In the months since, I continued my real-life work as a Data Scientist while keeping up-to-date on the latest LLMs popping up on OpenRouter. In August, Google announced the release of their Nano Banana generative image AI with a corresponding API that’s difficult to use, so I open-sourced the gemimg Python package that serves as an API wrapper. It’s not a thrilling project: there’s little room or need for creative implementation and my satisfaction with it was the net present value with what it enabled rather than writing the tool itself. Therefore as an experiment, I plopped the feature-complete code into various up-and-coming LLMs on OpenRouter and prompted the models to identify and fix any issues with the Python code: if it failed, it’s a good test for the current capabilities of LLMs, if it succeeded, then it’s a software quality increase for potential users of the package and I have no moral objection to it. The LLMs actually were helpful: in addition to adding good function docstrings and type hints, it identified more Pythonic implementations of various code blocks.
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Anthropic says the change was motivated by a "collective action problem" stemming from the competitive AI landscape and the US's anti-regulatory approach. "If one AI developer paused development to implement safety measures while others moved forward training and deploying AI systems without strong mitigations, that could result in a world that is less safe," the new RSP reads. "The developers with the weakest protections would set the pace, and responsible developers would lose their ability to do safety research and advance the public benefit."
Among the 19 people killed in the capital, Kathmandu, on 8 September last year was a teenager in school uniform, who had been walking away from the crowd when he was shot in the back of the head. Dozens more were injured.