The paper introduces bGPT, a model designed to process and simulate the digital world through next byte prediction. Traditional deep learning models often overlook the fundamental role of bytes in digital data, which are the basic units of all digital information and operations. bGPT addresses this by directly working with binary data, enabling it to simulate and understand various aspects of the digital world, including digital media files and complex algorithms and hardware operations. bGPT is structured as a hierarchical Transformer model that segments byte sequences into patches to manage computational efficiency. It consists of a linear projection layer, a patch-level decoder, and a byte-level decoder. The model is trained on generative modeling tasks, such as predicting the next byte in a sequence, and classification tasks, where it predicts categories from byte sequences. Experiments demonstrate that bGPT performs well across various tasks, including digital media processing and algorithm and hardware simulation. It achieves competitive performance with specialized models in tasks like text generation and classification, and shows strong scalability in handling large datasets of binary data. Notably, bGPT excels in data conversion tasks, such as converting symbolic music from ABC notation to MIDI, achieving a low error rate of 0.0011 bits per byte. It also demonstrates exceptional capabilities in simulating CPU behavior, with an accuracy exceeding 99.99% in executing various operations. The paper concludes by highlighting the potential of bGPT in advancing cybersecurity, software diagnostics, data compression, and reverse-engineering of software. However, it also raises ethical concerns, particularly regarding the potential for unauthorized access and modification of proprietary software. Future research directions include reducing computational costs, scaling models to handle larger datasets, and improving performance in underexplored tasks involving native binary data.The paper introduces bGPT, a model designed to process and simulate the digital world through next byte prediction. Traditional deep learning models often overlook the fundamental role of bytes in digital data, which are the basic units of all digital information and operations. bGPT addresses this by directly working with binary data, enabling it to simulate and understand various aspects of the digital world, including digital media files and complex algorithms and hardware operations. bGPT is structured as a hierarchical Transformer model that segments byte sequences into patches to manage computational efficiency. It consists of a linear projection layer, a patch-level decoder, and a byte-level decoder. The model is trained on generative modeling tasks, such as predicting the next byte in a sequence, and classification tasks, where it predicts categories from byte sequences. Experiments demonstrate that bGPT performs well across various tasks, including digital media processing and algorithm and hardware simulation. It achieves competitive performance with specialized models in tasks like text generation and classification, and shows strong scalability in handling large datasets of binary data. Notably, bGPT excels in data conversion tasks, such as converting symbolic music from ABC notation to MIDI, achieving a low error rate of 0.0011 bits per byte. It also demonstrates exceptional capabilities in simulating CPU behavior, with an accuracy exceeding 99.99% in executing various operations. The paper concludes by highlighting the potential of bGPT in advancing cybersecurity, software diagnostics, data compression, and reverse-engineering of software. However, it also raises ethical concerns, particularly regarding the potential for unauthorized access and modification of proprietary software. Future research directions include reducing computational costs, scaling models to handle larger datasets, and improving performance in underexplored tasks involving native binary data.