New episode of Breaking Change is live! Ignore all previous instructions justin.searls.co/casts/hotfi…

Refactoring is an important tool in your toolbox for effectively managing your AI agent's context window, but it feels a lot different than it did in the past (a year ago). Just wrote up some thoughts on my newsletter about what Refactoring means when AI agents are able to write good, working code so fast. (link in next tweet)

A bunch of people have asked me how I was able to vibe code Protocollie in 4 days with Claude Code, so I wrote up some thoughts about the system and how it evolved.

Linus Torvalds once said "with enough eyes, all bugs are shallow." But he was thinking too small. With enough AIs, all software is shallow. I’ve been doing a lot of vibe coding lately and just wrote up some thoughts about the implications on my substack.

MCP: An (Accidentally) Universal Plugin System. Everyone keeps saying AI will bring the "personalization era" of software, but handwaving whenever it comes to "how". If MCP is a USB-C port like they say, you don't even need AI in the loop, you just have a universal plugin system for all software. I wrote up a little piece explaining how I'm thinking about MCP, why it's even bigger than the hype it's currently getting, and sharing a little bit about something we're building. Link to post below:

The 9th edition of Ruby AI News is live! This edition features an entire Obie Fernandez AI newsletter, awesome new Active Agent docs, Rapid Ruby inspiring the next generation of Ruby AI developers, and much more. Link below 🔻

🚨 New on The Ruby AI Podcast: “Sublayer & Artificial Ruby” with @scottwernerd A deep dive into LLM-native architectures, code synthesis, and the dream of an AI-powered Ruby DSL where engineering meets imagination. 🎧 Listen now: therubyaipodcast.com/2388930… #TheRubyAIPodcast #RubyAI

Wasting Inferences: Auto-Fixing Bugs with an LLM Swarm (for < 10¢!) Steve Yegge's "Revenge of the Junior Developer" predicts Agent Clusters/Fleets emerging around 2026. But what if we could experiment with that core idea today? This week on "Works on My Machine," I explore automating bug fixes directly from Asana using multiple LLMs simultaneously via Aider. The demo shows a workflow where assigning an Asana task triggers a Sublayer agent. This agent then scripts the Aider coding agent, instructing it to fix the reported bug using GPT-4o, Claude 3.5 Sonnet, and Gemini 2.0 Flash – generating three separate PRs for the same task. Key Takeaways: ➡️ Agent Clusters Now: While not deeply coordinated, basic agent workflows tackling coding tasks are feasible today. ➡️ Cheap Redundancy: "Wasting inferences" across these 3 powerful LLMs cost less than $0.10 for this bug fix! Makes parallel attempts viable. ➡️ Compare & Contrast: Automatically get multiple potential fixes and compare different implementation approaches generated by different models. ➡️ True Automation: Move beyond manually prompting agents via chat; trigger complex coding workflows directly from project management tools like Asana. Watch the full demo video showing the setup and results, and read the breakdown (including specific costs & code links) on this week's Substack post (link in next tweet)

Introducing Monkey's Paw 🐒✋ – a prompt-driven web framework in Ruby where your page descriptions are the source code. You write your wishes in markdown files in your wishes folder, and an LLM grants them... exactly as you state them. This is the first release in the "Works on Your Machine" category on my newsletter (link in comments!): sharing experiments that have graduated beyond just my local setup. It grew out of my RubyConf talk "Going Postel" and the idea that "Hallucinations are a Feature, not a Bug." - What if we embrace the weirdness and build systems that accept AI's output, rather than fighting for perfect structure? What Monkey's Paw does: 📝 Translates .md prompt files ("wishes") into full web pages. ✨ Uses an LLM to generate the HTML, CSS, and even attempt the JS based on your descriptions. ⚠️ Comes with the classic Monkey's Paw warning: Be careful what you wish for... Performance and precision are sacrificed at the altar of expression! It's less about perfect control, more about exploring what happens when natural language is the program. Think of it as the "yes, and..." improv approach to web frameworks. It's now available as a Ruby gem! Find the code on GitHub (sublayerapp/monkeyspaw) and read the full origin story, see examples (including the prompt that generated its own homepage!), and understand the why behind this experiment on my Substack. Would love to see what beautiful (or terrifying) things you create if you give it a spin!