The Multiverse School

You'll configure an AI assistant with persistent memory and preferences, then put it through critical thinking boot camp. Learn to write prompts with specific evaluative criteria that get consistent results, practice divergent thinking exercises to offset AI's tendency to narrow creativity, and question your own assumptions about story, character, and style. Build a complete story framework using the Story Circle method, develop distinct character voices, and create a Director system that orchestrates multiple AI agents like a creative team. Master the art of managing artificial collaborators - knowing when to trust their output, when to push back, and how to maintain your unique creative voice. By the end, you'll have working character agents that can write full scenes in your style, plus the critical thinking frameworks to keep improving them. Perfect for writers who want AI superpowers without becoming generic.

- How to actually keep a budget - How to reduce costs and keep them under control - It's also a space and time to do this together

- Understand the history and genres of interactive fiction - Design compelling branching narratives and story trees - Use popular IF tools like Twine, Ink, or Inform - Write dialogue and scenes that respond to player choices - Balance player agency with narrative cohesion - Publish and share your interactive fiction projects

Download and use alternative, free versions of popular software, including business tools, email, and other stuff you don't need to pay anyone for.

You'll build AI-powered business workflows including email triage automation, calendar/task management, data processing pipelines, and complete business automation systems. No coding required - learn to use AI and no-code tools to handle repetitive business tasks.

You'll learn to build prompts and agentic applications with Python, master context engineering techniques, and develop advanced AI integration skills for technical applications.

- How to build agents using the ReACT pattern (Reason, Act, Observe) - Request-response agents with Claude Agent SDK and OpenAI Agents SDK - RAG implementation with FAISS and pgvector for retrieval - Building your own minimal agent framework (~140 lines of code) - Multi-agent coordination patterns (sequential, parallel, hierarchical) - Framework comparison: Autogen, LangGraph, OpenAI Swarm - When to use agents vs. simple prompts and common pitfalls

- How to automate the entire SDLC with AI agents - Techniques for making AI agents feel natural in your workflow - Moving beyond test generation to full process automation - Strategies for shipping well-tuned processes instead of org charts - How to avoid common pitfalls when integrating AI into development

- How to build agents using the ReACT pattern (Reason, Act, Observe) - Request-response agents with Claude Agent SDK and OpenAI Agents SDK - RAG implementation with FAISS and pgvector for retrieval - Building your own minimal agent framework (~140 lines of code) - Multi-agent coordination patterns (sequential, parallel, hierarchical) - Framework comparison: Autogen, LangGraph, OpenAI Swarm - When to use agents vs. simple prompts and common pitfalls

You'll learn to build prompts and agentic applications with Python, master context engineering techniques, and develop advanced AI integration skills for technical applications.

- Write effective system prompts for regularized outputs or tool use - Design and implement agents capable of using, creating, and managing tools - Develop agents with autonomous action capabilities, including scheduling and event-triggered responses - Utilize open-source tool hubs designed for Large Language Models - Manage and economically host large vector stores - Construct self-improving agents that can evolve their prompts - Create and manage swarms of agents collaborating on complex goals - Design meta-swarms and information hierarchies for advanced agent collaboration and secrecy - Evaluate and create benchmarks for LLM performance analysis