Mistral's newly announced 7B parameter Codestral Mamba model introduces an innovative approach to AI architecture by combining the efficiency of Recurrent Neural Networks (RNNs) with the comprehensive memory capabilities of Transformers, overcoming the latter's quadratic bottleneck issue. Known as a Selective State Space Model (SSM), Mamba achieves state-of-the-art performance across various modalities such as language, audio, and genomics by selectively retaining essential information and allowing for linear scaling with sequence length. This architecture not only matches or surpasses the performance of similarly sized Transformer models but also outperforms larger ones, offering a new paradigm in sequence processing that enhances efficiency and scalability without sacrificing long-term memory capabilities. As AI engineers explore next-generation language models and complex data processing, Mamba's selective efficiency opens new possibilities for computationally challenging tasks, demonstrating that advanced AI solutions sometimes require more than attention-based architectures.