Large Language Models (LLMs) are transforming software creation by enabling zero-code development platforms. This survey reviews recent platforms that let users build applications without writing code, by leveraging LLMs as the “brains” of the development process. We adopt a broad survey methodology, categorizing platforms based on key dimensions such as interface style, backend integration, output type, and extensibility. We cover both dedicated LLM-based app builders (e.g., OpenAI’s custom GPTs, Bolt.new, Dust.tt, Flowise, Cognosys) and general no-code platforms (e.g., Bubble, Glide) that integrate LLM capabilities. We present a taxonomy categorizing these platforms by their interface (conversational, visual, etc.), supported LLM backends, output type (chatbot, full application, workflow), and degree of extensibility. Core features such as autonomous agents, memory management, workflow orchestration, and API integrations are examined. We provide a detailed comparison, highlighting each platform’s strengths and limitations. Trade-offs (customizability, scalability, vendor lock-in) are discussed in comparison with traditional and low-code development approaches. Finally, we outline future directions—including multimodal interfaces, on-device LLMs, and improved orchestration for democratizing app creation with AI. Our findings indicate that while zero-code LLM platforms greatly lower the barrier to creating AI-powered applications, they still face challenges in flexibility and reliability. Overall, the landscape is rapidly evolving, offering exciting opportunities to empower non-programmers to create sophisticated software.