Update README.md

README.md

@@ -6,11 +6,16 @@ including converting, quantizing, compiling, running, benchmarking and
 visualizing LiteRT (TFLite) models on various hardware (CPU / GPU / NPU) across
 platforms (desktop, mobile, or cloud).
 
-🚀 [Installation](#-installation)  |  ⚡ [Quick start](#-quick-start)  |  💡 [Common commands](#-common-commands)
-<br>
-📓 [Try Colab](#-try-colab) &nbsp;|&nbsp; 🌟 [Quick demos](#-quick-demos) &nbsp;|&nbsp; 🤖 [Use in coding agent](#-use-in-coding-agent)
+🚀 [Installation](#-installation) | ⚡ [Quick start](#-quick-start) | 💡 [Common commands](#-common-commands) ｜
+📓 [Try Colab](#-try-colab) | 🌟 [Quick demos](#-quick-demos) | 🤖 [Use in coding agent](#-use-in-coding-agent)
 
-> [!NOTE] It's still an early preview under active development, thus has limited
+LiteRT CLI is built on top of [Google AI Edge](https://ai.google.dev/edge) stacks, including
+[LiteRT](https://github.com/google-ai-edge/LiteRT), [LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM),
+[LiteRT Torch](https://github.com/google-ai-edge/LiteRT-Torch), [AI Edge Quantizer](https://github.com/google-ai-edge/ai-edge-quantizer),
+[AI Edge Portal](https://ai.google.dev/edge/ai-edge-portal), and [Model Explorer](https://ai.google.dev/edge/model-explorer).
+
+> [!NOTE]
+> It's still an early preview under active development, thus has limited
 > platform and feature support, plus possible bugs. We appreciate your patience
 > and feedback to help us improve it. Welcome issues and PRs!
 
@@ -124,26 +129,25 @@ Add the LiteRT CLI skill
 into your coding agent (like [Google Antigravity](https://antigravity.google/))
 and try prompts such as:
 
-*   "Download LiteRT model `litert-community/efficientnet_b1` and run it on CPU"
-*   "Benchmark LiteRT model `litert-community/efficientnet_b1` on my Android
-    GPU"
-*   "Compile LiteRT model `litert-community/efficientnet_b1` for NPU target
-    `sm8750`"
-*   "Visualize LiteRT model `litert-community/efficientnet_b1`"
-*   "Download the FP32 EfficientNet model `litert-community/efficientnet_b1`
-    from HuggingFace. Quantize it to INT8 dynamic range (`--recipe
+*   *Download LiteRT model `litert-community/efficientnet_b1` and run it on CPU*
+*   *Benchmark LiteRT model `litert-community/efficientnet_b1` on my Android
+    GPU*
+*   *Compile LiteRT model `litert-community/efficientnet_b1` for NPU target
+    `sm8750`*
+*   *Visualize LiteRT model `litert-community/efficientnet_b1`*
+*   *Download the FP32 model `litert-community/efficientnet_b1`
+    , quantize it to INT8 dynamic range (`--recipe
     dynamic_wi8_afp32`), then benchmark both the original FP32 model and the
     newly quantized INT8 model on the GPU of my connected Android device.
-    Compare the average latency and report the throughput speedup."
-*   "Convert the model `Qwen/Qwen1.5-0.5B-Chat` from HuggingFace Hub to LiteRT
-    format, and run it locally using the prompt 'Explain edge machine learning
-    in one sentence'."
-*   "Download EfficientNet from huggingface repo
-    `litert-community/efficientnet_b1` . Offline compile (AOT) the model for the
+    Compare the average latency and report the throughput speedup.*
+*   *Convert the model `Qwen/Qwen1.5-0.5B-Chat` from HuggingFace, and run it
+    locally using the prompt 'Explain edge machine learning one sentence'*
+*   *Download EfficientNet from huggingface repo
+    `litert-community/efficientnet_b1`, offline compile (AOT) the model for the
     `sm8750` target NPU, and output the compiled model into `./models/compiled`.
     Then, run an on-device inference and benchmark using this newly compiled AOT
     model on the connected Android device's NPU (`--npu`). Confirm that the
-    graph loads directly without dynamic JIT compilation warmup latency."
+    graph loads directly without dynamic JIT compilation warmup latency.*
 
 The agent will automatically install the necessary tools, including Python
 virtual environments, `litert-cli-nightly`, and all required dependencies.
@@ -263,7 +267,8 @@ litert quantize model.tflite \
 
 ### 4. AOT Compile a LiteRT model for NPU
 
-> [!NOTE] Currently only support on Linux hosts and Qualcomm NPUs, and other NPU
+> [!NOTE]
+> Currently only support on Linux hosts and Qualcomm NPUs, and other NPU
 > supports are coming soon!
 
 ```bash