There’s a secondary pro and con to this pipeline: since the code is compiled, it avoids having to specify as many dependencies in Python itself; in this package’s case, Pillow for image manipulation in Python is optional and the Python package won’t break if Pillow changes its API. The con is that compiling the Rust code into Python wheels is difficult to automate especially for multiple OS targets: fortunately, GitHub provides runner VMs for this pipeline and a little bit of back-and-forth with Opus 4.5 created a GitHub Workflow which runs the build for all target OSes on publish, so there’s no extra effort needed on my end.
create code from natural language descriptions of software tasks. The system is
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（四）被依法执行管制、剥夺政治权利或者在缓刑、暂予监外执行中的罪犯或者被依法采取刑事强制措施的人，有违反法律、行政法规或者国务院有关部门的监督管理规定的行为的。。关于这个话题，搜狗输入法2026提供了深入分析
The spec does not mandate buffer limits for tee(). And to be fair, the spec allows implementations to implement the actual internal mechanisms for tee()and other APIs in any way they see fit so long as the observable normative requirements of the specification are met. But if an implementation chooses to implement tee() in the specific way described by the streams specification, then tee() will come with a built-in memory management issue that is difficult to work around.