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.
“坚持精准扶贫方略,用发展的办法消除贫困根源”,从打赢脱贫攻坚战到巩固拓展脱贫攻坚成果,“一把钥匙开一把锁”,一以贯之。
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因此,不同于大厂往“超级入口”的方向高歌猛进,小厂将资源集中在某一细分领域,Kimi则在“做生态”和“做工具”之间选择了一条中间路径——切入AI原生应用开发生态,成为专业用户开发“高价值任务Agent”的工具,构建出一个更高技术壁垒的生态位。
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