Automating Industries: How Robotics and AI Are Transforming Manufacturing

The future of manufacturing is smart, intuitive, and highly efficient, all thanks to robotics and AI. Automated factories are nothing new, as digital upgrades have been occurring for quite some time. However, with recent technological advances leading to smart robotics, artificial intelligence, and machine learning, the industry is rapidly moving forward by leaps and bounds. 

These technologies are drastically transforming factory settings, leading to significant improvements in quality control, safety, and supply chain optimization. As this transformation continues, it highlights a future where AI and robotics drive further advancements and innovations, leading to superior efficiency and unimaginable capabilities.

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What Are the Benefits?

Automation driven by robotics and AI can produce a number of benefits that can help further the manufacturing industry, including:

  • Improving worker safety
  • Lowering operational costs
  • Reducing factory lead times
  • Higher and faster ROI
  • Increased competitiveness
  • Greater consistency
  • Better planning
  • Increased output

For those companies that are looking to reduce their carbon footprint, AI and robotic automation can also help lower environmental impact. In essence, using AI and robotics helps factories run more efficiently, which means less usage of energy and resources that contribute to waste and pollution. 

How Robotics and AI Are Transforming Manufacturing

AI-powered systems run on machine learning programs that essentially tell robots, machines, and equipment what to do, when to do it, and how best to do it. In other words, they are designed to learn the best way to perform a task, especially repetitive tasks, which can help eliminate otherwise wasted time, money, and resources. This ultimately leads to overall operational optimization, which can boost efficiency and lead to greater productivity. 

Below are some of the most common ways AI and robotics are being used today to transform factory settings.

Smart Automation

Again, AI-powered systems are embedded with deep learning programs and neural networks that enable them to work optimally with little to no human intervention. This allows human workers to focus their attention on other more important tasks that do require a human touch, while the robots take care of everything else. This allows for numerous factory operations to be automated, which can reduce human error and cycle times, and speed up production processes. 

Quality Control

Consistency is key to success in the manufacturing industry. This means machines and equipment that are consistently working as they should to help produce consistent and high-quality products. This is where AI steps in. 

AI-powered computer systems can monitor input from various factory robots and equipment, keeping an eye out for any anomalies or issues that could impact production. For example, many AI-powered systems in manufacturing settings now use analysis for predictive maintenance, which helps avoid breakdowns and malfunctions that could lead to delays or shutdowns.  

Layout Optimization

The way the factory floor is laid out and organized can also play a role in production output and efficiency. If things are not laid out as optimally as possible, for example, it can make it harder for a worker to do their job. 

AI-powered tools and sensors, however, can analyze layouts and suggest a better plan to improve efficiency and reduce issues with safety, space, and materials. If a short-run project arises, these systems can also suggest a temporary reconfiguration to better suit the needs of the project. 

Even after the factory is set up, sensors can continuously monitor and make suggestions to improve process flow based on how things are running. 

Generative Design

Generative design using AI allows engineers to input a set of requirements for a product, such as the parameters and design goals, and then the AI uses that information to test out hundreds, even thousands, of different iterations until the best option is found. This is something that could normally take months or years for a person to do themselves, but AI can do it much faster, which allows companies to create the most optimally designed product in a shorter amount of time. 

Current Case Studies for AI and Robotics

Already, hundreds of companies around the globe are using AI and robotics to improve manufacturing processes. 

Siemens’ (SIEGY) has adopted Microsoft’s OpenAI Service to harness generative AI design to help a number of industrial companies drive efficiency and innovation across their manufacturing processes.  

DHL is deploying Boston Dynamic’s Stretch Robot to optimize its supply chain, using it to help grab and move packages. 

Rockwell Automation’s Smart Manufacturing software uses AI to equip factories with the insights needed to optimize risk management, quality, productivity, and sustainability. 

Walmart recently built a 1.4 million-square-foot facility in Florida, their first automated distribution center powered by AI supply chain technology from the company, Symbiotic. 

NVIDIA provides a number of AI solutions to the industrial sector, including IGX Origin, a platform that provides predictive maintenance, robotics, and industrial inspection solutions. 

In the United States alone, manufacturing companies have heavily invested in smart automation installations in recent years, including 44,303 robotic units in 2023. These numbers are expected to continue growing as more companies look to these technologies to upgrade their factories and improve efficiencies. 

Challenges to Consider

While smart automation technologies have the power to positively transform the manufacturing industry, using AI and robotics still comes with challenges. The ethical implications of AI are important to consider, for example. 

In any setting where you have human-robot collaborations or human-AI collaborations, it’s necessary to understand the ethical challenges, such as safety, communication, and job displacement. 

While robots are often used to make factory settings safer, if workers do not understand how to properly interact with these systems, it can end up causing more safety problems. This is where thorough training plays an important role. If companies intend to integrate robots, they must first make sure their workers fully understand how to use and interact with these advanced systems. 

For example, many robots have user interfaces that workers can use to access and adjust settings, however, if workers don’t have any programming knowledge, they could struggle without proper training. Additionally, factory robots can also sometimes be controlled using voice commands, but if workers don’t adapt to these voice controls, it will make it difficult for them to work in the same area as the robots.  

Another challenge that can arise is low morale due to fear of job displacement. When integrating AI-powered systems and robots, it’s crucial that companies alleviate these fears by assuring their workers that these systems are meant to help them do their jobs better as opposed to replacing them entirely. 

Cybersecurity Concerns

Another challenge worth noting is potential cybersecurity issues. As processes and systems become more digitally connected and intertwined, it makes them more susceptible to cyberattacks. For example, while rare, industrial robots can be hacked as can other AI-powered systems. 

To avoid these risks, manufacturers must take steps to increase cybersecurity awareness among workers and implement advanced cybersecurity protocols. This means training workers on how to use these systems without putting any sensitive data at risk and even limiting who has access to controls. It also means using device-hardening protocols and end-to-end encryption to protect data. 

Keeping up with the latest software updates and firmware patches is also important to reduce system attacks, as well as conducting regular cybersecurity risk assessments. If manufacturers don’t want to handle this themselves, they can hire vendors who can monitor cybersecurity threats for them and distribute updates and patches as needed.   

Final Thoughts

While there are risks associated with the adoption of any new advanced system, the pros generally outweigh the cons. So long as companies are smart about how they integrate AI and robotic systems, these technologies have the power to lead to greater efficiency and production output. It will also help companies stay competitive in an evolving digital landscape. 

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How to Leverage Software Development Lifecycle Diagram

Whether for the commercial or games market, software development is a complicated process. With over 26.3 million developers around the world, there are many tried and proven methods that can help make the development process easier. One main methodology is the Software Development Life Cycle (SDLC) which allows development teams to make high-quality software in the quickest time possible. 

As with any proven process, following the various steps can help people avoid mistakes that could delay deployment or create errors in the software. One helping hand that can be useful with the SDLC is the Software Development Lifecycle Diagram (SDLD). Just what are the SDLC and SDLD? And how can using them help ensure that your software is deployed on time and of the highest quality?

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What is the Software Development Life Cycle (SDLC)?

As mentioned, the SDLC is a methodology with well-defined processes that allows developers and DevOps teams to create quality software (usually within a short timeframe). To make things easier for developers, the SDLC breaks the development process into six main phases:

  • Requirement analysis.
  • Planning phase.
  • Software design incorporating features like architectural design.
  • Software development.
  • Testing.
  • Deployment.

As well as the six phases outlined above, there are various SDLC models such as the waterfall model, spiral model, iterative model, or the agile model. Developers will utilize different models according to their needs and the type of software they are developing. For example, the model used to develop software for a hosted VoIP service might be different from that used for inventory management software. 

