Formatting

Projects/Projects/AI-Defined-Vehicles-ADAS-IVI-on-Arm.md

@@ -52,9 +52,9 @@ The IVI application should combine:
 -	Multimedia services such as navigation, vehicle telemetry, video/audio streaming, internet radio, or gaming
 -	System-level integration, including boot sequencing, background services, and inter-process communication
 -	ADAS-inspired AI/ML-driven functionality that actively shapes system behavior, such as:
-  -	Driver monitoring (e.g. attention, drowsiness, or distraction detection)
-  -	In-cabin perception (e.g. sound classification, occupant presence)
-  -	Context-aware automation (e.g. adaptive UI, media muting, or alerts based on inferred events)
+       1.	Driver monitoring (e.g. attention, drowsiness, or distraction detection)
+       2.	In-cabin perception (e.g. sound classification, occupant presence)
+       3.	Context-aware automation (e.g. adaptive UI, media muting, or alerts based on inferred events)
 
 For example, an AI service might analyze camera or audio input and dynamically modify the UI, pause media, or trigger a **simulated** safety alert—illustrating how AI models become first-class components in vehicle software architecture.
 

Projects/Projects/Humanoid-&-Quadruped-Robotics-&-AI.md

@@ -47,12 +47,15 @@ This combination makes them ideal testbeds for exploring physical AI under real-
 By leveraging efficient Arm-native ML frameworks (e.g. PyTorch + ExecuTorch, LiteRT, ONNX Runtime, or accelerated ROS 2 pipelines), developers can study how modern AI models behave when tightly coupled to physical embodiment, whether quadrupedal or humanoid. Arm-based systems enable tight perception–action loops, reducing the latency from sensing a photon or audio wave to taking a meaningful physical action.
 
 **Example Platforms**
-Quadruped 
+
+**Quadruped**
 - [PuppyPi - Raspberry Pi 4/5](https://www.hiwonder.com/products/puppypi?variant=40213129003095&srsltid=AfmBOoonusv8sBF3hP6LMvyvBAXPVJh7eW0V60FU7IDGaPfYcz7cseXH)
 - [ROSPug - Jetson Nano](https://www.hiwonder.com/products/rospug?srsltid=AfmBOop83H4FhIvFPY9-fYUyQ2a0xrh9-_gfr4aHVuy15X8owRgYV2PL)
-Humanoid / Biped
+
+**Humanoid / Biped**
 - [TonyPi - Raspberry Pi 4/5](https://www.hiwonder.com/products/tonypi?variant=31753114681431&srsltid=AfmBOoosUcEQONClryEw_jPzqkrezui8d5BkTunVcWUTKhbD_xikG_10)
-Boards
+
+**Boards**
 - [Raspberry Pi 4, 5, CM4](https://www.raspberrypi.com/products/compute-module-4/?variant=raspberry-pi-cm4001000)
 - [Rockchip RK3566 or 3588](https://www.waveshare.com/core3566.htm?srsltid=AfmBOorJELpnCGbrB3pV489O_RIOvnExIjj8q84sPlp-N4W3b4_wsfRj)
 - [Nvidia Jetson Nano - Heterogeneous SoC with Arm-powered CPU for control and orchestration](https://www.nvidia.com/en-gb/autonomous-machines/embedded-systems/jetson-nano/product-development/)

Projects/Projects/Video-&-Audio-Provenance-In-The-Age-of-AI.md

@@ -63,8 +63,8 @@ Build and evaluate a comprehensive AI-augmented audio/video capture and provenan
 - captures streamed media with a camera or microphone,
 - runs AI models on-device (i.e. face/object/keyword/sentiment detection, upscaling/filters/enhancements),
 - generates C2PA Content Credentials that transparently disclose:
-  - which models were run,
-	- their effect/impact on the image or video
+  1. which models were run,
+	2. their effect/impact on the image or video
 
 and demonstrates how this provenance enables trust and auditability in real-world use cases such as content integrity validation and responsible media pipelines.
 

docs/_posts/2026-02-06-AI-Defined-Vehicles-ADAS-IVI-on-Arm.md

@@ -53,9 +53,9 @@ full_description: |-
   -	Multimedia services such as navigation, vehicle telemetry, video/audio streaming, internet radio, or gaming
   -	System-level integration, including boot sequencing, background services, and inter-process communication
   -	ADAS-inspired AI/ML-driven functionality that actively shapes system behavior, such as:
-    -	Driver monitoring (e.g. attention, drowsiness, or distraction detection)
-    -	In-cabin perception (e.g. sound classification, occupant presence)
-    -	Context-aware automation (e.g. adaptive UI, media muting, or alerts based on inferred events)
+         1.	Driver monitoring (e.g. attention, drowsiness, or distraction detection)
+         2.	In-cabin perception (e.g. sound classification, occupant presence)
+         3.	Context-aware automation (e.g. adaptive UI, media muting, or alerts based on inferred events)
 
   For example, an AI service might analyze camera or audio input and dynamically modify the UI, pause media, or trigger a **simulated** safety alert—illustrating how AI models become first-class components in vehicle software architecture.
 
