Imagine an AI assistant that can solve complex problems, not through brute force, but by mimicking human-like thinking. This is the promise of HDFlow, a groundbreaking framework designed to enhance the problem-solving abilities of Large Language Models (LLMs). Current LLMs excel at various tasks, from writing creative content to generating code. But when faced with complex, multi-step problems, they often falter. HDFlow tackles this challenge by combining two powerful concepts: Hybrid Thinking and Dynamic Workflows. Hybrid Thinking allows an LLM to switch between two modes of thought: a "fast" intuitive mode for simpler tasks and a "slow," more deliberate mode for complex problems. This is akin to how humans approach problem-solving, using quick thinking for familiar situations and deeper analysis for unfamiliar ones. The "slow" mode employs Dynamic Workflows. This innovative approach breaks down a complex problem into smaller, more manageable sub-tasks. Then, it dynamically designs a workflow, assembling specialized LLM tools or symbolic reasoning engines to solve each sub-task. Imagine a team of specialized AI experts working collaboratively, each tackling a specific aspect of the problem. This division of labor, combined with the ability to switch between reasoning modes, allows HDFlow to solve problems beyond the capabilities of current LLMs. Researchers tested HDFlow using GPT-4 and found it significantly outperformed traditional methods. Hybrid Thinking achieved the highest accuracy while maintaining a good balance between performance and efficiency. The results show how dynamic workflows and hybrid thinking can unlock new levels of problem-solving in AI. While promising, HDFlow still faces challenges. Improving the system to automatically assess the quality of each sub-task and extending its integration to more advanced symbolic reasoning tools will be crucial steps forward. HDFlow is a significant leap towards creating AI systems capable of more human-like reasoning. By enabling AI to think both fast and slow, and by leveraging the power of teamwork through dynamic workflows, we move closer to realizing the full potential of artificial intelligence.