THE GOAL

Create a system cron for WordPress, that is accessible and can be easily tweaked to provide more details. Here some basic information about crons and the tools I am going to use …

WordPress CRON

In WordPress, the term “cron” refers to the system used for scheduling tasks to be executed at predefined intervals. The WordPress cron system allows various actions to be scheduled, such as publishing scheduled posts, checking for updates, sending email notifications, and running other scheduled tasks.

WordPress includes its own pseudo-cron system, which relies on visitors accessing your site. When a visitor loads a page on your WordPress site, WordPress checks if there are any scheduled tasks that need to be executed. If there are, it runs those tasks. This system works well for most sites, but it has limitations, particularly for low-traffic sites or sites that need precise scheduling.

To overcome these limitations, WordPress also provides the option to use a real cron system. With a real cron system, tasks are scheduled and executed independently of visitor traffic. This can be more reliable and precise than relying on visitors to trigger cron tasks.

To set up a real cron system for WordPress, you typically need to configure your server’s cron job scheduler to trigger the wp-cron.php file at regular intervals. This file handles the execution of scheduled WordPress tasks.

List scheduled WordPress events

WP Crontrol is a solid UI, to list and see whats happening in the background.

• View all cron events along with their arguments, recurrence, callback functions, and when they are next due.
• Edit, delete, pause, resume, and immediately run cron events.
• Add new cron events.
• Bulk delete cron events.
• Add and remove custom cron schedules.
• Export and download cron event lists as a CSV file.

WP-CLI

WP-CLI (WordPress Command Line Interface) is a powerful command-line tool that allows developers and administrators to interact with WordPress websites directly through the command line, without needing to use a web browser.

It provides a wide range of commands for managing various aspects of a WordPress site, such as executing crons / scheduled tasks, installing plugins, updating themes, managing users, and much more.

Most WordPress hosts have it preinstalled. Installation

Test CRON

Run CRON

ntfy.sh

ntfy (pronounced notify) is a simple HTTP-based pub-sub notification service. It allows you to send notifications to your phone or desktop via scripts from any computer, and/or using a REST API.

You can host your own docker instance or use the hosted solution.

The documentation is detailed and offers many ways to tweak the resulting notification.

Server CRON / THE OLD WAY

Server – CRON / THE NEW COMBINED WAY

I am not showing you how to create a system cron, that can vary depending on your hosting provider. Some of you will just setup / modify the crontab themselves. So here an example of how I use it these days …

Lets dissect this :)

Execution Timeframe – Minute / Hour / Day (month) / Month / Day (week)

See example usage on crontab.guru.

Capture the output

Im using a variable to capture the output, allowing me to pass it to ntfy.

The command part 1

Use bash to launch wp-cli, passing in required parameters to make sure the right website is targeted.
I am using –due-now to only launch those schedules that are actually pending. >/dev/null 2>&1 prevents any emails to be send for this cron job, it redirects the error stream into the output stream. Always helpful to remove it for the first testdrive.

>/dev/null: redirects standard output (stdout) to /dev/null
2>&1: redirects standard error (2) to standard output (1), which then discards it as well since standard output has already been redirected :)

The command part 2

This part sends the output to a ntfy instance / topic.

1. -u %token% login / auth with your token
2. -H “Filename: …” this will move the body text into an attachment file. You can remove this and all output will be part of the body text. ntfy has a body message length limit and will shift to an attachment when reached.
3. -H “Title: …” Title for the message
4. -d “$Output” Set the body message to the output we captured, using the set variable
5. Finally the url to post to, the ntfy instance and topic.

You can set many other things as well, like tags, images …. Check the documentation about publishing for more options. You can even redirect to an email account ;)

Detecting WP-CLI execution programmatically

Detecting WP-CRON execution programmatically

Enjoy coding …
Alex

Short look back at 2023

This has been a busy and interesting year. I am always looking forward to new challenges and this year really had some nice puzzles to solve ;)

2023 has brought us “AI” in all its glory, for content creation and in many development areas. There is no way around it and no way to stop the current revolution. Either we adapt or parish as a developer or content creator.

I have never been a bloom and doom person. If I was, I would no longer be working as a fullstack developer.

LM Studio, ComfyUI and invokeAI, are only a part of the local tools I have been experimenting with. I have always set a side a day during my week to play with new tools or expand my knowledge.

Whats next?

New clients, new connections, old friends and thrilling puzzles :)

Its always difficult to find time to work on my own tools, website and experiments. I really hope that I can set aside a bit more for this 2024.

I wish you all Happy Holidays and a joyful transition into 2024.

