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.
首先，大模型本身无法主动感知，只能对输入被动响应。智能体需要用外部感知组件来主动获取环境信息。对于数字世界的任务，通过智能体工程可以建立基于时间的触发器，定期检查日志、邮件、股价变动等；或基于事件的订阅、监听，接收API推送的事件通知，或当数据库发生变更时自动唤醒记录数据。在物理世界中，智能体还可以通过传感器、摄像头、麦克风等设备采集视觉、听觉、触觉等信号。
，更多细节参见同城约会
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"The policy environment has shifted toward prioritizing AI competitiveness and economic growth, while safety-oriented discussions have yet to gain meaningful traction at the federal level," the company wrote. "We remain convinced that effective government engagement on AI safety is both necessary and achievable, and we aim to continue advancing a conversation grounded in evidence, national security interests, economic competitiveness, and public trust. But this is proving to be a long-term project—not something that is happening organically as AI becomes more capable or crosses certain thresholds."