Раскрыты подробности похищения ребенка в Смоленске09:27
One important thing to note about Wi-Fi range extenders (also sometimes called “repeaters”) is that most of them actually create a new Wi-Fi network when rebroadcasting your existing one. That network will have a new name (it’ll often be your default network’s name with an EXT appended at the end, unless you change it) and that means you’ll have to connect to different networks when in different parts of your home. While that’s a small tradeoff in return for improved internet connection, some will be more inconvenienced than others.
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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.
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.
The overview page covered how to use various SDF font libraries (msdfgen, stb_truetype, tiny-sdf, etc.). I wrote code for multiple libraries, had sketched out diagrams for various concepts, and had screenshots of outputs from each of those libraries.