In cases like eCommerce platforms, incorporating eCommerce analytics into the development process can enhance user experience and provide valuable business insights. This flexibility allows developers to choose the most effective approach for the specific requirements of their project.

What is a Software Development Lifecycle Diagram?

A software development lifecycle diagram is simply a pictorial representation of the particular SDLC that may apply to the software you are developing. It breaks the cycle down into the steps you need to take to successfully develop a product and can act as a checklist; complete one step before moving on to the next. 

The stages of your SDLC/SDLD

If you’re developing a new piece of software, there are many things to consider before you even move to designing and coding it. Is the software standalone or will it be added to a suite of existing applications? If so, does it need to fit with an application portfolio management APM system that’s already in place? 

Your starting point is always going to be purpose. What will the software be used for and what are the users’ requirements?

1. Analyze user requirements

Before you even start planning the software, you need to understand what it’s for and what features it needs to have. Is it to solve problems or fulfill wants? You need to get input from all relevant stakeholders and that can include everyone from customers to programmers.

For example, a business may be migrating legacy applications and some of the apps or software may not be compatible with the new system. You will need to be aware of any improvements that need to be made as well as any security risks the software may face. 

2. Planning

Once you know what is required from the software in the previous phase, you can move to creating a detailed plan. In this step, your DevOps team is going to look at various factors such as cost estimates for developing the software and what resources will be required. 

One major consideration at this stage is security. What sort of data will the software be handling, are there any regulatory requirements such as the CCPA, and what vulnerabilities may exist? 

In this stage, it is also crucial to consider the database concepts that will underpin the software, ensuring data integrity, scalability, and performance meet the anticipated needs.

A comprehensive security assessment should be conducted to identify and mitigate potential security risks before proceeding further in the development process. You need to be sure that every base is covered in the planning stage to prevent issues from arising later. 

3. Design phase

Now that you know that the plan is feasible and what resources are needed, you can start designing the software. You should be letting all stakeholders review your design specification to ensure it’s ticking all their boxes and so that they can give you feedback and offer any suggestions as to changes that may be needed. 

It’s essential that you have this feedback – and that you listen to it – as failure to do so could lead to exceeding planned costs or even project failure. If a stakeholder identifies that you are missing something from lead enrichment software, for example, not taking that feedback on board and rectifying any omission could mean the project is doomed. 

Ensuring all necessary features and functionalities are included and aligned with stakeholder requirements is critical for the success of the project.

4. Build it and they shall come

You have a roadmap of the software you are developing so can now move on to the actual building of the product. If your DevOps team is working from the same location, it should be fairly easy to stick to the blueprint and follow any guidelines you have established. If some of the team are working remotely, then you will need to ensure you have good communication tools that will foster collaboration

You will need to establish guidelines as to what code style you will use and what practices to follow. Have a set (or variable) naming practice for your files so that every member of your team can write code that is consistent and well-organized and will be easier for you to test during the next stage. 

5. Testing one two

Before you even think about deployment, you need to be sure that everything works as intended. Testing at this stage should also be looking for any defects and any potential security vulnerabilities as well as integration testing. You may operate a test-driven environment and do all your testing in-house or, especially for larger projects, you may choose to have external beta testers.

There are various types of beta testing that can allow you to focus on special features such as headless CMS or test the product’s overall functionality. Testing different aspects of your software is essential as identifying and fixing any problems prior to deployment can save a lot of headaches later. 

6. Ready, steady, go

Once the testing process is complete – and any problems rectified – you’re ready to deploy the software. During the deployment phase, utilizing a cloud computer can be crucial for ensuring the scalability and reliability of the software. This allows for seamless access by end users from various locations, enhancing the overall deployment strategy and user experience

This could be to consumers as a purchasable software product or as part of the apps supporting a business capabilities model. Even with the most rigorous testing, your DevOps team should be monitoring use and any feedback that comes from end users as to whether the product met customer expectations or not. 

The thing to remember here is that while testing may have a small pool of testers, actual deployment will have a pool of thousands of users if not more. In an ideal scenario, your software will be deployed with no problems but the reality is that you will probably expect some issues that are hopefully only minor ones. 

Software development lifecycle security

Security is always going to be a primary concern when developing and deploying software. You need to be aware of the answers to several questions such as ‘What sort of data will the software be handling’ or ‘What is enterprise architecture management and what role will the software play in it?’

With SDLC, and your SDLD, security should not be seen as a separate stage but as an integral part of the process that is involved in every stage through DevSecOps practices. This methodology assesses security throughout the development process and looks at how secure the various features of the software are and how well it can stand up to potential threats once deployed.

These assessments can include tasks such as analysis of the architecture, automated detection, penetration testing, and code review. Your assessments should be part of the integrated development environment (IDE), servers used for the build, and code repositories. You should be looking to integrate DevSecOps into your SDLC in the following ways:

  • Planning analysis: In this stage, you should be identifying any security needs, mitigation plans, and potential threats the software may face.
  • Design. During the design stage, think about what features will meet your security needs. You could utilize threat modeling and a risk analysis of your planned architecture. You could also consider features such as encryption mechanisms and access control. 
  • Development and testing. You should be carrying out code reviews to ensure that any code meets your standards and that the security measures are implemented. During the testing phase, carry out tests such as penetration testing to identify any vulnerabilities. 
  • Deployment. There are automated DevSecOps tools that can help ensure and improve app security. You should also be looking at other factors such as access controls, firewalls, and security settings. 
  • Maintenance. Cybercriminals are constantly looking for new ways to infiltrate software and systems. Cybersecurity experts need to be just as proactive in finding ways to stop attacks. If any new risks or vulnerabilities arise, have your team look at any required updates or patches. 

The takeaway

Every software project is important, whether it’s for a gaming app or a commercial application. Both simple and complex projects should follow the SDLC and SDLD process. Your end goal is always customer satisfaction and to avoid as many errors as possible, especially when it comes to security. 

Having a software development lifecycle diagram to guide you through every development project can ensure that you follow all required steps and adhere to the relevant best practices. You should always be aware of the most common errors and be aiming for consistency from all of your development team. Keep to the plan and you’ll have a quality product and happy customers. 


Diana Nechita – Director of Product Marketing

Diana is the Director of Product Marketing at Ardoq. Her passion lies in fostering a deep understanding of Ardoq’s value in delivering tangible results for organizations navigating the complexities of digital transformation. This is her LinkedIn.

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Developing AI-Assisted Software with TensorFlow and Keras

In very simple words, AI-assisted software uses artificial intelligence to perform tasks that normally require human intelligence. You might be wondering what human skills can be replaced with AI, so here is the answer to your questions. AI-assisted software can recognize speech, make decisions really fast, and understand natural language.

From all this, you can guess why it’s such an important innovation. It fundamentally changes how we operate in different fields, making our work smooth and more efficient. AI also improves accuracy and decision-making in various fields. For example, one can apply it in healthcare for diagnosing diseases, in finance for fraud detection, and in customer service for chatbots.

You Main Helpers: TensorFlow and Keras

If you want to dig deeper into the topic, then check out two powerful tools for AI development: TensorFlow and Keras. We’ll talk about them below, but for starters TensorFlow is an open-source platform for machine learning and  Keras is a user-friendly neural networks API that runs on top of TensorFlow. What’s so great about them for developers? These two frameworks make it easier for tech specialists to build and train AI models, which are so helpful.

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Getting Started with TensorFlow and Keras

Before you reap all the benefits of these tools, you have to understand how to install them properly. We are not going to leave you alone with it, let us guide through this complicated process. 

