defining Quality Context 24:29 Humans and Team Adoption 25:20 Remote Change Adoption 27:00 Creating Sharing Rituals 29:19 Release Calls As Watercooler 30:12 Mob Sessions With Agents 33:55 Security And YOLO Risks 35:45 Too Much Code Problem 37:16 Vibe Coding Vs SaaS 42:10 AI Engineering In Two Years 45:33 Codex Versus Claude 47:39 Wrap Up And Farewell

You Can’t Vibe-Code Trust: Why Real SaaS Still Wins in the AI Era

On the Ruby AI Podcast, hosts Valentino and Joe Leo welcome Scholarly CTO/co-founder Kelly Sutton to discuss building a vertical SaaS “faculty information system” for universities. Sutton explains why competitors can’t easily replicate Scholarly: higher ed is moving off decades-old homegrown software, and the product must meet trust, security, compliance, and regulatory demands such as SOC 2 Type II. He describes how Scholarly expanded from replacing Excel/Access tracking to sophisticated workflow automation and how universities recently shifted from AI skepticism to AI FOMO. Scholarly uses AI in product surfaces, heavily in engineering, and via an admin MCP server that helps ops/customer success rapidly configure workflows from faculty handbooks with human-in-the-loop review. The conversation debates MCP’s likely temporariness versus traditional APIs, emphasizes smaller reviewable “PR-sized” outputs, and frames AI as an implementation detail focused on customer value. Valentino also shares an experiment training Claude to build products, including ups.dev [http://ups.dev/] and an open-source Ruby uptime-monitoring gem.

CRMs Don’t Have to Suck: Rebuilding Business Software with AI and Ruby with Thomas Witt

Many “AI startups” today are little more than thin wrappers around large language model APIs. But what happens when those APIs improve and the platforms absorb those features? In this episode of The Ruby AI Podcast, Valentino Stoll and Joe talk with builder and investor Thomas Witt, founder of Vendis.ai [https://vendis.ai/] and operator of the pre-seed firm Expedite Ventures. Thomas shares why he believes the next generation of durable companies must deliver real value deep in the product stack rather than bolting chat onto existing software. The conversation explores why traditional CRMs are widely disliked and how an AI-native CRM might look completely different. Instead of rigid forms and required fields, Thomas describes a system where conversations themselves become the primary data source. Emails, meetings, and messages are embedded, searched semantically, and transformed into structured knowledge automatically. They also dive into the architecture required to support this shift. From Ruby on Rails and Hotwire to DynamoDB, vector search, async Ruby, and multi-model LLM workflows, Thomas shares practical lessons from building AI-heavy production systems. Along the way the discussion touches on agentic coding workflows, LLM-as-a-judge evaluation patterns, telemetry for prompt chains, and why small teams may soon replace the massive engineering orgs we’ve grown used to. If you’re curious where Ruby, Rails, and AI systems are heading next, this conversation offers a fascinating glimpse. Show Notes Guest: Thomas Witt Founder of Vendis.ai [https://vendis.ai/] Investor at Expedite Ventures Topics we explore • Why many AI startups are just “wrappers” around LLM APIs  • What an AI-native CRM looks like when conversations become the database • Why Thomas chose Ruby on Rails [https://rubyonrails.org/] with minimal JavaScript using Hotwire [https://hotwired.dev/] and Stimulus [https://stimulus.hotwired.dev/] • Using Amazon DynamoDB [https://aws.amazon.com/dynamodb/] instead of relational databases for AI workloads • Hybrid keyword + vector search with OpenSearch [https://opensearch.org/] and Elasticsearch [https://www.elastic.co/elasticsearch/] • Async Ruby patterns using fibers, the Async ecosystem [https://github.com/socketry/async], and the Falcon web server • Orchestrating many concurrent LLM calls within a single user interaction • Background job systems and queues such as Amazon SQS [https://aws.amazon.com/sqs/] • Code quality workflows with StandardRB and RuboCop [https://rubocop.org/] • Using models like Claude [https://www.anthropic.com/claude], OpenAI Codex [https://openai.com/blog/openai-codex], and Gemini [https://deepmind.google/technologies/gemini/] together in multi-model workflows • Observability and prompt tracing with Langfuse [https://langfuse.com/] • Why AI tooling may enable much smaller engineering teams Mentioned in the Show • Vendis.ai [https://vendis.ai/] – Thomas’s AI-native CRM platform • Hotwire [https://hotwired.dev/] – HTML-over-the-wire approach for modern Rails apps • Falcon [https://socketry.github.io/falcon/] – Fiber-based Ruby web server • Ruby AI Builders Discord [https://discord.gg/] – Community of Ruby developers building AI tools • Chaos to the Rescue @ Artificial Ruby [https://youtu.be/X1jsOe1F62g]

