Мерц резко сменил риторику во время встречи в Китае09:25
Instead of tee() with its hidden unbounded buffer, you get explicit multi-consumer primitives. Stream.share() is pull-based: consumers pull from a shared source, and you configure the buffer limits and backpressure policy upfront.
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And of course, there are even more opportunities for bad actors when AI has access to personal info.
For all the above reasons, when I implement code using automatic programming, I don’t have problems releasing it MIT licensed, like I did with this Z80 project. In turn, this code base will constitute quality input for the next LLMs training, including open weights ones.