1. Install Python

You might not understand what’s the point of this step but it’s very important. Here is why: TensorFlow and Keras are libraries built for Python, which is a programming language commonly used for AI and machine learning. That’s why you can’t use these great tools without installing Python on your computer first. Basically, it is an essential component to run these libraries and write your AI programs.

2. Install pip

The next step is also related to Python. You have to make sure that you have pip on your laptop as it simplifies the process of managing and installing Python packages. Thanks to pip, you can be calm knowing that you have the latest versions of essential packages and all the required dependencies. So, don’t skip this stage even if you feel tempted to do it.

3. Create a Virtual Environment

Unlike the previous necessary step, this one is optional. You can skip it if you want even though we don’t recommend doing it. To give you a deeper insight into this stage, let’s define a virtual environment. In short, it’s a space where you can install Python packages without affecting your system-wide Python setup. Why is this so important? 

Because this helps to manage dependencies and avoid conflicts between different projects. If it sounds too theoretical for you, let’s consider an example. Imagine that you have multiple projects at the same time and each of them requires  different versions of the same library. Virtual environment can come in handy in this case it ensures each of your projects has its own dependencies and versions.

4. Install TensorFlow

Now, let’s move to the most interesting part and the essence of this section, which is installing TensorFlow. Among the benefits of this tool, you get access to its powerful features for building and training machine learning models needed for your software. If you don’t want to handle all the  heavy lifting of complex mathematical computations, then go for this solution. All the experts say that it provides an extensive library of pre-built models and algorithms needed for their professional purposes. 

5. Install Keras

Now it’s time for Keras. This is a high-level neural networks API that runs on top of TensorFlow and simplifies processes. The point of Keras is that it builds and trains neural network models with  an intuitive and user-friendly interface. Obviously, this is one of the main things when it comes to user experience, so you can greatly benefit from Keras, no matter whether you are an experienced developer or a beginner. 

6. Verify the Installation

Verifying the installation ensures that TensorFlow and Keras have been installed correctly and are ready to use. By importing these libraries and checking their versions, you confirm that your setup is complete and functional. This step helps to catch any installation issues early on, ensuring a smooth start to your AI development journey.

Building Your First AI Model with TensorFlow and Keras

You must be scared to even think about such a big task ahead. But trust us, it’s going to be a fun and rewarding journey. And remember, there is only one way to eat an elephant: one bite at a time.

1. Setting Up Your Environment

Logically, you’ll need to set up your environment first. You should check if TensorFlow and Keras are installed properly and ready to use. 

2. Understanding Key Concepts

If you don’t understand basic concepts, you’ll be helpless. So let’s make sure you know what we are talking about. 

– Layers: Layers can be perceived as building blocks of your neural network. To dig deeper into the structure, each layer has neurons (or nodes) that process the input data and pass it along. If it’s still not clear enough, you can think of layers like a conveyor belt in a factory, where each station does something different with the product (your data) as it passes through.

– Neurons: If we continue to expand this metaphor, neurons are the workers at each station on our conveyor belt. As you may guess, their function is to receive input, apply a weight to it, and then pass it through an activation function to produce output.

– Activation Functions: Here is where things become more complicated. These kinds of functions introduce some complexity to our model because it’s their job to decide which neurons to activate based on the input they receive. Common ones are ReLU, sigmoid, and tanh. 

– Training: Please, don’t be fooled by the familiarity of this word because now, this is where the magic happens. Training is when your model learns from the data. It works the following way: you feed the data into the model, it calculates the error (the difference between what it predicted and the actual answer), and then it adjusts its weights to get better. This process is repeated many times (called epochs) until the model performs well enough to be functional.

3. Building the Model

Alright, now let’s end with terms and build our model. As a first step in this exciting process, you need to start by defining the structure of your neural network. And now it must become clear why we mentioned Keras. By using it, you stack layers and specify the type and number of neurons in each layer. If we were to provide a metaphor again, it’s like sketching out a blueprint for a building.

4. Compiling the Model

The next step brings you much closer to a ready software. Here, you have to compile the model so that it’s ready for training. To add details, this is where you specify:

  • the loss function (to measure error)
  • the optimizer (to adjust weights)
  • metrics (to evaluate performance).

5. Training the Model

Now it’s time to train the model you’ve carefully created in the previous steps! You’ll use your dataset, splitting it into training and validation sets. What is happening during training? In simple words, the model adjusts its weights to minimize the loss function. You might think that it’s the end of your developing journey, but it’s not true. There is one more important step left.

6. Evaluating the Model

Finally, you evaluate your model’s performance using test data. Otherwise, you never know if the model generalizes well and performs accurately on new, unseen data. 


AI-assisted software development with TensorFlow and Keras can deeply transform human experiences. This way, we can create intuitive, engaging, and accessible applications that truly resonate with users. As you venture into AI development, remember that you’re making the digital world a more connected and inclusive place. So, dive in with enthusiasm!

Author’s BIO

Dan Mathews is a seasoned AI developer and technology enthusiast. His main talent is to combine technical expertise with a deep understanding of user behavior. When he’s not coding, Dan offers dissertation writing services as he strives to help students articulate complex ideas. His dedicated work continues to inspire and empower others to create meaningful technological solutions.

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How to Optimize CSS and JS to Speed up your site?

Suppose you’ve noticed that your site is taking a bit too long to load. More often than not, the problem lies in its code. Whether it’s Cascading Style Sheets (CSS) or JavaScript (JS), it has to be optimized so the code can be processed faster. 

Doing so will instantly give a boost to your site’s speed and improve its performance as well. Having said that, there are many ways to go about this. 

In this article, we’ll cover most of them so if you want to learn how to optimize CSS and JS for speeding up your site, read on. 

Ways for Optimizing CSS and JS to Give a Boost to Your Site Speed

Optimize CSS and JS to Speed up your site

Below, we’re going to discuss the different ways in detail so you can understand better. An example will also be provided for each one. 

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1. Minification

Minification is the process of removing all the unnecessary things from a code without changing its functionality. These things mainly include the characters, whitespaces, and comments.

This reduces the size of your website code and the servers take less time processing it, resulting in faster load speeds. Having said that, you can minify your code manually by removing the aforementioned things. However, the manual process is only recommended when the original code isn’t too long. 

Website codes, whether CSS or JS are usually quite long, and manual minification isn’t suggested for them. This is because there are huge chances of errors and it’ll just take a lot of your time. Therefore, it’s best if you just run your site’s code through a tool like to automate the process. We personally do this as well whenever the need arises. 

To exemplify minification, below are examples for both CSS and JS. It is worth mentioning that we used the said tool to generate these examples. 

CSS Minification Example

Original CSS Code:

/* This is a comment */
body {
    background-color: white;
    font-size: 16px;

Minified CSS:


JavaScript Minification Example

Original JavaScript:

// This is a comment
function add(a, b) {
    return a + b;

Minified JavaScript:

function add(a,b){return a+b;}

2. Concatenation

Concatenation is to attach two strings to make a single long one. Combining multiple CSS and JS files into one file each can reduce the number of HTTP requests

The fewer number of HTTP requests are received, the better the site speed will be. While concatenation can be done manually using the “concat()” method, it is not recommended on large codes. This is simply because too much time will be required and you’ll be frustrated by the end. 

A better way is to use a tool like Gulp that can automate this process. That said, here’s what a concatenated JS code looks like using Gulp.

Example with Gulp

const gulp = require('gulp');
const concat = require('gulp-concat');

gulp.task('concat-css', function() {
    return gulp.src('src/css/*.css')

gulp.task('concat-js', function() {
    return gulp.src('src/js/*.js')

3. Asynchronous Loading

Asynchronous loading allows certain things like images or scripts of a webpage to be loaded in the background while the rest of the elements continue to load as well. This way, the rendering of the page won’t be hindered and the overall speed of your site will improve. 

