That’s a similar amount of CPU usage as when we started - but I’m running with 250 users, not 10. 25 times faster isn’t bad. With this setup, I’m able to support about 2,500 concurrent users before I start to see any stuttering.
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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My base layout is very close to Halmak. I modified a couple of characters in the center columns to better align frequency with ease of reach on a columnar layout. I use home row mods for my home keys, and all of my thumb keys are dual-function keys with layer switching hold actions.