Innovating Development: The Future of GitHub Agents and AI in Rails

In this episode of the Ruby AI Podcast, hosts Joe and Valentino welcome special guest, Kinsey Durham Grace, a prominent figure in the Ruby community and member of the GitHub team. The discussion covers a range of topics including the use of AI for generating episode artwork, the application of AI agents in coding tasks, and the recent developments at GitHub like the Agent HQ. Kinsey shares insights into her day-to-day work on the coding agent core team at GitHub, including the use of custom agents to enhance coding efficiency. They also delve into the impact of AI on software development, the importance of well-rounded developer skills, and Kinsey’s perspective on the future of Ruby in the AI landscape. 00:00 Introduction and Guest Welcome 00:30 AI-Generated Images and Their Drawbacks 03:07 Kinsey's Role at GitHub 06:33 Using AI Tools in Development 11:26 Challenges in Large Monolith Apps 18:23 Modular and Maintainable Agents 24:47 AI's Role in Software Development 25:29 Challenges with Current AI Tools 26:50 Observational Memory in AI 27:42 Open Claw and Heartbeat Concepts 28:22 Collaborative AI and Future Prospects 29:22 In-House vs. Third-Party Observability Tools 29:54 New AI Products and Intent Capture 31:08 Persisting Context in Software Development 37:42 Custom Agents and Knowledge Management 46:13 The Human Element in AI Collaboration 47:20 Skills for the Future of AI in Engineering 48:54 Ruby and AI: Staying Relevant 50:50 Conclusion and Final Thoughts 🔗 Resources Mentioned in This Episode Kinsey’s talk at RailsWorld 2025: The Rise of the Agents In Rails [https://www.rubyevents.org/talks/the-rise-of-the-agent-rails-in-the-ai-era] GitHub & Agent Workflows * https://github.com [https://github.com/] * https://github.com/features/copilot [https://github.com/features/copilot?utm_source=chatgpt.com] * https://github.blog [https://github.blog/] * https://cli.github.com [https://cli.github.com/] * https://code.visualstudio.com [https://code.visualstudio.com/] * https://github.com/features/codespaces [https://github.com/features/codespaces] Models & AI Tools Mentioned * https://claude.ai [https://claude.ai/] * https://www.anthropic.com [https://www.anthropic.com/] * https://openai.com [https://openai.com/] * https://platform.openai.com [https://platform.openai.com/] * https://gemini.google.com [https://gemini.google.com/] * https://cursor.sh [https://cursor.sh/] * https://ampcode.com [https://ampcode.com/] Observability & Infrastructure * https://www.datadoghq.com [https://www.datadoghq.com/] * https://learn.microsoft.com/en-us/azure/data-explorer/kusto/query/ [https://learn.microsoft.com/en-us/azure/data-explorer/kusto/query/] OpenClaw * https://openclaw.ai [https://openclaw.ai/?utm_source=chatgpt.com] * https://github.com/OpenClaw Mastra AI * https://mastra.ai [https://mastra.ai/?utm_source=chatgpt.com] QMD (Referenced by Valentino) * https://github.com/tobi/qmd [https://github.com/tobi/qmd] Stephen Margheim – SQLite / Ruby Work * https://fractaledmind.github.io [https://github.com/fractaledmind/] * https://github.com/digital-fabric/extralite [https://github.com/digital-fabric/extralite] GitHub-Related Announcements (Former CEO Mention) * https://entire.io/ [https://entire.io/]