To load JavaScript asynchronously, useasyncordefer attributes. What these attributes will do is the first one will signal the browser to download the script while the page is rendering without waiting for the download to be completed. 

The second attribute we mentioned instructs the browser to download the script in the background after some delay or wait until the page has finished rendering before executing the script and user has interacted with the page.

Below is an example JS code that showcases the use of both attributes for asynchronous loading. 

Async JavaScript

<script async src=""></script>

This code is an example of loading a Google AdSense script asynchronously.

Defer JavaScript

<script defer src=""></script>

This code is an example of loading a Google AdSense script deferred.

4. Inline Small CSS and JS

Inlining small CSS and JavaScript means including the code directly within your HTML file instead of linking to external files. This is done by placing CSS code within the relevant HTML tag as an attribute.

Having said that, if you inline small CSS and JS snippets that are essential for rendering above-the-fold content into your site’s HTML, its loading speed and performance can be increased since the number of HTTP requests and additional round trips will be lowered.

To exemplify this, an inlined CSS code is provided below. 

Inlined CSS

<h1 style="size:48px; color:#fff; margin: 10px;"> HELLO WORLD </h1>

5. Remove Unused CSS and JS

JavaScript and CSS files usually include the scripts and styling of the whole website. It can just so happen that the page the user is trying to load uses only a small portion of that file. To ensure maximum speed, it is important to remove all the unnecessary scripts needed to load a particular page. This is because the unused snippets of code won’t have to be executed in the background. 

Having said that, for CSS, a tool like PurgeCSS can work well and automate the process of removing unused strings. Conversely, for JavaScript, Webpacks’ tree-shaking feature is great. 

To install and configure the plugins of these two tools, enter the following codes and your site’s unused CSS and JS will be removed. 

Unused CSS Removal with PurgeCSS


npm install @fullhuman/postcss-purgecss


// postcss.config.js
const purgecss = require('@fullhuman/postcss-purgecss')({
  content: ['./**/*.html'],
  defaultExtractor: content => content.match(/[\w-/:]+(?<!:)/g) || []

module.exports = {
  plugins: [

Unused JS Removal with Webpack

Configuration File:

// webpack.config.js
module.exports = {
  mode: 'production',
  entry: './src/index.js',
  output: {
    filename: 'bundle.js',
    path: __dirname + '/dist'
  optimization: {
    usedExports: true,

These are the ways to optimize CSS and JS to give a boost to your site’s speed. With everything discussed, our post comes to an end. 

Final Words

When the need for website speed-up arises, you might have to optimize its code. Whether it’s in JavaScript or Cascading Style Sheets, optimizing isn’t an easy task. There are many ways to do it and in this article, we have discussed each one in detail so you can do it yourself easily. We’ve also mentioned some tools that can automate each way for assistance.

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Edge Computing and Machine Learning Key Insights

This blog post is powered by data from Developer Nation’s 26th global survey wave (conducted from November 2023 to January 2024), delves into the latest and most crucial developer trends for Q1 2024. With insights from over 13,000 developers across 136 countries, it’s a treasure trove of knowledge.

37% of developers who target non-x86 architectures write optimised code for Arm-based processors, making them the second most popular target behind microcontrollers (40%).

Nearly half (47%) of ML developers have deployed on-device AI solutions in the past 12 months. The top motivations for doing so are increased user privacy and faster inferencing.

The most popular on-device ML framework is Google MLKit, used by 46% of developers who deploy on-device AI solutions, followed by OpenCV (28%), PyTorch Mobile (26%), and TensorFlow Lite (25%).

The vast majority (86%) of developers working on Industrial IoT projects implement on or near-device solutions, with the most popular being on-device processing (26%), automation control (19%), and real-time analytics (17%).


Troubleshooting and Fixing Common Online Connectivity and Server Issues

In the digital age, connectivity, server reliability, and app testing are the cornerstones of a seamless online experience. Whether you run a private weblog, a bustling e-trade web page, or a complex internet application, encountering connectivity problems or server outages can be frustrating and disruptive. Here’s a complete manual for troubleshooting and solving common online connectivity, app testing, and server issues, ensuring your website stays accessible and efficient.

Understanding Common Connectivity Issues

Connectivity Issues can stem from a ramification of assets, ranging from network issues to server misconfigurations. here are a number of the most common issues you might come upon: 

Internet Connection Problems: Sometimes, the problem lies not with the server but with the internet connection. take a look at your modem, router, and network cables. Restarting your router or switching to a wired connection can regularly remedy these problems.

Optimising Server Performance with Java: Optimising Server Performance with Java involves leveraging the powerful features of Java to ensure your server runs efficiently and can handle high traffic without downtime. By implementing reliable Java services, you can enhance server responsiveness, manage resources effectively, and provide a seamless user experience. Key techniques include:

  • Java Servlets and JSP: Leveraging Java Servlets and JSP to handle dynamic content efficiently.
  • Thread Management: Using Java’s concurrency API to manage multiple threads and prevent server overloads.
  • Memory Management: Implementing effective garbage collection strategies to maintain optimal server performance.

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DNS Resolution Failures: DNS Issues can save you, users, from gaining access to your website. check if your DNS settings are accurate and take into account using a dependable DNS service like Google DNS ( and or Cloudflare DNS ( 

Also read: 6 Best AI Website Builders For Ecommerce

Server Overload: Excessive visitors can crush your server, causing slowdowns or outages. Use equipment like Google Analytics or server monitoring software programs to music visitor spikes and do not forget to scale your server assets. 

Firewall and Security Settings: Overly strict firewall policies or safety settings can block valid site visitors. ensure your firewall is configured efficiently and that your safety software is not overly competitive.

Diagnosing Server-Side Problems

Online Connectivity and Server Issues

When your website is down, diagnosing the hassle is essential. right here’s a step-by-step guide to figuring out and solving server Issues:

Check Server Status: If you suspect the ‘web server is down,’ the first step is to confirm the status. Use gear like UptimeRobot or Pingdom to test if your server is responding. This equipment can provide signals while your server goes down. 

Examine Server Logs: Access your server logs to discover any errors or warnings that befell across the time of the outage. Logs can offer insights into issues like failed login attempts, script errors, or resource limits being hit.

Restart the Server or Services: Sometimes, a simple restart can resolve many issues. Restarting your internet server (Apache, Nginx, and so forth.) or related offerings can clear temporary glitches. 

Check for Software Updates: Make sure that your server software program, CMS, and plugins are up to date. outdated software programs could have vulnerabilities and insects that would motivate your server to head down. ordinary updates are essential for keeping server fitness and security.

Also read – From Rendering to AI: 5 Reasons Why You Can Consider an NVIDIA GPU Dedicated Server?

Handling Specific Issues

Positive issues require focused troubleshooting. right here are a few techniques for managing precise issues:

Database Connectivity Issues: In case your website is predicated on a database, ensure that the database server is going for walks and reachable. check database credentials, server repute, and network connectivity. tools like phpMyAdmin or MySQL Workbench permit you to diagnose database Issues.

Resource Limitations: Server assets including CPU, memory, and disk space are finite. check-in case your server is walking out of assets. upgrade your website hosting plan or optimise your website online’s code and database queries to reduce useful resource consumption.

Configuration Errors: Misconfigurations in server documents (e.g., .htaccess, nginx. conf) can cause downtime. Double-test your configuration files for syntax mistakes or incorrect settings. the use of a configuration validator tool can assist spot errors. 

