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There are a couple ways mitigate this drawback, both of which are outside the scope of this article. One is “garbage collection”: pruning tombstones from CRDTs, which prevents you from merging states with any changes made before the tombstones were removed. Another is creating an efficient format to encode the data. You can also combine these methods. Research suggests that this can result in as little as 50% overhead compared to the “plain” data CRDTs: The Hard Parts A talk on the latest research on CRDTs, originally given at the Hydra distributed computing conference on 6 July 2020.References: https://martin.kleppmann.co... youtu.be/x7drE24geUw?t=3587 . If you’d like to skip ahead and see some of this optimization in action, check out the final part in this series: Making CRDTs 98% More Efficient Making CRDTs 98% More Efficient | jakelazaroff.com State-based CRDTs grow monotonically, but that doesn't mean they can't be efficient. We'll learn how to compress the pixel editor state by 98%. jakelazaroff.com/words/making-crdts-98-percent-more-efficient/ . ↩
Precompiled Function Blobs
数据显示，截至2026年2月11日，中证人工智能主题指数(930713)前十大权重股分别为中际旭创、新易盛、寒武纪-U、澜起科技、科大讯飞、中科曙光、海康威视、豪威集团、芯原股份、金山办公，前十大权重股合计占比55.72%。（以上所列股票仅为指数成份股，无特定推荐之意，历史持仓不代表现在和未来。）