Fix typo in README

README.md

@@ -39,7 +39,7 @@ That said, a few important things about this project:
 
 * The local inference landscape contains many excellent projects, but new models are released continuously, and the attention immediately gets captured by the next model to implement. This project takes a deliberately narrow bet: one model at a time, official-vector validation (logits obtained with the official implementation), long-context tests, and enough agent integration to know if it really works. The exact model may change as the landscape evolves, but the constraint remains: local inference credible on high end personal machines or Mac Studios, starting from 96/128GB of memory.
 * This software is developed with **strong assistance from GPT 5.5** and with humans leading the ideas, testing, and debugging. We say this openly because it shaped how the project was built. If you are not happy with AI-developed code, this software is not for you. The acknowledgement below is equally important: this would not exist without `llama.cpp` and GGML, largely written by hand.
-* This implementation is based on the idea that compressed KV caches like the one of DeepSeek v4 and the fast SSD disks of modern MacBooks should change our idea that KV cache belongs to RAM. **The KV cache is actually a first-class disk citizen**. Fast SSD disks also changed the inference game from the point of view of "model needs to fit RAM": while having more RAM the the model size is still preferred, SSD streaming allows to turn the available amount of RAM from a hard cutoff (can I run this model or not?) to continuous spectrum of speed levels.
+* This implementation is based on the idea that compressed KV caches like the one of DeepSeek v4 and the fast SSD disks of modern MacBooks should change our idea that KV cache belongs to RAM. **The KV cache is actually a first-class disk citizen**. Fast SSD disks also changed the inference game from the point of view of "model needs to fit RAM": while having more RAM than the model size is still preferred, SSD streaming allows to turn the available amount of RAM from a hard cutoff (can I run this model or not?) to continuous spectrum of speed levels.
 * Our vision is that local inference should be a set of three things working well together, out of the box: A) inference engine with HTTP API + B) GGUF specially crafted to run well under a given engine and given assumptions + C) testing and validation with coding agents implementations. D) Purpose built agents for specific models and execution environments. DwarfStar only runs with the GGUF files provided. It gets tested against officially obtained logits at different context sizes. This project exists because we wanted to make one local model feel finished end to end, not just runnable. However this is beta quality code, so probably we are not still there, especially since recently we introduced large new features: distributed inference, SSD streaming, and other minor improvements.
 * The optimized graph path targets **Metal on macOS** and **CUDA on Linux**. The CPU path is only for correctness checks and model/tokenizer diagnostics. For CPU-only Linux builds, use `make cpu`; it builds the normal `./ds4` and `./ds4-server` binaries without CUDA or Metal. On macOS, **warning: current macOS versions have a bug in the virtual memory implementation that will crash the kernel** if you try to run the CPU code. Remember? Software sucks. It was not possible to fix the CPU inference to avoid crashing, since each time you have to restart the computer, which is not funny. Help us, if you have the guts.