Keep on coding!
Alex

Artificial intelligence (AI) has revolutionized the way we interact with images, and the current AI image solutions are a testament to this. AI image solutions are applications of AI that can identify, classify, and manipulate images with remarkable accuracy and speed. With the rise of deep learning and computer vision, AI image solutions have become increasingly sophisticated, and their applications have expanded to fields such as healthcare, finance, entertainment, and more. Some of the most prominent AI image solutions and their applications.

Image Recognition

Image recognition is the process of identifying and classifying objects, people, or other elements in an image. AI-powered image recognition solutions use deep learning algorithms to recognize images with high accuracy. Image recognition is widely used in various fields, including healthcare, security, and retail. For example, in healthcare, AI image solutions are used to analyze medical images such as X-rays and MRIs to detect diseases and conditions such as cancer, brain injuries, and more. In security, AI-powered image recognition solutions are used to identify faces, license plates, and other elements in surveillance videos. In retail, AI image solutions are used to identify and classify products and improve inventory management.

Object Detection

Object detection is a subset of image recognition that involves detecting the location of specific objects in an image. AI-powered object detection solutions can identify and locate objects in an image with high accuracy. Object detection is used in various fields, including self-driving cars, security, and e-commerce. For example, in self-driving cars, AI image solutions are used to detect pedestrians, traffic lights, and other objects on the road. In security, AI-powered object detection solutions are used to identify suspicious behavior and detect objects such as weapons and explosives. In e-commerce, AI image solutions are used to detect and locate products in images, improving search and recommendation algorithms.

1. TensorFlow: TensorFlow is an open-source platform for building and training machine learning models, including image recognition models. It provides a wide range of tools and resources for image processing and can be used for tasks such as image classification, object detection, and segmentation.
2. Keras: Keras is a high-level neural networks API written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It is a user-friendly and efficient way to build and train deep learning models, including image recognition models.
3. PyTorch: PyTorch is another popular open-source machine learning framework that can be used for image recognition. It provides a flexible and easy-to-use platform for building and training deep learning models and supports various image processing tasks.
4. OpenCV: OpenCV is a widely used computer vision library that provides tools and algorithms for image processing, including image recognition. It can be used for tasks such as object detection, face recognition, and motion detection.
5. YOLO: YOLO (You Only Look Once) is an open-source object detection system that can recognize objects in images and videos with high accuracy and speed. It is based on deep learning and can be trained on custom datasets.
6. ImageAI: ImageAI is an open-source library that provides pre-trained models for image recognition tasks such as object detection, classification, and segmentation. It supports several deep learning frameworks, including TensorFlow, Keras, and PyTorch.
7. Detectron2: Detectron2 is an open-source object detection framework built on top of PyTorch. It provides a flexible and efficient platform for building and training object detection models and supports various architectures such as Faster R-CNN, Mask R-CNN, and RetinaNet.
8. MXNet GluonCV: MXNet GluonCV is an open-source computer vision library built on top of MXNet. It provides a wide range of tools and resources for building and training object detection models and supports various architectures such as Faster R-CNN, Mask R-CNN, and SSD.
9. SimpleDet: SimpleDet is an open-source object detection framework based on PyTorch. It provides a simple and efficient platform for building and training object detection models and supports various architectures such as Faster R-CNN and RetinaNet.

Image Segmentation

Image segmentation is the process of dividing an image into multiple segments or regions based on specific criteria. AI-powered image segmentation solutions use deep learning algorithms to identify different objects and elements in an image and segment them accordingly. Image segmentation is widely used in various fields, including healthcare, entertainment, and transportation. For example, in healthcare, AI image solutions are used to segment medical images such as CT scans and MRIs to aid in diagnosis and treatment. In entertainment, AI-powered image segmentation solutions are used to create special effects and manipulate images in movies and games. In transportation, AI image solutions are used to segment images of roads and traffic to aid in autonomous driving.

1. Mask R-CNN: Mask R-CNN is an open-source framework based on deep learning that can perform instance segmentation, which involves identifying and segmenting each object in an image. It is built on top of the Faster R-CNN architecture and supports various deep learning frameworks such as TensorFlow and PyTorch.
2. U-Net: U-Net is a popular open-source deep learning architecture for image segmentation. It is based on a fully convolutional neural network and has been widely used for tasks such as medical image segmentation.
3. DeepLab: DeepLab is an open-source image segmentation framework based on deep learning. It supports various architectures such as DeepLab v3 and DeepLab v3+, which have achieved state-of-the-art performance on several benchmarks.
4. SegNet: SegNet is an open-source deep learning architecture for image segmentation. It is based on an encoder-decoder network and has been widely used for tasks such as road segmentation and object detection.
5. PyTorch-UNet: PyTorch-UNet is an open-source implementation of the U-Net architecture in PyTorch. It provides a simple and efficient platform for building and training image segmentation models.
6. SimpleITK: SimpleITK is an open-source library for image analysis that provides tools and algorithms for image segmentation, registration, and filtering. It supports various programming languages such as Python, C++, and Java.

