Your content is only as good as it presents 💬 I've had plenty of blog articles that I thought were good enough to put out on the Internet. It was only after I tried to use them as a basis for a video that I realized how chaotic or incomplete they were. AI-Assisted Timeline 2 hours wall clock getting an LLM to create outlines for 6 blog articles 1 hour to generate a blog article from the outline 2 wall clock hours of iteration and ideation and proofreading 4 days of coffee decompression Return to project 6 wall clock hours of iteration with the AI and proofreading Published the article. Immediately found problems 2 wall clock hours of iteration and republished 1 day later LLM found problems while working on other article. 1 wall clock hour of proofreading and iteration 4 days of other stuff including coffee decompression Started video recording Unrecorded hours of swearing 1 wall clock hour of iteration and republished Unrecorded hours of moaning and banging head on table. 2 wall ...
This article was revised with later test data. Earlier figures and tables on this page were replaced. Ollama's prompt cache let nine of seventeen local builds start nearly every new conversation without re-evaluating the shared system prompt. Four others re-evaluated a 1,024-token batch of it each time. Four more re-evaluated all of it once per system prompt, up to 37 seconds on the largest. The model's memory type and runner set the group, not the prompt. In freemansoft/Flutter-AdaptiveCards a demonstration Dart chat server asks a local Ollama model for an answer as Adaptive Card JSON. That is a strict, closed-vocabulary schema, which a Flutter app renders as interactive UI. The card system prompt that describes the element vocabulary runs to about 3,800 to 4,400 tokens, by the model's tokenizer. The chat server replays up to ten prior exchanges on every turn. Each Ollama runner keeps a prefix cache, so a request whose opening tokens match an earlier one skips proces...
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...