The self-driving revolution is transforming the landscape with a combination of hardware, software, and strategy. Powerful GPUs are driving innovation by enabling rapid model training and scaling. Open-source libraries have improved significantly, streamlining workflows and allowing for faster experimentation. Self-driving datasets are vast and complex, requiring tools like FiftyOne to organize and manage them effectively. Top-tier talent is behind the scenes, with companies like Waymo, Wayve, and Tesla leading the charge. Strategies for self-driving success vary among companies, including focusing on individual cities or relying solely on image-based systems. Beginner techniques include curation, digitization, and dataset management, which are crucial for organizing unstructured data and scaling. Pretrained models, such as SAM2, can recognize real-world objects without human annotations, while embeddings help identify hidden patterns in the data and solve challenges like finding unique or outlier samples. The power of embeddings is that they enable similarity search, refining the dataset to ensure model training efficiency. Real-world applications include tackling labeling mistakes and improving model performance with tools like SAM2 and Depth Anything. Experts are pushing the limits with simulation techniques, building controlled environments for testing and validating self-driving models in a fraction of the time.