Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
If you’re serious about starting and growing an ecommerce store, Shopify is truly the best website builder for you. It’s been around for almost 20 years and is the gold standard for selling online. If you haven’t looked into it, I encourage you to do so. If you HAVE looked […]
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When it comes to Java web servers, Apache Tomcat remains a strong favorite. Some of these instances have been containerized over the years, but many still run in the traditional setup of a Linux-based virtual machine or even on bare metal.
Red Hat JBoss Web Server (JWS) combines the servlet engine (Apache Tomcat) with the web server (Apache HTTPD), and modules for load balancing (mod_jk and mod_cluster). Ansible is an automation tool that provides a suite of tools for managing an enterprise at scale.
It really doesn’t matter which email marketing service you choose–there’s a learning curve no matter what, they all do most of the same things, and it’s relatively easy to switch between them. Sure, some are better for different situations, experience levels, goals, and types of businesses. But I encourage you […]
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Attention Deficit Hyperactivity Disorder (ADHD) presents a complex challenge in the field of neurodevelopmental disorders, characterized by a wide range of symptoms such as inattention, hyperactivity, and impulsivity that significantly affect individuals' daily lives. In the era of digital healthcare transformation, the role of artificial intelligence (AI), and more specifically Generative AI, has become increasingly pivotal. For developers and researchers in the tech and healthcare sectors, this presents a unique opportunity to leverage the power of AI to foster advancements in understanding, diagnosing, and treating ADHD.
From a developer's standpoint, the integration of Generative AI into ADHD research is not just about the end goal of improving patient outcomes but also about navigating the intricate process of designing, training, and implementing AI models that can accurately generate synthetic patient data. This data holds the key to unlocking new insights into ADHD without the ethical and privacy concerns associated with using real patient data. The challenge lies in how to effectively capture the complex, multidimensional nature of ADHD symptoms and treatment responses within these models, ensuring they can serve as a reliable foundation for further research and development.
Trained neural networks arrive at solutions that achieve superhuman performance on an increasing number of tasks. It would be at least interesting and probably important to understand these solutions.
Interesting, in the spirit of curiosity and getting answers to questions like, “Are there human-understandable algorithms that capture how object-detection nets work?”[a] This would add a new modality of use to our relationship with neural nets from just querying for answers (Oracle Models) or sending on tasks (Agent Models) to acquiring an enriched understanding of our world by studying the interpretable internals of these networks’ solutions (Microscope Models). [1]
Artificial intelligence (AI) is revolutionizing customer service across industries by enabling efficient issue resolution, personalized recommendations, and omnichannel integration. AI-powered self-service, intelligent chatbots, sentiment analysis, predictive modeling, process automation, and data-driven insights are key technologies improving customer satisfaction and operational efficiency. Leading brands across retail, telecom, financial services, healthcare, and other sectors are using AI to transform their contact centers and customer support functions.
This article will analyze the breadth of AI adoption for customer service, benefits attained through reduced call volumes and enhanced issue containment, constructive use cases for total experience delivery, implementation challenges related to data integrity and ethical concerns, best practices for change management, workforce enablement, and responsible AI governance. Global examples from Microsoft, American Express, Disney, and Anthem demonstrate AI's transformative impact in building next generation customer service ecosystems geared for higher productivity and predictive support at scale. This article also looks ahead to examine AI's expanding role in customer intelligence, predictive analytics, service ecosystems, and creating sustainable competitive differentiation going forward.
Redis is a popular in-memory data store known for its speed and flexibility. It operates efficiently when a particular workload's memory and computational demands can be managed within a single node. However, scaling beyond a single node often leads to the consideration of the Redis Cluster. There's a common assumption that transitioning to the Redis Cluster is straightforward and existing applications will behave the same, but that's not the case in reality. While Redis Cluster addresses certain scaling issues, it also brings significant complexities. This post will discuss the limitations of scaling with Redis Cluster, and introduce some simpler alternatives that may meet many organizations' needs.
