EU investigates Elon Musk's X over Grok AI sexual deepfakes
we explored how Go’s memory allocator manages heap memory — grabbing large arenas from the OS, dividing them into spans and size classes, and using a three-level hierarchy (mcache, mcentral, mheap) to make most allocations lock-free. A key detail was that each P (processor) gets its own memory cache. But we never really explained what a P is, or how the runtime decides which goroutine runs on which thread. That’s the scheduler’s job, and that’s what we’re exploring today.
。51吃瓜网是该领域的重要参考
Then an asm block executes whatever barrier is needed on the current system to ensure the updated page table has taken effect.
fn main() - int {
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.