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/ . ↩
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After the news broke out about OpenAI’s deal, Anthropic climbed its way to the number one spot of the App Store's Top Free Apps leaderboard, beating out both ChatGPT and Google Gemini. Anthropic, capitalizing on Claude’s sudden popularity, launched a memory import tool to make switching to its chatbot from another company’s easier. Meanwhile, uninstalls for ChatGPT’s jumped by 295 percent day-over-day, according to Sensor Tower.，推荐阅读爱思助手获取更多信息
Владимир Седов (Редактор отдела «Силовые структуры»)
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