Preventive Measures and Best Practices

Prevention is higher than cure. implementing pleasant practices can notably lessen the probability of encountering server Issues:

Regular Backups: Often back up your internet site and server data. In the event of a crash or information loss, backups ensure you may restore your web page quickly. 

Monitoring and Alerts: Set up monitoring and alerting systems to maintain an eye on server performance and uptime. tools like New Relic, Datadog, or server-specific tracking answers can provide actual-time insights and indicators. 

Load Balancing: For excessive-site visitors sites, take into account imposing load balancing. This distributes site visitors throughout a couple of servers, improving overall performance and reliability. 

Work with a Website Development Expert: Collaborating with a reputable website development company or freelancer can help ensure your site is designed and implemented with best practices in mind, reducing the likelihood of connectivity and server problems down the road.

Security Best Practices: Keep your server comfortable by way of using strong passwords, allowing two-element authentication, and often updating software. additionally, remember the usage of an internet application firewall (WAF) to protect in opposition to common threats. 

By understanding and addressing these common issues, you can ensure that your website remains online and performs optimally. Remember, while encountering a ‘Web Server is Down’ message can be alarming, with the right tools and strategies, you can quickly diagnose and resolve the problem, minimising downtime and maintaining a seamless online experience for your users.

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Generative AI and Its Evolving Role in Software Development

Remember the days when software development was solely the domain of humans painstakingly writing lines of code? Those days are evolving rapidly. Generative AI, a branch of artificial intelligence capable of creating original content, is quickly becoming the co-pilot for software developers worldwide. This technology, leveraging advanced models like GPT-4, is not only automating mundane tasks but is also opening doors to unprecedented creativity and efficiency in the software development lifecycle.

 The Rise of AI Coding Companions

Generative AI models, like OpenAI’s ChatGPT or GitHub’s Copilot, have emerged as powerful allies for developers. These models can:

  • Generate Code: Need a function to sort a list? Just describe what you need, and the AI can generate the code for you.
  • Complete Code: Start typing a line of code, and the AI can suggest how to finish it, saving you keystrokes and brainpower.
  • Refactor Code: Want to clean up or optimize your code? The AI can suggest improvements.
  • Explain Code: Encountered a complex piece of code? Ask the AI to break it down for you in simple terms.
  • Detect Bugs: The AI can scan your code for potential bugs and suggest fixes, reducing the time spent on debugging.

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Boosting Productivity and Creativity

The benefits of these AI coding companions are clear:

  • Increased Efficiency: Developers can accomplish tasks much faster, from writing boilerplate code to debugging complex issues.
  • Enhanced Creativity: The AI can offer alternative solutions or suggest innovative approaches, sparking new ideas.
  • Reduced Tedium: Developers can focus on higher-level problem-solving instead of getting bogged down in repetitive tasks.
  • Accelerated Learning: Newcomers can learn faster by getting instant feedback and explanations from the AI.

Real-World Applications

Generative AI isn’t just a theoretical concept; it’s already being used in the real world:

  • GitHub Copilot: This popular tool integrates with various code editors, providing code suggestions and completions in real time.
  • Tabnine: Another AI-powered code completion tool that supports multiple languages and frameworks.
  • Replit Ghostwriter: This tool offers AI-powered code generation, completion, and transformation features.
  • Deep TabNine: A deep learning-based code completion tool that can be integrated with various IDEs and text editors.

Challenges and Considerations

While the potential of generative AI is exciting, there are also challenges to address:

  • Accuracy: AI-generated code might not always be perfect and may require human review and correction. While these tools can significantly speed up the coding process, developers should still verify the AI’s suggestions to ensure they meet required standards and project specifications.
  • Bias: AI models can inherit biases from their training data, leading to potentially biased or unfair code suggestions. This requires developers to remain vigilant and critically assess any suggestions made, ensuring equitable and inclusive coding practices.
  • Security: The security of code generated by AI needs careful consideration to prevent vulnerabilities. Developers must be aware of potential security gaps and rigorously test AI-generated code to protect against cyber threats and maintain the integrity of their applications.
  • Ethics: As with any technology, the ethical implications of AI in coding should be carefully evaluated and addressed. This entails considering the broader impact of AI-generated solutions and ensuring that their use aligns with ethical standards and promotes positive societal outcomes.

The Future of AI-Assisted Development

The future of AI software development services is undoubtedly intertwined with generative AI. As these models continue to improve, we can expect even more sophisticated tools that will:

  • Understand Natural Language Better: Allowing developers to communicate with AI in a more intuitive way. As natural language processing capabilities advance, developers will be able to describe the functionality they need in plain English, and the AI will generate the corresponding code, reducing the need for detailed programming knowledge.
  • Generate More Complex Code: Tackling larger, more complex programming tasks. Future AI models will be capable of handling intricate logic, cross-functional dependencies, and larger codebases, thus enabling the automation of more sophisticated software projects.
  • Integrate with More Development Tools: Becoming a seamless part of the developer’s workflow. As generative AI tools continue to evolve, their integration with a wider range of development environments, version control systems, and project management tools will ensure a smoother and more cohesive development experience.

The Developer Nation Survey, a comprehensive look at developer trends, already highlights a growing interest in AI tools for coding. This indicates a shift in how developers perceive and use AI, moving from skepticism to embracing its potential.


Generative AI is a game-changer for software development, offering a glimpse into a future where humans and AI collaborate to create more efficient, innovative, and secure software. While challenges remain, the potential benefits are too significant to ignore. As we move forward, developers who embrace these AI-powered tools will be well-positioned to thrive in the ever-evolving landscape of software development.

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Cloud Application Performance Monitoring: Strategies to Boost User Experience

The performance of cloud applications plays a big role in user experience. At the end of the day, we’re all looking for a smooth-running service that’s free from bugs and crashes. A slow-running app is a one-way street to frustrated users, but you can avoid this eventuality with Cloud Application Performance Monitoring (CAPM). 

CAPM is the process by which you track and manage the performance of cloud applications. It involves monitoring the key metrics that determine performance and taking measures to optimise where necessary. Overall, the goal is to achieve the best possible experience for all of your users. 

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What is CAPM?

Cloud application performance management is achieved by monitoring important performance metrics so that any issues looming on the horizon are caught early. It helps you to maintain the best user experience possible and should be a key part of your customer orientation strategy.

Core Components of CAPM

Let’s take a look at the essential elements of CAPM: 

  • Real-time monitoring: Application performance is continuously tracked to pick up issues instantly, meaning no problem goes undetected. For instance, a live chat app relies heavily on real-time monitoring to ensure smooth and uninterrupted user interactions, making it crucial to address any performance issues immediately.
  • Alerts and notifications: You can set up alerts to notify you when performance metrics drop out from the acceptable range, so you’re always kept in the loop.
  • Root cause analysis: Knowing there is a problem is only half the story. Root cause analysis will also tell you what the underlying problem is. 
  • Performance optimisation: This is where you swing into action to make the changes needed to get your cloud application performance back on track.

CAPM vs. Traditional APM

If you’re familiar with application performance monitoring, you might be wondering if CAPM is really so different. However, they are distinct in a few ways. 

Environment Focus

Traditional APM typically applies to on-premises applications and, as the name suggests, CAPM focuses on cloud-based applications. What’s important about this differentiation is that on-premises applications need to be monitored within a static, controlled environment, whereas cloud applications require a dynamic environment that can change based on usage and demand.


Cloud environments are scalable by nature, and CAPM is able to handle this. As user demand rises or drops, CAPM can likewise scale resources up and down, achieving optimal performance with minimum manual intervention. Meanwhile, traditional APM usually deals with fixed resources that need manual adjustments.


