В Белом доме ответили на вопрос о снятии санкций с России00:46
Немецкий чиновник отказался участвовать в выборах и выиграл их14:47
，更多细节参见雷电模拟器
此外，大会秘书处已收到代表建议7000余件。代表建议较为集中在：纵深推进全国统一大市场建设，着力建设强大国内市场。加紧培育壮大新动能，培育壮大新兴产业和未来产业。加快高水平科技自立自强，推进关键核心技术攻坚。持续深化重点领域改革，推进法治化营商环境建设。扎实推进乡村全面振兴，推动乡村特色产业提质增效。推动新型城镇化和区域协调发展，全面践行人民城市理念。推进民族地区高质量发展，夯实中华民族共同体意识基础。更大力度保障和改善民生，强化基本医疗卫生服务，加强优秀传统文化保护与传承。加快推动全面绿色转型，筑牢“绿水青山”安全屏障。加强重点领域风险防范化解和安全能力建设，建设更高水平平安中国。
As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?