@@ -129,9 +129,9 @@ The IVI application should combine:
 -	Multimedia services such as navigation, vehicle telemetry, video/audio streaming, internet radio, or gaming
 -	System-level integration, including boot sequencing, background services, and inter-process communication
 -	ADAS-inspired AI/ML-driven functionality that actively shapes system behavior, such as:
-  -	Driver monitoring (e.g. attention, drowsiness, or distraction detection)
-  -	In-cabin perception (e.g. sound classification, occupant presence)
-  -	Context-aware automation (e.g. adaptive UI, media muting, or alerts based on inferred events)
+       1.	Driver monitoring (e.g. attention, drowsiness, or distraction detection)
+       2.	In-cabin perception (e.g. sound classification, occupant presence)
+       3.	Context-aware automation (e.g. adaptive UI, media muting, or alerts based on inferred events)
 
 For example, an AI service might analyze camera or audio input and dynamically modify the UI, pause media, or trigger a **simulated** safety alert—illustrating how AI models become first-class components in vehicle software architecture.
 

docs/_posts/2026-02-06-Humanoid-&-Quadruped-Robotics-&-AI.md

@@ -48,12 +48,15 @@ full_description: |-
   By leveraging efficient Arm-native ML frameworks (e.g. PyTorch + ExecuTorch, LiteRT, ONNX Runtime, or accelerated ROS 2 pipelines), developers can study how modern AI models behave when tightly coupled to physical embodiment, whether quadrupedal or humanoid. Arm-based systems enable tight perception–action loops, reducing the latency from sensing a photon or audio wave to taking a meaningful physical action.
 
   **Example Platforms**
-  Quadruped 
+
+  **Quadruped**
   - [PuppyPi - Raspberry Pi 4/5](https://www.hiwonder.com/products/puppypi?variant=40213129003095&srsltid=AfmBOoonusv8sBF3hP6LMvyvBAXPVJh7eW0V60FU7IDGaPfYcz7cseXH)
   - [ROSPug - Jetson Nano](https://www.hiwonder.com/products/rospug?srsltid=AfmBOop83H4FhIvFPY9-fYUyQ2a0xrh9-_gfr4aHVuy15X8owRgYV2PL)
-  Humanoid / Biped
+
+  **Humanoid / Biped**
   - [TonyPi - Raspberry Pi 4/5](https://www.hiwonder.com/products/tonypi?variant=31753114681431&srsltid=AfmBOoosUcEQONClryEw_jPzqkrezui8d5BkTunVcWUTKhbD_xikG_10)
-  Boards
+
+  **Boards**
   - [Raspberry Pi 4, 5, CM4](https://www.raspberrypi.com/products/compute-module-4/?variant=raspberry-pi-cm4001000)
   - [Rockchip RK3566 or 3588](https://www.waveshare.com/core3566.htm?srsltid=AfmBOorJELpnCGbrB3pV489O_RIOvnExIjj8q84sPlp-N4W3b4_wsfRj)
   - [Nvidia Jetson Nano - Heterogeneous SoC with Arm-powered CPU for control and orchestration](https://www.nvidia.com/en-gb/autonomous-machines/embedded-systems/jetson-nano/product-development/)
@@ -135,12 +138,15 @@ This combination makes them ideal testbeds for exploring physical AI under real-
 By leveraging efficient Arm-native ML frameworks (e.g. PyTorch + ExecuTorch, LiteRT, ONNX Runtime, or accelerated ROS 2 pipelines), developers can study how modern AI models behave when tightly coupled to physical embodiment, whether quadrupedal or humanoid. Arm-based systems enable tight perception–action loops, reducing the latency from sensing a photon or audio wave to taking a meaningful physical action.
 
 **Example Platforms**
-Quadruped 
+
+**Quadruped**
 - [PuppyPi - Raspberry Pi 4/5](https://www.hiwonder.com/products/puppypi?variant=40213129003095&srsltid=AfmBOoonusv8sBF3hP6LMvyvBAXPVJh7eW0V60FU7IDGaPfYcz7cseXH)
 - [ROSPug - Jetson Nano](https://www.hiwonder.com/products/rospug?srsltid=AfmBOop83H4FhIvFPY9-fYUyQ2a0xrh9-_gfr4aHVuy15X8owRgYV2PL)
-Humanoid / Biped
+
+**Humanoid / Biped**
 - [TonyPi - Raspberry Pi 4/5](https://www.hiwonder.com/products/tonypi?variant=31753114681431&srsltid=AfmBOoosUcEQONClryEw_jPzqkrezui8d5BkTunVcWUTKhbD_xikG_10)
-Boards
+
+**Boards**
 - [Raspberry Pi 4, 5, CM4](https://www.raspberrypi.com/products/compute-module-4/?variant=raspberry-pi-cm4001000)
 - [Rockchip RK3566 or 3588](https://www.waveshare.com/core3566.htm?srsltid=AfmBOorJELpnCGbrB3pV489O_RIOvnExIjj8q84sPlp-N4W3b4_wsfRj)
 - [Nvidia Jetson Nano - Heterogeneous SoC with Arm-powered CPU for control and orchestration](https://www.nvidia.com/en-gb/autonomous-machines/embedded-systems/jetson-nano/product-development/)

docs/_posts/2026-02-06-Video-&-Audio-Provenance-In-The-Age-of-AI.md

@@ -64,8 +64,8 @@ full_description: |-
   - captures streamed media with a camera or microphone,
   - runs AI models on-device (i.e. face/object/keyword/sentiment detection, upscaling/filters/enhancements),
   - generates C2PA Content Credentials that transparently disclose:
-    - which models were run,
-  	- their effect/impact on the image or video
+    1. which models were run,
+  	2. their effect/impact on the image or video
 
   and demonstrates how this provenance enables trust and auditability in real-world use cases such as content integrity validation and responsible media pipelines.
 
@@ -137,8 +137,8 @@ Build and evaluate a comprehensive AI-augmented audio/video capture and provenan
 - captures streamed media with a camera or microphone,
 - runs AI models on-device (i.e. face/object/keyword/sentiment detection, upscaling/filters/enhancements),
 - generates C2PA Content Credentials that transparently disclose:
-  - which models were run,
-	- their effect/impact on the image or video
+  1. which models were run,
+	2. their effect/impact on the image or video
 
 and demonstrates how this provenance enables trust and auditability in real-world use cases such as content integrity validation and responsible media pipelines.