chore: remove showAuthor from recent articles with authors

Author: f-hollow

content/blog/2025/02/rust-esp-hal-beta/index.md

@@ -1,7 +1,6 @@
 ---
 title: "esp-hal 1.0.0 beta announcement"
 date: 2025-02-24
-showAuthor: true
 featureAsset: "img/featured/featured-rust.webp"
 authors:
   - scott-mabin
@@ -108,7 +107,7 @@ As this is a beta release, we'd absolutely love to hear your feedback on esp-hal
 * Please open issues for anything that should be working that isn't
 * Please open discussions to discuss API decisions that perhaps aren't quite as ergonomic or thought through as we intended
 
-We've created our own project generation tool, [esp-generate] to bootstrap starting a project, which is often a bit of a tricky thing to set up in embedded. Please do give it a try by first installing the tool with 
+We've created our own project generation tool, [esp-generate] to bootstrap starting a project, which is often a bit of a tricky thing to set up in embedded. Please do give it a try by first installing the tool with
 
 ```bash
 cargo install esp-generate
@@ -124,7 +123,7 @@ We're currently rewriting the [book], but in the meantime it can still be helpfu
 
 ### Where Does This Leave the Standard Library Port?
 
-At this time we're officially marking the `std` _crates_ as community supported, which we've reflected on the [organization landing page](https://github.com/esp-rs/). We will still maintain the upstream compiler targets, and ensure that those targets continue to function, but `esp-idf-sys`, `esp-idf-hal` and `esp-idf-svc` are now community projects. It's been moving this way for a while, but we'd like to officially announce it here. Users wanting a more stable (and official) development environment should transition to `esp-hal` and the other `no_std` crates. 
+At this time we're officially marking the `std` _crates_ as community supported, which we've reflected on the [organization landing page](https://github.com/esp-rs/). We will still maintain the upstream compiler targets, and ensure that those targets continue to function, but `esp-idf-sys`, `esp-idf-hal` and `esp-idf-svc` are now community projects. It's been moving this way for a while, but we'd like to officially announce it here. Users wanting a more stable (and official) development environment should transition to `esp-hal` and the other `no_std` crates.
 
 ### What’s Next?
 
@@ -165,4 +164,4 @@ If you're a company using (or considering) Rust on our chips, please do contact
 [@BjoernQ]: https://github.com/BjoernQ
 [@arjanmels]: https://github.com/arjanmels
 [@t-moe]: https://github.com/t-moe
-[@bugadani]: https://github.com/bugadani
+[@bugadani]: https://github.com/bugadani

content/blog/2025/03/security-in-esp-rainmaker/index.md

@@ -1,7 +1,6 @@
 ---
 title: "Security in ESP RainMaker"
 date: 2025-03-20
-showAuthor: true
 authors:
   - piyush-shah
 tags:
@@ -139,4 +138,3 @@ ESP RainMaker is currently undergoing ETSI EN 303 645 (Cybersecurity Standard fo
 ---
 
 The security measures taken for all components of ESP RainMaker ensure that the overall solution is highly secure and robust. With the added privacy measures of GDPR compliance, it makes for a perfect platform for your IoT devices. If you are interested in commercial deployments of ESP RainMaker, please reach out to `[email protected]` or `[email protected]`.
-

content/blog/2025/06/simple-boot-explained/index.md

@@ -1,7 +1,6 @@
 ---
 title: "Simple Boot explained"
 date: 2025-06-30
-showAuthor: false
 authors:
   - "marek-matej"
 tags:

content/blog/2025/06/touchpad-digit-recognition/index.md

@@ -1,7 +1,6 @@
 ---
 title: "Touchpad Digit Recognition Based on ESP-DL"
 date: 2025-06-18
-showAuthor: false
 summary: "This article demonstrates how to implement a touchpad-based digit recognition system using ESP-DL on ESP32 series chips. It covers the complete workflow from data collection and preprocessing to model training, quantization, and deployment, showcasing ESP-DL's capabilities in edge AI applications."
 authors:
   - yan-ke
@@ -73,7 +72,7 @@ The Interpolated touch dataset contains ten categories from digit 0 to 9, with a
 
 ### Dataset Preparation
 
-Before model training, the interpolated touch data is preprocessed using the `torchvision.transforms` module from `PyTorch`. First, the images are converted to single-channel grayscale images, and random affine transformations (rotation of ±10 degrees, translation of ±10%) are applied to enhance data diversity. Subsequently, the images are converted to tensor format and normalized with a mean of 0.5 and standard deviation of 0.5. 
+Before model training, the interpolated touch data is preprocessed using the `torchvision.transforms` module from `PyTorch`. First, the images are converted to single-channel grayscale images, and random affine transformations (rotation of ±10 degrees, translation of ±10%) are applied to enhance data diversity. Subsequently, the images are converted to tensor format and normalized with a mean of 0.5 and standard deviation of 0.5.
 
 After data processing, the dataset is loaded using `ImageFolder`, a PyTorch utility that organizes images into labeled datasets based on folder names. It is then randomly split into training and test sets with an 8:2 ratio.
 
@@ -173,13 +172,13 @@ def train_epoch(model, train_loader, criterion, optimizer, device):
 
     for inputs, labels in train_loader:
         inputs, labels = inputs.to(device), labels.to(device)
-        
+
         optimizer.zero_grad()
         outputs = model(inputs)
         loss = criterion(outputs, labels)
         loss.backward()
         optimizer.step()
-        
+
         running_loss += loss.item()
         _, predicted = torch.max(outputs.data, 1)
         total += labels.size(0)
@@ -342,7 +341,7 @@ To avoid inconsistencies between the actual inference results and PC inference r
 
