If the transform's transform() operation is synchronous and always enqueues output immediately, it never signals backpressure back to the writable side even when the downstream consumer is slow. This is a consequence of the spec design that many developers completely overlook. In browsers, where there's only a single user and typically only a small number of stream pipelines active at any given time, this type of foot gun is often of no consequence, but it has a major impact on server-side or edge performance in runtimes that serve thousands of concurrent requests.
Companies like SpaceX, Google, or Starcloud are examining traditional satellite form factors for their proposed space data center constellations, which rely on large radiators to keep chips in optimal thermal condition. But Sophia Space’s founders — CTO Leon Alkalai, CEO Rob DeMillo, and chief growth officer Brian Monnin — have a different approach.
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Around this time, my coworkers were pushing GitHub Copilot within Visual Studio Code as a coding aid, particularly around then-new Claude Sonnet 4.5. For my data science work, Sonnet 4.5 in Copilot was not helpful and tended to create overly verbose Jupyter Notebooks so I was not impressed. However, in November, Google then released Nano Banana Pro which necessitated an immediate update to gemimg for compatibility with the model. After experimenting with Nano Banana Pro, I discovered that the model can create images with arbitrary grids (e.g. 2x2, 3x2) as an extremely practical workflow, so I quickly wrote a spec to implement support and also slice each subimage out of it to save individually. I knew this workflow is relatively simple-but-tedious to implement using Pillow shenanigans, so I felt safe enough to ask Copilot to Create a grid.py file that implements the Grid class as described in issue #15, and it did just that although with some errors in areas not mentioned in the spec (e.g. mixing row/column order) but they were easily fixed with more specific prompting. Even accounting for handling errors, that’s enough of a material productivity gain to be more optimistic of agent capabilities, but not nearly enough to become an AI hypester.
Screenshot has updated icons and labels for modes