add more info about selecting software

setup.md

@@ -53,7 +53,8 @@ There are a few necessary components to a Local LLM stack. There's more technica
    }
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-## Model Selection
+## Stack Selection & Technical Details
+### Model Selection
 Obviously, model selection is crucial here - some models will be better than others depending on the architecture, number of parameters, desired use-cases, and whether they can even run on your hardware. 
 
 I want models to be good at a few main things. These don't have to all be fulfilled by the same model. 
@@ -61,3 +62,21 @@ I want models to be good at a few main things. These don't have to all be fulfil
 2. General knowledge - I mostly use LLMs for cooking (science, not recipes), but other domains would be helpful too. 
     * One other thing to look into is Retrieval-Augmented Generation (RAG). Modern big tech models sometimes use web searches to retrieve up-to-date information, but I might want to, say, point it to my library of cooking books. 
 3. Multi-language - I'd like to learn some more Spanish, and having a personal tutor will be great for an intro level. 
+
+I chose Qwen3-coder primarily for coding, given its name. It scores well on benchmarks and doesn't support chain-of-thought reasoning. I chose Qwen3.8:27b for everything else because it's just about the right size for my hardware and performs well on a wide variety of tasks. The model weights are slightly too large to fit entirely in my 16GB VRAM, but I have enough system RAM (64GB) and a fast enough CPU that I can still get good performance by offloading some of the model to CPU/system RAM. 
+
+### Inference Engine
+There are three main inference engines that I'm aware of at the time of writing: Ollama, llama.cpp, and vLLM. 
+
+Ollama is widely regarded as the easiest inference engine to set up; my experience with it has been "curl the install script and go" every time I've set it up. However, this smoothness comes with the cost of having less control over how LLMs are run. It's a good place to start, but for more control I'd choose something else. 
+
+llama.cpp provides more control over how models are run, quantized, and distributed among resources - it's able to set up custom GPU/CPU offloading ratios and run models with more parameter customization. For limited hardware, I'd like to explore this more in the future. 
+
+vLLM is optimized for running multiple models simultaneously, usually with multiple GPUs and multuple users. This is probably a little overkill for what we need. 
+
+### Chat UI
+There are lots of options for a chat UI, and I tried quite a few. One thing that I really dislike is when software treats me like an idiot by abstracting away too much of what's going on under the hood. Many of the desktop chat UIs do that; they're too polished and don't give me any control. For example: AnythingLLM, Jan, and (to some extent) Cherry Studio. Several of them even try to manage their own inference engine stack. Web browser UIs are better about this. I tried both LibreChat and OpenWebUI, finding problems with both of them. LibreChat requires registering with an email address, which I find a little ridiculous. It also requires a lot of editing config files to add and update models - not a big deal for me, but that is an obstacle for someone less technical, and it's more brittle. OpenWebUI had a slightly smoother setup, but still felt over-engineered and had some minor annoyances; they're trying to shoehorn AI prompt suggestions into the UI. I also tried LM Studio a while back, and had a mostly positive experience, from what I remember - I'll probably revisit that again some other time. However, it's not open-source. 
+
+### Agent Harness
+There are lots of options here: OpenCode, OpenClaw, Qwen Code, Claude Code, etc. I chose OpenCode because I'd heard of it before. This is something to play around with more in the future, if I ever care enough - I don't see myself doing much agentic coding. 
+