Жители Санкт-Петербурга устроили «крысогон»17:52
2026-02-26 00:00:00:0 拥有全球60%人工智能专利、约2/3机器人相关专利。业内人士推荐safew官方下载作为进阶阅读
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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.
As the founding member of the backend team, I worked to establish the underlying technical architecture that powers the persistent live components of the game. As the backend team grew, we built numerous C# microservices running in Kubernetes hosted on Azure. Viewing this as a long-term live-service game, we designed our systems with that in mind. Multiple region-aware matchmaking flows. An internal web portal for customer support. Player reporting and moderation systems. Cross-platform account linking. Login queues. Extensive load testing. The list goes on and on.