The transition from mainframe to cloud applications is an important consideration. CAPM integrates with cloud services and platforms to allow for comprehensive monitoring across different components within the cloud ecosystem. This provides a complete view of the performance of the application, whereas traditional APM tools are unlikely to offer the same level of integration with cloud-specific services, and are therefore less effective. 

Moreover, as CAPM integrates deeply with cloud services and platforms, it also expands the attack surface — the set of points where unauthorized access can occur. Therefore, it is crucial to ensure that security measures are part of the performance monitoring process to protect against vulnerabilities that could be exploited through these additional points of exposure.

Resource Allocation

APM’s primary focus is on the application itself, and this rarely widens to the infrastructure around it. CAPM, on the other hand, not only monitors the application’s performance but keeps an eye on the underlying infrastructure and resources. Memory, CPU, and storage are all tracked to assess if they are being used efficiently.

Key Metrics in CAPM

The effectiveness of CAPM lies in closely monitoring the right metrics.

Response Time

How long is a user waiting for an application to respond to a request? Faster response times keep everything ticking over quickly and, most importantly, keep users happy. High response times point to performance bottlenecks, suggesting improvement might need to be made in database indexing strategies or in optimising server configurations to speed up retrieval times.

Error Rates

Keeping on top of the frequency of errors happening within the application will tell you where there are common problems that are damaging the user experience. If errors are happening a lot users are likely to get frustrated quickly, and you may lose their trust. As such, lowering error rates should be a top priority.

Request Rates

How is the application managing traffic? This metric will tell you the volume of user requests currently being handled, which makes sure the application scales as needed. As high request rates can put a strain on resources, causing slowdowns or even crashes, they need to be monitored to keep performance steady during peak times.

Application Availability

Measuring the accessibility of an application and the extent to which it’s operational shows you how consistent your service is. High availability is a must to make sure users know they can rely on the application to be there when they need it, whereas excessive downtime can damage that trust.

Strategies for Effective Cloud Application Performance Monitoring

Use these tactics for CAPM to work at maximum effectiveness.

Ensure Compatibility with Existing IT Infrastructure

Effective software development practices are crucial for integrating CAPM tools with existing IT infrastructure, ensuring that all systems operate cohesively and are aligned with business objectives.

Before you go live, conduct some tests to make sure everything is working well together and the data you’re capturing is accurate. If you have a team, provide them with adequate training. 

Managing Real-Time Monitoring and Alerts

When you are informed in real-time of high error rates or slow response times, you can fix issues before they snowball and cause big problems for users. Configure alerts for specific performance thresholds, so you or your team members get immediate notification if anything doesn’t look right. 

For example, if the average response time of an application is usually 200 milliseconds, a consistent response time of 500 milliseconds for more than five minutes would send an alert. Every alert should be integral to performance and actionable, so when one comes through, you know it’s important. 

Additionally, your thresholds should be precisely defined based on historical data to balance sensitivity and relevance. There should also be clear incident management protocols that kick in, in response to an alert, with assigned roles and documented procedures. Maintain DevOps best practices by regularly reviewing and adjusting thresholds to maintain the highest performance standards for your cloud application.

If you use a WordPress site for your business, incorporating a WordPress table plugin can help you effectively organize and display performance data in a clear and accessible manner.

Root Cause Analysis

Finding out the underlying cause of cloud application performance issues requires effective Root Cause Analysis (RCA). This way, you both fix the problem and stop it from happening again. One of the best techniques for identifying the root cause of an issue is log analysis, where you examine the logs to find errors and anomalies. 

Another effective technique is transaction tracing, which tracks the journey of a transaction through an application to discover where bottlenecks are occurring. Performance profiling is also useful, monitoring resource usage and revealing where the most resources are being consumed.  

CAPM tools have advanced features on their dashboards that provide real-time and historical data, so you can detect what may be a recurring theme or a one-off anomaly. With error tracking, you can capture in-depth information about errors, such as stack traces and user actions, which are integral for root cause diagnosis. 

Tools should also offer synthetic monitoring, which simulates user interactions to test performance issues without affecting real users. 

Optimise Application Responsiveness

You’re looking to optimise application responsiveness wherever possible to reduce latency, provide quicker user interactions, and prevent negative repercussions for users. Suppose, for example, high latency was occurring in a cloud-based CRM platform. This could slow down valuable tasks like sales discovery calls, leading to irritated users and missed opportunities. 

Combat slow load times by using content delivery networks (CDNs), employing efficient caching strategies, and optimising database queries. Improved performance will give users confidence that the application will always allow them to complete tasks without delay. 

Ensure Application Availability

Maintain high availability and reduce downtime by establishing redundancy and failover mechanisms. Redundancy involves duplicating all the critical components and systems so that if one fails, another can take over without disrupting service. For instance, cloud-based call centre software might use redundant servers to ensure continuous operation, even during hardware failures.

Failover mechanisms play a similar role, automatically redirecting traffic to backup systems when the primary system is unavailable. This is invaluable for applications that have a big impact on sales or customer service, where downtime can be seriously costly. 

There is also a benefit to adopting cloud-native practices like auto-scaling and load balancing. Auto-scaling adjusts resources in real-time based on demand and prevents the overloading of servers, while load balancing spreads the traffic evenly across servers, improving application availability. 

Remember, all these mechanisms should be tested regularly to make sure they are ready when a real problem arises.

The Future of Cloud Application Performance Monitoring

CAPM will continue to be an area that improves as technology evolves. The impact of generative AI has already made itself known, as AI and machine learning fuel predictive analytics in CAPM. This technology supports proactive performance management, discovering problems before they start to affect users. Edge computing allows for data to be processed close to the source, reducing latency and improving responsiveness.  

Transform User Experience with CAPM

Providing the best possible user experience for your cloud-based applications is a top priority for any developer, and cloud-based performance monitoring is, without a doubt, one of the best ways to make it a reality. 

CAPM gives you the heads up as soon as anything deviates from acceptable standards, so you can take action immediately. The visibility it gives you means no delays or bugs will go unnoticed and you can both fix issues promptly and understand how to stop them happening again. 

Altogether, it’s the ideal approach to keep your application working perfectly and your users satisfied and happy.

Austin Guanzon – Tier 1 Support Manager

Austin Guanzon is the Tier 1 Support Manager for Dialpad, the leading AI-powered customer intelligence platform. He is a customer retention and technical support expert, with experience at some of the largest tech service companies in the US.You can find him on LinkedIn.

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9 Best Security Practices For REST API Development

If you work in any area of application programming interface (API) development, you’ll know that there are always concerns. Will the software manage errors effectively? How will it cope with large datasets? The list seems endless at times. One major concern many developers focus on is the security threats their APIs may face. 

With the cost of cybercrime expected to reach $13.82 trillion by 2028, the issue of cybersecurity is very real. If you are developing REST APIs, what threats might you face, and how should you be tackling them? Should you have a checklist of best practices during development to minimize any potential threats?

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What is REST API?

Security Practices For REST API

An application programming interface (API) is a list of protocols and definitions developers use when building application software. To define it simply, it’s a ‘contract’ between the application user and the application. You could also see it as a communication conduit that communicates a request and allows for an exchange of information and/or data.

REST API (you may also hear the term RESTful API) is an application programming interface developers utilize when working in the REST (representational state transfer) architecture that allows for communication between RESTful web services. 

REST API security threats

Security Practices For REST API

To paraphrase Sun Tzu, knowing your enemy is crucial. By knowing what cyber security threats (especially the most common ones) your REST API may face, you can better plan for how to prevent those threats. 

We’ve listed some of them below:

  • Denial of Service (DoS): When a DoS attack occurs, the system is overloaded by an enormous amount of messages sent by a cybercriminal. If your REST API experiences a successful DoS attack, then it could be rendered non-functional and accessed by the attackers. 
  • Injection attack: This attack can allow cybercriminals access to often sensitive data and information. The attacker embeds a dangerous code into unsecured programs, often SQL injection or cross-site scripting. 
  • Sensitive data exposure: If there is a lack of encryption at any point in how your API handles data, then it may be exposed to attack. When you consider that a lot of data (health details, credit card info, etc.) is highly confidential, unsecured data can be a major risk. 
  • Broken authentication: If you have inadequate or missing authentication, you are leaving your API and app open to a cyberattack. From passwords to JSON web tokens to API keys, this can be a major weak point if not tackled. 
  • Parameter tampering: If a cybercriminal manipulates the parameters that are exchanged between user and server, they can modify various data in the application such as prices, product quantities, and user credentials. This can pose a major risk to enterprise collaboration systems
  • Man in the Middle (MITM): As the name suggests, with this type of attack, the cybercriminal positions themselves between two systems and intercepts the communications. This allows them to alter or steal any confidential data. There are two stages to MITM attacks; interception and decryption.
  • Broken access control: Access control (or authorization) is how you limit access to some functions or contents. If your access control is faulty or flawed, attackers can access data or take control of accounts. 

Moreover, Implementing proxy detection mechanisms can help identify and mitigate attacks originating from suspicious or anonymized sources, adding an essential layer to your security framework.

9 best security practices for REST API development

Security Practices For REST API

You are now aware of some of the most common security threats your REST API may face. You have to assume that any program or system is under threat, whether it is a banking app or an AI customer care program. In the development stage, what security measures should you implement or advise users to use?

1. TLS (transport layer security) encryption 

The data transferred by your API—such as B2B intent data—is important and can have varying degrees of confidentiality. If you use TLS for your API, then all communications between the end user and the application will be encrypted. 

TLS is not only good for your REST API but also for your web app. It will also secure any authentication credentials such as passwords, tokens, or API keys. 

2. Have a robust authentication and authorization model

You may use common techniques—such as security tokens or API keys—to manage access to your REST API. However, managing those keys and tokens can present its own challenges. 

The complexity of managing those access options can lead to security vulnerabilities for your REST API. You can reduce security risks in this area by integrating your API with an identity management system that will both issue and authenticate tokens and keys. You can also use a centralized gateway for your API that will protect your data.

3. Keep URLs free of sensitive information

One of the most common design flaws with REST APIs is the inclusion of sensitive information in the URL. This can include things such as API keys, user credentials, or tokens. Even if you are using TLS, cybercriminals can easily discover this information. 

You also have to consider that your URL may be logged frequently by the servers it passes through and any networking devices on the API’s data path. This can expose any sensitive information to further threats. Always ensure that any URL you use is free of all sensitive data and that you follow online security protocols.

4. Utilize the cloud for large API security datasets

Security Practices For REST API

If you operate your API security on-premises, then you will have a limit when it comes to analyzing activity. Not only are you limited to short windows, but that API data is then discarded. Given that many cyberattacks are ‘slow burn’ and can happen over weeks or months, this can render your security ineffectual. 

If you instead use the cloud for data from your API’s activity, you are accessing the computational power and scalability to analyze activity over longer periods. It also means you can conduct more detailed analyses and boost your security. 

5. Use behavioral analytics

The power afforded you by using the cloud for API activity data also means that, once you have accumulated enough activity data, you can use behavioral analytics. Behavioral analytics can be very useful when it comes to formulating an outbound sales strategy, but it can also be an important tool in your security strategy. 

Furthermore, incorporating tools like a cold emailing tool to enhance engagement can optimize your interactions with potential clients, leveraging the insights gained from your data analysis. This approach not only supports sales initiatives but is also an important tool in your security strategy.

The first benefit of applying behavioral analytics is that it identifies all the players and may include end users as well as legitimate business processes. You can then identify ‘normal’ patterns of usage and, from that, make it easier to identify any ‘abnormal’ behavior that may indicate a security threat.

6. Implement continuous discovery

It’s not always about the REST API you’re developing now. Even with the best security measures, developers can be caught out by ‘shadow’ APIs. These could exist in old legacy infrastructure or may have been implemented outside of your normal processes. Whatever their origins, they can pose a real threat to your API’s security. 

Utilizing collaboration software in this continuous discovery process can ensure that information about all APIs is shared and understood by all relevant teams, enhancing transparency and proactive management.

By implementing continuous discovery, you can build an inventory of all APIs. You should be looking at data from API activity that includes the following sources:

  • Any API gateways
  • Your content delivery networks (CDN)
  • Cloud provider logs
  • Log management systems

Analyzing the data collected from these sources will identify all APIs in use across your systems. If you find other REST APIs that are now defunct but causing issues, you can look to remove or decommission them.

7. Provide narrow definitions for requests and responses

Cybercriminals look to utilize APIs in malicious ways. This means that a request (or response) may not be what it purports to be. By providing narrow definitions for API requests—such as format, parameter types, length, etc.—you reduce the chances of an attack using requests to your API. 

It can also help if you extend these narrow definitions that your REST API is able to provide. Consider limiting the responses to content types such as GET or POST. 

8. Share and collaborate

It may seem obvious, but one of the best security practices you can follow is to share and collaborate. Highlighting how your REST API is being used, and what security threats it faces (or any vulnerabilities you may have identified) and sharing that information with your DevOps team and other relevant personnel can help mitigate risk. This can be especially helpful if you have teams using cross-platform development tools.

9. Be proactive and hunt for threats

Don’t wait till threats become a very real risk, seek them out so you can take action. If you do wait, then there is a chance that a risk becomes an incident, one that could damage your business. Implementing preventive maintenance for your systems and regularly updating security protocols can further strengthen your defenses against potential breaches.

If you go looking for threats, you may find there have been unsuccessful attempts but these can help you find weaknesses and shore them up. 

Close analysis of your API’s usage activity can also expose any previously undiscovered vulnerabilities before they are exploited. As the saying goes, prevention is better than cure.

Security Practices For REST API

The takeaway

As cyber criminals get cleverer and find new and innovative ways to mount attacks, you need to keep up with them or ideally ahead of them. These criminals often find APIs as a convenient way of gaining access to an app or system and stealing any data and information used and stored there. 

There will always be attacks and there will always be vulnerabilities with REST APIs, but developers have a responsibility to reduce and mitigate any identified risks. By following these best practices, you are taking an important step to making your API less prone to any attack.

Austin Guanzon – Tier 1 Support Manager

Austin Guanzon is the Tier 1 Support Manager for Dialpad, the leading AI-powered customer intelligence platform. He is a customer retention and technical support expert, with experience at some of the largest tech service companies in the US.You can find him on LinkedIn.

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What is Developer Experience and Why Should You Care?

With all the talk of users and customers, you’d be forgiven for thinking they were the only stakeholders who matter. However, the truth is that for any company producing software, their dev teams matter at least as much. Developer experience should be a top priority for businesses in 2024.

What is developer experience?

Developer experience (DX) is an umbrella term for everything affecting the way software developers work. It shares many of the same factors as user experience, such as accessibility and engagement. In fact, when it comes to applications for software development like APIs or SDKs, user and developer experience are one and the same.

However, developer experience often refers to the experience of the team creating software to be sold by your business. Say you have a team designing Contact Center as a Service (CCaaS) software. You’ll need a solid developer experience strategy for your team to work efficiently and support the product long-term.

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Why it matters

Behind every successful app or web service are a vast number of work hours and the contributions of any number of people. Poor developer experience is rife with obstacles and inefficiencies. Perhaps there isn’t a quick way of mediating between different dev apps and data collection tools, for example.

It’s important to resist the “if it ain’t broke, don’t fix it” mentality with things like this. Even if your team is used to working around a given issue, it’s still a drain on time and energy, which means less left over for innovation or running additional product testing.

Of course, developer experience isn’t just about workflow or setup. It’s also about the support you provide your dev team as their employer. After all, it’s important to keep your people fresh if you want them to do their best work.

For a glimpse of what happens when dev teams don’t get this support, look at Developer Nation’s State of Developer Wellness report for 2024.

83% of surveyed developers reported experiencing burnout at some stage in their careers. 84% had to work overtime “at least occasionally.” 38% reported dissatisfaction with this, and a further 8% reported overwhelming dissatisfaction.

Of course, some overtime is inevitable in full-time work. It’s still important, however, to consider when it may be unnecessary or excessive.

It’s also important to remember that burnout isn’t merely being tired at the end of a long week. It’s the cumulative result of ongoing stress and exhaustion. If someone regularly gets given unreasonable workloads, they’ll still be there at the end of a weekend off.

The responsibilities of DX teams and specialists

Developer Experience

Developer experience hasn’t historically been considered to be as essential as user experience, but many tech companies have DX specialists or even dedicated teams nowadays. These personnel help to keep things running smoothly by maintaining support processes and identifying potential blockers.

Before we get into how to improve your company’s developer experience, let’s look at some of the core responsibilities for your DX team.

Technical documentation

Technical documentation may not be the most interesting thing in the world, but it’s essential for keeping your people on the same page. Think patch notes, user manuals, or any additional knowledge resources.

To further empower developers, incorporating self-service resources within this documentation can be a game changer. This acts as a knowledge base from which developers can find solutions and learn independently, enhancing their ability to handle challenges more efficiently and reducing downtime.

Each iteration of your platform could include any number of changes, large or small. As such, technical documentation is essential for your people to keep track of them. This helps to standardize their approaches and keeps different devs from accidentally working against each other.

Proper documentation is also the best tool for getting new members of your team up to speed on how the product works.

Let’s say you’re designing a platform as part of plans to start a virtual call center, and you’ve designed your own proprietary VoIP system. New hires need a working knowledge of the digital architecture before they can meaningfully contribute to the development process. Otherwise, they’ll be operating on potentially incorrect assumptions, which is a quick path to bugs and incompatibility errors.

Establishing efficient workflow procedure

While all roads may lead to Rome, some definitely get there more quickly than others. As such, another key developer experience responsibility is to define workflow.

You need to set up efficient procedures for everything, from stack management and testing to the documentation underpinning everything. This helps to prevent internal bottlenecks, as well as mistakes and oversights.

For example, let’s say you want to include more stringent review processes for work on the stack. Let’s see how that might look using a product landing page as an example:

  • Product landing page ready for the stack.
  • The page is queued for review by two or more colleagues (they check the code for errors, similar to how you might proofread a written document).
  • In case of errors, the landing page returns to the stack for alterations.
  • Queued for re-review.
  • A/B testing queue.
  • Final report.

Choosing the best tools and programming languages

The saying “a poor craftsman blames their tools” arguably doesn’t apply when it comes to the developer experience. Between APIs, SDKs, and programming languages, devs have all sorts of options for how to start building virtual apps and interfaces.

Add to that all the secondary and tertiary applications, like data analysis AI or stack management software, and the sheer volume of tools and techniques for developers is mind-boggling.

Effective internal tools streamline these processes by automating routine tasks, ensuring that developers can focus more on creativity and less on cumbersome workflows.

Whichever options you choose, it’s important to keep things standardized across your business to prevent compatibility issues.

The best ways to support and improve developer experience

If you thought DX work began and ended with the above responsibilities, then think again. There’s so much more you can do to create an innovative, supportive developer experience for your business.

Enable innovation and creativity

Designing a virtual product can so often feel like an exercise in hitting points on a checklist. Customer portal login? Check. Product search function? Check. Personalized recommendation system? Check.

To further enhance our team’s ability to innovate, we encourage developers to pursue further education, such as a certificate in data science, equipping them with advanced analytical skills that are essential in today’s tech landscape.

The importance of upskilling cannot be overstated, as it ensures developers stay current with emerging technologies and industry standards, which is crucial for maintaining competitive and innovative teams.

But a solid developer experience offers countless opportunities to experiment and think outside the box. Compare a product that was rushed out the door to one which had time to breathe, and the difference is like night and day.

Let’s revisit our example of developing for a virtual call center. Your major checklist might include:

  • VoIP functionality.
  • Customer or client database.
  • Product or service knowledge database.

That might be all call center workers need, strictly speaking. Given the time and resources to innovate, however, you can come up with all kinds of functions and features, like a call center quality monitoring scorecard to help track worker performance and customer attitudes.

Support developer wellbeing

Employee wellbeing is an essential priority for any successful business. It’s influenced by things like workload, working conditions, employer supportiveness, and even employer ethics. After all, people want to think they’re working for the good guy. ESG reporting can help companies stay on top of their moral and societal obligations like fair pay and green practices (if you find yourself asking, “what is an ESG report?” take this as a sign to look into them!).

We’ve already mentioned the prevalence of developer burnout, but it’s important to understand the consequences of letting it go on unchecked. Burnout can deal serious, lasting damage to a person’s mental and even physical health.

Victims of burnout often need a long time to recover. Many end up quitting their role, or even their entire industry or sector altogether. As such, if you don’t want attrition to claim your top dev talent, you need to take preventative measures. These include:

  • Minimizing overtime wherever possible.
  • Appropriate, reasonable goal-setting (such as SMART Goals).
  • Hiring enough people to handle projects and absences.
  • Providing sick days and PTO, and encouraging their use.
  • Enabling job flexibility for developers.

Provide job flexibility

If your experience as a manager has been more traditional, learning how to manage remote developers effectively can seem quite daunting. However, the COVID-19 pandemic showed that remote work and other types of job flexibility… well, work.

After all, everyone has some sort of life outside of work, whether it’s family commitments, side hustles, or active social lives. Job flexibility allows people to more effectively maintain their work/life balance. This helps to prevent personal problems from spilling into the workplace.

It’s never been easier for businesses to support hybrid or remote teams, making for a far more flexible developer experience.

Make web analytics and app stats front and center

It’s often not just a product or service’s initial launch you’ve got to worry about. You also need to consider how it’ll be supported going forward. For this, a steady supply of web analytics is absolutely essential.

Accessible KPIs, like CTRs, conversions, ad engagement, and general traffic help devs to see what is and isn’t working. Of course, monitoring these things over time can turn into busy work unless you make them easy to find.

Develop a system to display them in real-time, or periodically update documentation on your developer portal. Either way, it’s in your best interests to serve that info up on a silver platter.

A great developer experience gives you an edge on competitors

If you’ve read this far, then you know that prioritizing developer experience isn’t just great for your dev team. It’s essential if you want your software brand to succeed.

DX is now an essential aspect of any tech company’s workplace culture, particularly useful for keeping your team fresh enough to offer their best. On top of that, standardized tools and practices help to prevent conflicting efforts, wasted resources, and issues with compatibility.

In other words, if you don’t have a dedicated DX team, or even a single specialist, then that absolutely needs to change.

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Austin Guanzon – Tier 1 Support Manager

Austin Guanzon is the Tier 1 Support Manager for Dialpad, the leading AI-powered customer intelligence platform. He is a customer retention and technical support expert, with experience at some of the largest tech service companies in the US. You can find him on LinkedIn.