Merge pull request #2002 from jasonrandrews/spelling

Spelling updates

Author: jasonrandrews

.wordlist.txt

@@ -4184,4 +4184,20 @@ subgenre
 submodule
 subword
 techcrunch
-transformative
+transformative
+Aude
+Gian
+Iodice
+SmolLM
+VME
+Vuilliomenet
+cpus
+fLO
+invalidations
+libtensorflowlite
+macos
+multithreaded
+Wix's
+ngrok's
+qs
+qu

content/learning-paths/cross-platform/mcp-ai-agent/intro-to-mcp-uv.md

@@ -17,7 +17,7 @@ The Model Context Protocol (MCP) is an open specification designed to connect La
 
 - **Security by design:** MCP encourages running servers inside your own infrastructure, so sensitive data stays within your infrastructure unless explicitly shared.
 
-- **Cross-ecosystem momentum:** recent roll-outs from an official C# SDK to Wix’s production MCP server and Microsoft’s Azure support show the MCP spec is gathering real-world traction.
+- **Cross-ecosystem momentum:** recent roll-outs from an official C# SDK to Wix's production MCP server and Microsoft’s Azure support show the MCP spec is gathering real-world traction.
 
 ## What is uv?
 

content/learning-paths/cross-platform/mcp-ai-agent/mcp-server.md

@@ -115,7 +115,7 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
 
 You will now use ngrok to expose your locally running MCP server to the public internet over HTTPS.
 
-1. Add ngrok’s repo to the apt package manager and install:
+1. Add ngrok's repo to the apt package manager and install:
 ```bash
 curl -sSL https://ngrok-agent.s3.amazonaws.com/ngrok.asc \
   | sudo tee /etc/apt/trusted.gpg.d/ngrok.asc >/dev/null \

content/learning-paths/mobile-graphics-and-gaming/run-stable-audio-open-small-with-lite-rt/3-converting-model.md

@@ -88,7 +88,7 @@ python3 ./scripts/export_conditioners.py --model_config "$WORKSPACE/model_config
 ```
 
 
-After successful conversion, you now have a `conditioners_tflite` directory containing models with different precisions (e.g., float16, float32).
+After successful conversion, you now have a `conditioners_tflite` directory containing models with different precision (e.g., float16, float32).
 
 You will be using the float32.tflite model for on-device inference.