python scripts/convert_nemo.py checkpoint.nemo -o model.safetensors --model 600m-tdt
By the 2030s, Sophia hopes to be building larger space data centers out of thousands of TILEs, envisioning a 50-meter-by-50-meter structure delivering 1 MW of computing power. DeMillo argues that attempting to build space data centers with less efficient systems will not be economical and that a single structure rather than a distributed network linked by lasers will be easier to execute.
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虽然豆包手机出师未捷身先残 ，但更深层次的思考是：既然 AI Agent 通过通过视觉感知（看屏幕）和模拟操作（点屏幕）就可以达到一切目的。那么 AI Agent 的载体可以是手机，也应该会有其他的形态吧？，这一点在爱思助手下载最新版本中也有详细论述
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.
Why did the committee come to that conclusion?