Image Generation

Image generation is the process of creating new images using AI algorithms. AI-powered image generation solutions can generate highly realistic images based on specific inputs, such as text descriptions or reference images. Image generation is used in various fields, including art, fashion, and advertising. For example, in art, AI image solutions are used to generate unique and creative designs and artworks. In fashion, AI-powered image generation solutions are used to create new designs and prototypes. In advertising, AI image solutions are used to generate highly realistic product images and visualizations.

1. GAN (Generative Adversarial Networks): GANs are a type of deep learning model that can generate realistic images by training two neural networks, a generator and a discriminator, in a adversarial setting. Several open-source implementations of GANs are available, including TensorFlow-GAN, PyTorch-GAN, and Keras-GAN.
2. StyleGAN (Style-Based Generative Adversarial Networks): StyleGAN is a type of GAN that can generate high-quality images with fine-grained control over the image style and content. It has been used to generate realistic human faces, animals, and other objects. An open-source implementation of StyleGAN is available on GitHub.
3. DeepDream: DeepDream is a visualization technique that uses convolutional neural networks to generate abstract and surreal images. It works by optimizing the input image to maximize the activation of specific neurons in the network. An open-source implementation of DeepDream is available in the TensorFlow library.
4. PixelRNN (Pixel Recurrent Neural Networks): PixelRNN is a type of neural network that can generate images pixel by pixel. It works by predicting the probability distribution of each pixel value based on the context of previous pixels. An open-source implementation of PixelRNN is available in the PyTorch library.
5. VAE (Variational Autoencoder): VAEs are a type of deep learning model that can generate images by learning a low-dimensional representation of the image space. They work by encoding an input image into a lower-dimensional latent space and then decoding it back into an image. Several open-source implementations of VAEs are available, including TensorFlow-VAE and PyTorch-VAE.

AI image solutions are transforming the way we interact with images and are being used in various fields to improve efficiency, accuracy, and creativity. From image recognition and object detection to image segmentation and generation, AI image solutions are increasingly sophisticated and capable. As AI technology continues to evolve, we can expect to see more applications of AI image solutions in the future, and they will undoubtedly play an essential role in many industries.

The Future

Some of the tools are already used in new ways, to help reconstruct or understand archaeology dig sites. They help analyse genes and will help to discover new remedies or cures for illnesses in the future.

Even though many complain about the exponential growth of AI, it brings so many positive angels into the mix, allowing us to fix and elevate our lives. Sadly change often comes at a cost, but it lies in our hands to direct and secure AI technology to help and not destroy lives.

First a bit of context :)

What is Gettext?

Translation within WordPress is based of Gettext. Gettext is a software internationalization and localization (i18n) framework used in many programming languages to facilitate the translation of software applications into different languages. It provides a set of tools and libraries for managing multilingual strings and translating them into various languages.

The primary goal of Gettext is to separate the text displayed in an application from the code that generates it. It allows developers to mark strings in their code that need to be translated and provides mechanisms for extracting those strings into a separate file known as a “message catalog” or “translation template.”

The translation template file contains the original strings and serves as a basis for translators to provide translations for different languages. Translators use specialized tools, such as poedit or Lokalize, to work with the message catalog files. These tools help them associate translated strings with their corresponding original strings, making the translation process more manageable.

At runtime, Gettext libraries are used to retrieve the appropriate translated strings based on the user’s language preferences and the available translations in the message catalog. It allows applications to display the user interface, messages, and other text elements in the language preferred by the user.

Gettext is widely used in various programming languages, including C, C++, Python, Ruby, PHP, and many others. It has become a de facto standard for internationalization and localization in the software development community due to its flexibility and extensive support in different programming environments.

What are WordPress language files?

WordPress uses dedicated language files to store translations of your strings of text in other languages. Two formats are used:

1. Portable Object (PO) files: They serve as the message catalog, containing the original strings that need to be translated and placeholders for the translations. Each entry in a PO file consists of several fields:
   • msgid: This field represents the original string that needs to be translated.
   • msgstr: This field holds the translated string corresponding to the msgid field.
   • Comments: PO files may include comments that provide context or instructions for translators. These comments start with a “#” symbol.
2. Machine Object (MO) files: MO files are generated from PO files using a compilation process. The compilation process converts the human-readable PO file, which contains the original strings and their translations, into a more compact and efficient binary format suitable for runtime use.
   