Redis Cluster is a distributed implementation that allows you to share your data across multiple primary Redis instances automatically and thus scale horizontally. In a Redis Cluster setup, the keyspace is split into 16384 hash slots (read more here), effectively setting an upper limit for the cluster size of 16384 master instances. However, in practice, the suggested maximum size is on the order of ~1000 master instances. Each master instance within the cluster manages a specific subset of these 16,384 hash slots. To ensure high availability, each primary instance can be paired with one or more replica instances. This approach, involving data sharding across multiple instances for scalability and replication for redundancy, is a common pattern in many distributed data storage systems. A cluster having three primary Redis instances, with each primary instance having one replica, is shown below:
Are you developing Google Sheets functions or Google Workspace add-ons that tap into the power of Google Gemini AI or OpenAI? This tutorial explains how you can use Google Apps Script to verify that the API keys provided by the user are valid and working.
The scripts make an HTTP request to the AI service and check if the response contains a list of available models or engines. There’s no cost associated with this verification process as the API keys are only used to fetch the list of available models and not to perform any actual AI tasks.
The snippet makes a GET request to the Google Gemini API to fetch the list of available models. If the API key is valid, the response will contain a list of models. If the API key is invalid, the response will contain an error message.
const verifyGeminiApiKey = (apiKey) => {
const API_VERSION = 'v1';
const apiUrl = `https://generativelanguage.googleapis.com/${API_VERSION}/models?key=${apiKey}`;
const response = UrlFetchApp.fetch(apiUrl, {
method: 'GET',
headers: { 'Content-Type': 'application/json' },
muteHttpExceptions: true,
});
const { error } = JSON.parse(response.getContentText());
if (error) {
throw new Error(error.message);
}
return true;
};This snippet works with Gemini API v1. If you are using Gemini 1.5, you need to update the
API_VERSIONvariable in the script.
The Apps Script snippet makes a GET request to the OpenAI API to fetch the list of available engines. Unlike Gemini API where the key is passed as a query parameter in the URL, OpenAI requires the API key to be passed in the Authorization header.
If the API key is valid, the response will contain a list of engines. If the API key is invalid, the response will contain an error message.
const verifyOpenaiApiKey = (apiKey) => {
const apiUrl = `https://api.openai.com/v1/engines`;
const response = UrlFetchApp.fetch(apiUrl, {
method: 'GET',
headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${apiKey}` },
muteHttpExceptions: true,
});
const { error } = JSON.parse(response.getContentText());
if (error) {
throw new Error(error.message);
}
return true;
};In the dynamic world of cloud-native technologies, monitoring and observability have become indispensable. Kubernetes, the de-facto orchestration platform, offers scalability and agility. However, managing its health and performance efficiently necessitates a robust monitoring solution. Prometheus, a powerful open-source monitoring system, emerges as a perfect fit for this role, especially when integrated with Kubernetes. This guide outlines a strategic approach to deploying Prometheus in a Kubernetes cluster, leveraging helm for installation, setting up an ingress nginx controller with metrics scraping enabled, and configuring Prometheus alerts to monitor and act upon specific incidents, such as detecting ingress URLs that return 500 errors.
Prometheus excels at providing actionable insights into the health and performance of applications and infrastructure. By collecting and analyzing metrics in real-time, it enables teams to proactively identify and resolve issues before they impact users. For instance, Prometheus can be configured to monitor system resources like CPU, memory usage, and response times, alerting teams to anomalies or thresholds breaches through its powerful alerting rules engine, Alertmanager.
With 2023 in the rearview mirror, it's fair to say that OpenAI's release of ChatGPT just over a year ago threw the tech industry into an excited, manic state. Companies like Microsoft and Google have thrown tremendous resources at AI in order to try to catch up, and VCs have tripped all over themselves to fund companies doing the same. With such a tremendous pace of innovation, it can be difficult to spot what's coming next, but we can try to take clues from AI's evolution so far to predict where it's headed. Here, we present 10 bold predictions laying out how emerging trends in AI development are likely to play out in 2024 and beyond.