 ```info
 test outputs value:
-%23, shape: [1, 10], exponents: [0], 
+%23, shape: [1, 10], exponents: [0],
 value: array([4.78089300e-25, 1.05306175e-20, 3.62514101e-34, 9.60267995e-24,
         9.60267995e-24, 6.47023468e-26, 3.97544995e-31, 1.15482239e-17,
         3.87399781e-21, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00],
@@ -390,7 +389,7 @@ void DataPreprocessor::process(const uint8_t* input_data, int8_t* quant_buffer)
 }
 ```
 
-After data preprocessing, the processed data needs to be fed into the model for inference. The entire inference process consists of three steps: 
+After data preprocessing, the processed data needs to be fed into the model for inference. The entire inference process consists of three steps:
 
 1. Data Encapsulation.
 2. Model Execution.
@@ -450,7 +449,7 @@ int DataPostprocessor::process()
 
 ## Summary
 
-This article introduces a handwritten digit recognition application implemented using the ESP-DL framework, demonstrating how to deploy deep learning models on ESP32 series chips. 
+This article introduces a handwritten digit recognition application implemented using the ESP-DL framework, demonstrating how to deploy deep learning models on ESP32 series chips.
 
 All the Python scripts for data processing, model training, quantization, as well as the C++ code for model inference and the pre-trained models mentioned in this article, are available in the esp-iot-solution GitHub repository: [touchpad_digit_recognition](https://github.com/espressif/esp-iot-solution/tree/master/examples/ai/esp_dl/touchpad_digit_recognition)
 

content/blog/2025/07/overview-of-esp-audio-effects/index.md

@@ -10,7 +10,7 @@ summary: This article explores the features and capabilities of ESP-Audio-Effect
 
 We are excited to announce the official release of **ESP-Audio-Effects v1.0.0**! This is a powerful and flexible audio processing library designed to provide developers with efficient audio effects processing capabilities. You can now visit the [ESP-Audio-Effects](https://components.espressif.com/components/espressif/esp_audio_effects/versions/1.0.0?language=en) release page for more details. ESP-Audio-Effects is widely used in various smart audio devices, including smart speakers, headphones, audio playback devices, and voice interaction systems. Let's dive into ESP-Audio-Effects now and enhance your audio processing capabilities!
 
-## Introduction to ESP-Audio-Effects Component 
+## Introduction to ESP-Audio-Effects Component
 
 - **ALC(Automatic Level Control):** By adjusting the audio gain, the input audio volume is automatically regulated. When the gain is set to a positive value, the audio volume increases; when set to a negative value, the volume decreases. A gradual adjustment approach is used to prevent sudden volume changes, ensuring a smoother and more natural audio transition. If the gain is set too high, it will dynamically adjust the gain in real-time to keep the audio signal within a reasonable range, preventing distortion caused by clipping.
 
@@ -62,14 +62,14 @@ The component adopts a unified and simple API design, with a clear and intuitive
 
 ## Example of Using Audio Effects in GMF
 
-Each function of esp-audio-effects has been added to the [esp-gmf](https://github.com/espressif/esp-gmf) (General Multimedia Framework) framework as a [gmf-audio](https://github.com/espressif/esp-gmf/tree/main/elements/gmf_audio) element. By integrating these audio effect elements through a pipeline, flexible combinations of audio processing can be achieved. You can see an example of pipeline in the picture below. 
+Each function of esp-audio-effects has been added to the [esp-gmf](https://github.com/espressif/esp-gmf) (General Multimedia Framework) framework as a [gmf-audio](https://github.com/espressif/esp-gmf/tree/main/elements/gmf_audio) element. By integrating these audio effect elements through a pipeline, flexible combinations of audio processing can be achieved. You can see an example of pipeline in the picture below.
 
 {{< figure default=true src="img/pipeline.webp" height=500 caption="GMF Pipeline Sketch Map" >}}
 
 Here is a simple [example](https://github.com/espressif/esp-gmf/tree/main/gmf_examples/basic_examples/pipeline_play_sdcard_music) of how to use esp-audio-effects in GMF.
 
 ## Conclusion
 
-ESP-Audio-Effects v1.0.0 is a versatile audio processing library. With features like Automatic Level Control, Equalizers, Dynamic Range Control, and more, it offers developers more control over audio effects. Through seamless integration with esp-gmf, developers can create easily create audio pipelines tailored to their application. 
+ESP-Audio-Effects v1.0.0 is a versatile audio processing library. With features like Automatic Level Control, Equalizers, Dynamic Range Control, and more, it offers developers more control over audio effects. Through seamless integration with esp-gmf, developers can create easily create audio pipelines tailored to their application.
 
 If you want to learn more about esp-audio-effects, check out our [Github repository](https://github.com/espressif/esp-adf-libs/blob/master/esp_audio_effects/README.md). Feel free to contact us and let's explore more possibilities in audio processing together!