德国电气与电子行业研发实力突出，目前拥有约9万名研发人员，70%的企业定期开展产品或流程创新，年均专利申请量达1.3万项，推动德国制造业1/3的创新成果落地。当前，德国正加快工业人工智能在电气与电子行业的应用，依托深厚的工业基础挖掘工业数据红利。企业层面，西门子研发工业人工智能产品与解决方案，推出智慧能碳管理平台Smart ECX，帮助企业监测能耗数据、管理碳足迹；博世集团从传统汽车供应商转型为领先的传感器、人工智能解决方案和互联产品供应商；软件企业SAP打造的平台，为物联网技术与工业应用的融合提供了支撑。
Git packfiles use delta compression, storing only the diff when a 10MB file changes by one line, while the objects table stores each version in full. A file modified 100 times takes about 1GB in Postgres versus maybe 50MB in a packfile. Postgres does TOAST and compress large values, but that’s compressing individual objects in isolation, not delta-compressing across versions the way packfiles do, so the storage overhead is real. A delta-compression layer that periodically repacks objects within Postgres, or offloads large blobs to S3 the way LFS does, is a natural next step. For most repositories it still won’t matter since the median repo is small and disk is cheap, and GitHub’s Spokes system made a similar trade-off years ago, storing three full uncompressed copies of every repository across data centres because redundancy and operational simplicity beat storage efficiency even at hundreds of exabytes.
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