While some consumers were awed by the introduction of ChatGPT, perhaps many more picked it up, played with it, and moved on with their lives. But in 2024, the former audience is likely to re-engage with the technology, as the trend towards personal AI will revolutionize user interactions with technology. These AI systems, trained on individual user data, offer highly personalized experiences and insights. For example, Google Gemini now integrates with users' Google Workspace data, enabling it to leverage everything it knows about their calendars, documents, location, chats and more. Meanwhile, companies like Apple and Samsung are likely to emphasize on-device AI as a key feature, prioritizing privacy and immediacy. It's not hard to imagine a personal AI with access to all of your data acting as a relationship, education, and career coach, becoming a more integral, personalized part of everyday life.
Surprise! This is a bonus blog post for the AI for Web Devs series I recently wrapped up. If you haven’t read that series yet, I’d encourage you to check it out.
This post will look at the existing project architecture and ways we can improve it for both application developers and the end user.
Generative AI, a type of AI that can create new data or content, is revolutionizing business process automation. By utilizing generative AI, businesses can streamline and enhance various processes, resulting in increased productivity, efficiency, and innovation. One of the key advantages of generative AI in business automation is its ability to speed up content creation.
With generative AI, businesses can produce high-quality writing in seconds, reducing the time and effort required to develop marketing copy, technical materials, or any other written materials. Generative AI can also assist in software development, generating code that is largely correct and instantaneous. This allows IT and software organizations to accelerate their development cycle, saving time and resources.
When it comes to keeping customers on the line, there are four key call center IVR (Interactive Voice Response) practices that make a real difference. These practices are vital because they address common reasons why customers hang up—and can help you prevent customer hangups in the future. We’ll break down […]
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In the dynamic landscape of software development, where change is the only constant, the ability to adapt quickly is paramount. This adaptability hinges on the flexibility of our codebases, which can be significantly enhanced through the judicious use of refactoring techniques. Among these techniques, the extraction of interfaces is a powerful tool for architecting robust and agile systems. In this article, we’ll explore the significance of interface extraction in refactoring, using a practical example from e-commerce development to illustrate its transformative potential.
Creating software that gracefully accommodates change is a hallmark of practical software engineering. Yet, striking the balance between adaptability and complexity can be challenging. It's tempting to preemptively introduce numerous layers and interfaces in anticipation of future requirements, but this approach can often backfire. As the adage goes, "We might need it in the future," but over-engineering for hypothetical scenarios can lead to unnecessary complexity and maintenance overhead.
The interest and investment in AI is skyrocketing, and generative AI is fueling it. Over one-third of CxOs have reportedly already embraced GenAI in their operations, with nearly half preparing to invest in it.
What’s powering it all, AI chips used to receive less attention. Up to the moment, OpenAI’s Sam Altman claimed he wants to raise up to $7 trillion for a “wildly ambitious” tech project to boost the world’s chip capacity. Geopolitics and sensationalism aside, however, keeping an eye on AI chips means being aware of today’s blockers and tomorrow’s opportunities.
SIEM solutions didn't work perfectly well when they were first introduced in the early 2000s, partly because of their architecture and functionality at the time but also due to the faults in the data and data sources that were fed into them.
During this period, data inputs were often rudimentary, lacked scalability, and necessitated extensive manual intervention across operational phases. Three of those data sources stood out.
A sign of a good understanding of a programming language is not whether one is simply knowledgeable about the language’s functionality, but why such functionality exists. Without knowing this “why," the developer runs the risk of using functionality in situations where its use might not be ideal - or even should be avoided in its entirety! The case in point for this article is the lateinit keyword in Kotlin. Its presence in the programming language is more or less a way to resolve what would otherwise be contradictory goals for Kotlin:
NullPointerException if they are accessed without a value being provided beforehand.The problem here is this: what happens when it’s impossible to declare a class field’s value immediately? Take, for example, the extension model in the JUnit 5 testing framework. Extensions are a tool for creating reusable code that conducts setup and cleanup actions before and after the execution of each or all tests. Below is an example of an extension whose purpose is to clear out all designated database tables after the execution of each test via a Spring bean that serves as the database interface: