一场淘金热，以这个奇怪的方式，悄悄在北京、上海、深圳的各个角落开始了。
一是国家创新体系效能不断提升。提高国家战略科技力量效能，组织实施一批重大科技项目。完善国家实验室体系，支持国家实验室建设重大科研平台，提升体系化攻关能力。提升国家重大科技基础设施规划布局、建设管理、开放运行、协同创新水平，44个设施已建成运行，推动产出“国产高性能超导磁共振成像设备”、“全超导托卡马克核聚变实验装置（EAST）创造‘亿度千秒’世界纪录”、“‘祖冲之三号’量子计算原型机领跑全球”等一批标志性成果。加强国家级科技创新平台基地建设，推动国家新兴产业创新中心加快建设，启动国家产业技术工程化中心优化重组。建设全国高校区域技术转移转化中心。充分发挥科技领军企业作用，支持企业牵头或参与国家重大科技项目，推动企业主导的产学研融通创新。
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12月22日，一群野鸭和鸳鸯聚集在北海公园太液池上。图/IC photo
第四，强化检察监督。加强刑事、民事、行政检察基本职能建设，提高法律监督精准度、力度和深度。加强对查封、扣押、冻结等强制措施的法律监督。强化对执行活动的全程监督。依法规范适用认罪认罚从宽制度。健全检察环节司法公正实现和评价机制，完善检察办案质效评价标准，构建高质效办案制度机制体系。完善检察权运行内外部制约监督机制。
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?