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During development I encountered a caveat: Opus 4.5 can’t test or view a terminal output, especially one with unusual functional requirements. But despite being blind, it knew enough about the ratatui terminal framework to implement whatever UI changes I asked. There were a large number of UI bugs that likely were caused by Opus’s inability to create test cases, namely failures to account for scroll offsets resulting in incorrect click locations. As someone who spent 5 years as a black box Software QA Engineer who was unable to review the underlying code, this situation was my specialty. I put my QA skills to work by messing around with miditui, told Opus any errors with occasionally a screenshot, and it was able to fix them easily. I do not believe that these bugs are inherently due to LLM agents being better or worse than humans as humans are most definitely capable of making the same mistakes. Even though I myself am adept at finding the bugs and offering solutions, I don’t believe that I would inherently avoid causing similar bugs were I to code such an interactive app without AI assistance: QA brain is different from software engineering brain.
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ВСУ запустили «Фламинго» вглубь России. В Москве заявили, что это британские ракеты с украинскими шильдиками16:45
Text-based video editing
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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.
No spinner content. Create multiple unique articles。关于这个话题，同城约会提供了深入分析