The DeepSeek-Prover-V2 model represents a significant advancement in neural theorem proving, with a remarkable 88.9% pass ratio on the MiniF2F-test and an impressive capability of solving 49 out of 658 problems from PutnamBench. Users have noted that the cold-start training procedure, which allows the model to break complex problems into smaller subgoals, is akin to project management techniques often taught to engineering students. This modular approach could greatly enhance problem-solving in both mathematical and coding contexts. Furthermore, the static weights of the model ensure reliability across updates, contrasting with some models that may become unusable after changes. There is an expectation that future developments might see a suite of expert LLMs that can be used together, facilitating task delegation based on individual model strengths and providing even more sophisticated problem-solving capabilities in various domains.