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Installing the RNDIS driver on Windows 11 to use USB Raspberry Pi as network attached

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I do a lot of my development and configuration via ssh into my Raspberry Pi Zero over the RNDIS connection. Some models of the Raspberry PIs can be configured with  gadget drivers  that let the Raspberry pi emulate different devices when plugged into computers via USB. My favorite gadget  is the network profile that makes a Raspberry Pi look like an RNDIS-attached network device.  All types of network services travel over an RNDIS device without knowing it is a USB hardware connection. A Raspberry Pi shows up as a Remote NDIS (RNDIS) device when you plug the Pi into a PC or Mac via a USB cable. The gadget  in the Windows Device Manager picture shows this RNDIS Gadget connectivity between a Windows machine and a Raspberry Pi. The Problem Windows 11 and Windows 10 no longer auto-installs the RNDIS driver that makes magic happen. Windows recognizes that the Raspberry Pi is some type of generic USB COM device. Manually running W indows Update  or  Upd...
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Understanding your WSL2 RAM and swap - Changing the default 50%-25%

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The Windows Subsystem for Linux operates as a virtual machine that can dynamically grow the amount of RAM to a maximum set at startup time.  Microsoft sets a default maximum RAM available to 50% of the physical memory and a swap-space that is 1/4 of the maximum WSL RAM.  You can scale those numbers up or down to allocate more or less RAM to the Linux instance. The first drawing shows the default WSL memory and swap space sizing. The images below show a developer machine that is running a dev environment in WSL2 and Docker Desktop.  Docker Desktop has two of its own WSL modules that need to be accounted for.  You can see that the memory would actually be oversubscribed, 3 x 50% if every VM used its maximum memory.  The actual amount of memory used is significantly smaller allowing every piece to fit. Click to Enlarge The second drawing shows the memory allocation on my 64GB laptop. WSL Linux defaul...
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Micro benchmarking Apple M1 Max - MLX vs GGUF - LLM QWEN 2.5

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MLX is an ML framework targeted at Apple Silicon.  It provides noticeable ML performance gains when compared to the standard (GGUF) techniques running on Apple Silicon.  This MLX project describes MLX as:   MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research. A notable difference from MLX and other frameworks is the   unified memory model . Arrays in MLX live in shared memory. Operations on MLX arrays can be performed on any of the supported device types without transferring data. LM Studio added support for Apple Silicon MLX models in 2024 . I totally ignored it until I saw a 2025/02 Reddit post in the /r/ocallama subreddit .  I wanted to execute their microbenchmark on my Mac to get a feel for the possible performance difference.  The performance improvement is exciting.  I am waiting on really jumping into the MLX until Ollama supports MLX something they are working on as of 2025/0...
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