Finding software bugs is like searching for a needle in a haystack. Fuzzing, a technique that throws random inputs at software to make it crash, helps, but it needs good starting points. Think of it like trying to find a specific location in a vast forest—you wouldn't just start wandering aimlessly. You'd need a map or some initial clues. In fuzzing, those clues are called "seeds." Researchers have traditionally crafted these seeds by hand, a tedious and time-consuming process. But what if AI could help? A new system called SeedMind is doing just that. It uses large language models (LLMs), the same technology behind chatbots like ChatGPT, to generate these crucial seeds automatically. Instead of directly creating test cases, SeedMind takes a clever approach: it asks the LLM to write a program that generates the seeds. This sidesteps a key limitation of LLMs: their inability to handle the diverse formats of software inputs, which can range from text and images to complex binary data. SeedMind also addresses the limited "memory" of LLMs by feeding them only the most relevant code snippets. It uses a feedback loop, guiding the LLM to refine its seed-generating program iteratively. The results are impressive. SeedMind generates seeds that are comparable to, and in many cases even better than, those created by humans. When tested on real-world software, these AI-generated seeds helped uncover bugs faster and more effectively than existing methods. While promising, SeedMind still has room for improvement. The quality of the seeds depends on the specific LLM used, and cost can be a factor. However, this research opens exciting new avenues for automating software testing and making our digital world more secure. The future of bug hunting may just be powered by AI.