European go-to-market search firm Nobel Recruitment has acquired Berlin-based ARRtist, a practitioner-led tech community platform for founders, C-level executives and investors. The deal strengthens Nobel’s position in Germany while expanding its reach beyond executive search into community building and ecosystem development. Financial terms were not disclosed. Founded more than four years ago, ARRtist built a […]
As space companies itch to push the most advanced chips into orbit, the problem of cooling those high-powered processors is top of mind.
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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.
Any point within a given Voronoi region is proximal to the data site (black point) associated with that region.
"success": true,