   The purpose of MO files is to provide a faster and more efficient way of accessing translations during the execution of a software application. Instead of parsing and processing the text-based PO files at runtime, the Gettext libraries directly read the pre-compiled MO files.

Caching Language Files!

There has been a discussion for years, if it makes sense to cache mo-files, to speedup WordPress when multiple languages are in use. Discussion of the WordPress Core team.

There have been a couple of plugins trying to fix this and prevent reloading of language files on every pageload.

• https://de.wordpress.org/plugins/fast-translate/
• https://de.wordpress.org/plugins/mo-cache/
• https://de.wordpress.org/plugins/speed-up-translation/
• https://github.com/inpsyde/translation-cache
• https://www.bjornjohansen.com/wordpress-translations-loading-time

Most of these use transients in your database and object caches if active.

Does it really make any sense?

Well it all depends on the amount of language files and your infrastructure. This load/parse operation is quite CPU-intensive and does spend quite a significant amount of time.

I have decided, not to bother with it in the past :) But I am always looking for ways to speed up multi-lingual websites, as that is my daily bread & butter ;) Checkout Index WP MySQL For Speed & Index WP Users For Speed, which optimizes the MySQL Indexes for optimal speed. That change, really does make a bit difference!

Native Gettext for WordPress

One thing that can help to speed up things, is to use the native gettext extension and not the WordPress integration of it. This will indeed help speedup translation processing and help big multilingual websites.

Native Gettext for WordPress by Colin Leroy-Mira, provides just that.

Disable Gettext, when only using English!

Create a must use plugin and add this:

This will prevent any .mo file from loading.

Use the gettext filter to change any translatable string.

Please consider the performance impact. Read about it at the main documentation.

Enjoy ….

Chatbots

Typebot is a free and open-source platform that lets you create conversational apps and forms, which can be embedded anywhere on your website or mobile apps.

It provides various features like text, image, and video bubble messages, input fields for various data types, native integrations, conditional branching, URL redirections, beautiful animations, and customizable themes.

You can embed it as a container, popup, or chat bubble with the native JS library and get in-depth analytics of the results in real-time.

Typebot can be used in the Cloud (free tier available) or installed via Docker.

It drag & drop builder makes it amazingly fast to build your personal chatbot. It provides a ton of integrations (Google Analytics, Google Sheets, OpenAI, Zapier, Webhooks, Email, Chatwood) out of the box or you can connect other things via your own API Requests.

I have been a longtime user of RocketChat and build my own chatbots using Hubot or BotPress.

RocketChat
“Rocket.Chat is a free and open-source team chat platform that enables real-time communication between team members. It provides team messaging, audio and video conferencing, screen sharing, file sharing, and other collaboration features. It offers a familiar chat interface that makes it easy for teams to stay connected and collaborate effectively.

Rocket.Chat is highly customizable and extensible, with a large number of community-contributed plugins and integrations. It supports multiple platforms, including web, desktop, and mobile, making it accessible to teams regardless of their device or location. It also offers end-to-end encryption for secure communication, and compliance with various industry regulations such as HIPAA, GDPR, and others.”

Hubot
“Hubot is a popular open-source chatbot framework developed by GitHub. It allows developers to build and deploy custom chatbots that can automate tasks, integrate with various APIs, and enhance team communication and collaboration.

Hubot is built on Node.js and can be extended with various plugins and scripts, making it highly customizable and flexible. It comes with many built-in plugins, including integration with popular chat platforms like Slack, HipChat, and Campfire, as well as various APIs like Twitter, GitHub, and Google.

Hubot enables developers to build chatbots that can perform various tasks, such as scheduling meetings, deploying code, fetching data, and responding to user queries. It also provides a command-line interface for managing and interacting with the chatbot.”

Botpress
“Botpress is an open-source platform that allows you to build and deploy chatbots and conversational interfaces. It provides a user-friendly interface and a visual flow editor that allows you to create and manage your chatbot’s conversation flow. Botpress comes with many pre-built components and integrations, including natural language processing (NLP) engines, chat widgets, and webhooks, making it easy to build complex chatbots with advanced functionality.

Botpress offers features like machine learning-based NLP, conversational analytics, multi-language support, customizable themes, and a modular architecture that allows you to add custom functionalities with ease. It supports multiple channels, including web, Facebook Messenger, Slack, and WhatsApp, allowing you to reach your audience wherever they are.”

Typebot rocks

Typebot beats Botpress and could be the reason why I might be skipping RocketChat as well. Not sure I want to build an integration for RocketChat myself.

Setup

Base YAML Docker-Compose File to get you started. This will setup a Postgres instance, the typebot builder and typebot viewer.

Github
Docker Setup