havent seen many people outside anthropic ultracode yet. this thing is scarily good at burning tokens but you need to set up your repo to parallelize properly to make use of the fanout that i think subagents are best at. basically the idea is "subroutines but intelligent". when you undersatnd just how much knowledge work is just yakshaves after yakshaves that require some judgment and intelligence, you start to appreciate that dynamic workflows are not just for coding tasks...
还没看到很多 Anthropic 之外的人在用 ultracode。这个东西在烧 token（令牌）方面强得吓人，但你得把自己的 repo 配置好、正确并行化，才能利用我认为 subagents 最擅长的那种 fanout（扇出）能力。基本思路就是“subroutines（子程序），但有智能”。当你理解了有多少知识型工作其实只是一个接一个的 yak shave（为次要前置问题反复折腾），而这些事又确实需要一定判断力和智能时，你就会开始意识到，dynamic workflows（动态工作流）并不只是适用于编码任务……
Satya on loops as IP: > This is the first time we can create a real cognitive loop between people and digital systems. That is a mind-bender, because it changes how we even conceptualize work inside an enterprise. > This means the real opportunity is not in picking the best model but instead in building a learning loop on top of models where human capital and token capital compound. You can offload a task, or even a job, but you can never offload your learning > In my view, our priority has to be building a frontier ecosystem, not just a frontier model, so value flows broadly across every company, every industry, and every country. One where every organization can own the learning loop that encodes its institutional knowledge, compounding its human and token capital.
Satya 谈把 loops（循环）作为 IP（知识产权/核心资产）：> 这是我们第一次能够在人和数字系统之间创造一个真正的 cognitive loop（认知循环）。这很颠覆认知，因为它改变了我们甚至如何去概念化企业内部的工作。> 这意味着，真正的机会不在于挑出最好的 model（模型），而在于在模型之上建立一个 learning loop（学习循环），让 human capital（人力资本）和 token capital（token 资本）产生复利。你可以外包一个任务，甚至一份工作，但你永远无法外包自己的学习。> 在我看来，我们的优先事项必须是构建一个 frontier ecosystem（前沿生态系统），而不只是一个 frontier model（前沿模型），这样价值才能广泛流向每一家公司、每一个行业和每一个国家。在这样的生态里，每个组织都能拥有那个编码其 institutional knowledge（机构知识）的 learning loop，并让其 human capital 和 token capital 持续复利增长。
@DevinAI link here!
@DevinAI 链接在这！