Integrating Large Language Models (LLMs) with traditional solving techniques creates new synergies in automated reasoning. This talk explores both (i) how LLMs can enhance SAT and constraint solving through structural analysis and search guidance and (ii) how formal reasoning can help LLMs tackle hard reasoning and optimization problems. We will present case studies exploring the practical advances and future potential of combining neural and symbolic approaches in computational reasoning.