The Genesis of Utopia.Poker: AI Tackles Poker Hands!

Well, hello there, fellow tech enthusiasts and poker aficionados! We've been on a bit of a wild ride, and we wanted to share the story of our first attempts at building Utopia.Poker – our very own AI-powered poker hand analysis tool. Imagine an AI agent so clever it can take those cryptic "phh" (poker hand history) files and magically translate them into actionable insights. That's the dream, and we're building it, one line of AI-generated code at a time!

Scaling with AI: Terraform and the Power of Automation

One of the coolest parts? Utopia.Poker, from its core application logic to the very infrastructure it runs on, is 100% AI-generated. We're talking Auto Scaling Groups (ASGs), Application Load Balancers (ALBs), the whole network shebang – all conjured into existence by our AI overlords... I mean, assistants!

Our Terraform setup, also an AI masterpiece, is designed for scalability. Think of it like having an infinitely expandable poker table. We're leveraging the power of AWS ECS Fargate, which allows us to run our containerized application without worrying about managing the underlying servers. The Application Load Balancer ensures that as more users flock to get their hands analyzed, the system can gracefully distribute the load, spinning up new instances of our poker-savvy AI agent as needed. It's like magic, but with more configuration files. This means Utopia.Poker is built to grow, ready to handle a full house of users (pun absolutely intended!).

The Hilarious Struggle of Prompt Engineering an LLM... to Write Prompts

Now, let's talk about the really fun part: getting an LLM to generate prompts for another LLM. It's like a digital game of telephone, but with more existential crises for the AI. We quickly learned that prompting an LLM to write good prompts is an art form in itself.

There were moments of sheer comedic gold. Picture this: you've carefully crafted a meta-prompt, explaining in excruciating detail that you need a series of sub-prompts. And what does the LLM do? It starts generating the sub-prompts, gets about halfway through, and then... completely forgets its original mission! It's like it suddenly developed AI-ADHD and decided, "You know what? I'm just going to write a sonnet about teapots now." We'd be left scratching our heads, wondering where our carefully orchestrated prompt-generating assembly line went off the rails. It was a masterclass in patience and iterative refinement.

Wisdom from the Trenches: Tips from Harper Reed

Speaking of iterative refinement, we stumbled upon a fantastic blog post by Harper Reed, "My LLM codegen workflow atm", which resonated deeply with our own chaotic-yet-productive process. Two tips that really stood out:

  1. Iterative Specification Development: Harper suggests using a conversational LLM to hone an idea by asking one question at a time. This helps build a thorough, step-by-step specification. We found this incredibly useful for breaking down complex features into manageable, AI-digestible chunks.
  2. The Almighty todo.md: Keeping a todo.md file as a checklist that your codegen tools can update is genius! It’s a simple yet powerful way to track progress and maintain state, especially when you're juggling multiple AI-generated components.

The Journey Continues

So, that's a little peek into our first attempts at bringing Utopia.Poker to life. It's been a journey filled with challenges, laughter, and a whole lot of AI-generated code. We're excited about what the future holds and can't wait to share more of our adventures (and misadventures) with you.

Stay tuned, and may your flops be favorable!