Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
The only thing you need to crank up those call center sales is speed. And it’s not just us saying …
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AI-powered tools for writing code, such as GitHub Copilot, are increasingly popular in software development. These tools promise to boost productivity, but some also claim that they democratize programming by allowing non-programmers to write applications.
But how do we actually know whether the code written by an AI tool is fit for purpose?
JWTs or JSON Web Tokens are most commonly used to identify authenticated users and validate API requests. Part of this verification process requires the use of cryptographic keys to validate the integrity of the JWT to make sure it has not been tampered with. The set of keys used for this process is called JWKS or JSON Web Key Set. In this blog post, we will go over what JWKS are and how they are used.
JSON Web Keys (JWKs) are a JSON data structure that represents cryptographic keys. These keys are primarily used for verifying JWTs in OAuth flows. JWKs are designed to be easily exchanged, making them a standardized and interoperable format for representing cryptographic keys.
Micro frontends are an architectural approach that extends the concept of microservices to the front end of web applications. In a micro frontend architecture, a complex web application is broken down into smaller, independently deployable, and maintainable units called micro frontends. Each micro frontend is responsible for a specific part of the user interface and its related functionality.
Key characteristics and concepts of micro frontends include:
Cloud solutions are becoming increasingly prevalent. I’ve observed their adoption even among companies that were traditionally very conservative. Previously, these organizations insisted that no data leave their premises, operating all applications within data centers safeguarded by two-meter-thick, steel-reinforced concrete walls. However, these companies are now beginning to explore and adopt cloud solutions, simultaneously becoming aware of the true costs associated with cloud computing.
In this article, I will delve into the costs associated with cloud solutions. While this is not a technical piece, a basic understanding of cloud computing might be beneficial, though I will aim to provide an overview rather than delve into technical specifics.
Do Not Call (DNC) scrubbing is the step telemarketers take to cross-reference their call lists with the Federal Trade Commission’s …
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In Part 1, we looked at the overview of FinOps as an evolving practice to deliver maximum value from investments in the cloud. We also discussed the challenge or the need for shifting left FinOps for better optimization of your cloud usage and cost. In part 2 of the series, we will walk you through the steps on how you go about creating the FinOps cost model for an example solution.
There are 3 steps to creating and implementing the cost model:
In the rapidly evolving landscape of security technologies, remote video surveillance has emerged as a powerful tool to protect homes, businesses, and public spaces. Leveraging the advancements in camera technology, connectivity, and artificial intelligence, remote video surveillance provides a proactive approach to security, allowing real-time monitoring and response. This article explores the key components, benefits, and challenges of remote video security surveillance.
Remote video surveillance has revolutionized the way we approach security, offering real-time monitoring, cost-effective solutions, and scalability. As technology continues to advance, the integration of artificial intelligence, improved analytics, and enhanced cybersecurity measures will further strengthen the effectiveness of remote video surveillance systems. By addressing privacy concerns and diligently managing potential challenges, businesses and individuals can harness the power of this technology to create safer environments.
The core goal of recommender systems is to predict the most likely next interaction by modeling the user's historical behavior. This goal is particularly challenging when there is limited user interaction history, which has long plagued the development of recommender systems, known as the cold-start problem. In cold-start scenarios, such as in newly established recommendation platforms with limited interaction sequences for new users, the early stages of model training often suffer from a lack of sufficient sample data. Modeling with limited training data inevitably results in unsatisfactory user recommendations, hindering the growth of the platform. Transfer learning is a solution that both the academic and industrial communities have focused on to address this issue. Introducing pre-trained knowledge into downstream scenarios will greatly alleviate the cold-start problem and help to model user interactions.
Therefore, research on transferable recommender systems has been almost continuous throughout every stage of the development of the recommender systems field. From the era of matrix factorization based on item IDs and user IDs, transferable recommender systems had to achieve transfer learning for ID-based recommender systems based on data overlapping from both source and downstream scenarios. In recent years, there has been rapid development in multimodal understanding technology. Researchers are gradually shifting their focus to modeling user sequences using pure modal information, achieving transferable recommendations even in scenarios where there is no data overlapping between source and downstream scenarios. Currently, ‘one-for-all’ recommender systems that use large language models (LLM) have received a lot of attention. Exploring transferable recommender systems and even foundation models for recommender systems has emerged as the next frontier in the field of recommender systems.
We can agree decoupling is a good practice that simplifies the code and the maintainability of the project.
A common way of decoupling the code is to divide the responsibilities into different layers. A very common division is:
On November 6, 2023, OpenAI announced the release of GPTs. On this no-code platform, as a professional (or hobbyist) developer, you can build customized GPTs or chatbots using your tools and prompts, effectively changing your interactions with OpenAI's GPT. Previous interactions mandated using dynamic prompting to retrieve responses from GPT with LangChain (opens new window)or LlamaIndex (opens window. Now, the OpenAI GPTs handle your dynamic prompting by calling external APIs or tools.
This also changes how we (at MyScale) build RAG systems, from building prompts with server-side contexts to injecting these contexts into the GPTs model.
The realm of Artificial Intelligence (AI) and Machine Learning (ML) is rapidly transforming various industries, from healthcare to finance. In this digital era, the power of AI and ML is harnessed through a myriad of tools and frameworks, each offering unique capabilities and features. The right selection of these tools and frameworks is pivotal for the success of any AI/ML project. This guide serves as a comprehensive resource, aiming to compare and contrast the most prominent AI/ML tools and frameworks available today. By the end of this guide, readers will have a clearer understanding of which tools and frameworks best suit their specific needs in the ever-evolving landscape of AI and ML.
AI/ML tools and frameworks are the building blocks of modern AI and ML applications. These tools provide the necessary infrastructure to develop, train, and deploy AI and ML models efficiently. While AI tools often include software libraries and platforms for designing and implementing AI algorithms, ML frameworks offer a more structured environment for machine learning development, including automated data processing, algorithm selection, and model evaluation. The choice of the right tool or framework can significantly impact the performance, efficiency, and scalability of AI and ML projects. Understanding these tools and frameworks is crucial for developers, researchers, and businesses to navigate the complex world of artificial intelligence and machine learning.
Salesforce is a powerful cloud-based platform that is used by businesses of all sizes to manage their customer relationships, sales, marketing, and other operations. However, with its wide range of features and functionality, Salesforce can be a complex system to manage and optimize.
One of the most important aspects of optimizing Salesforce is performance testing. Performance testing is the process of testing a software application under load to identify any performance bottlenecks or issues. This is important for many businesses as it can have a significant impact on business productivity and profitability.
The landscape of software engineering is rapidly evolving, marked significantly by the emergence and integration of Generative AI. This evolution represents not just a technological advancement but a paradigm shift in the way software development is perceived and approached. This comprehensive article aims to delve deeply into this transformation, highlighting the pivotal role of Generative AI, with a particular focus on Large Language Models (LLMs), in reshaping the future of software engineering.
Modern software engineering is a complex amalgamation of advanced methodologies and cutting-edge tools. Key elements include Code AutoComplete systems, Continuous Integration pipelines, and sophisticated Debugging Tools. These advancements have revolutionized the development process, enhancing efficiency and accuracy. The integration of AI has transitioned from a theoretical concept to a practical, indispensable tool. It streamlines workflows, automates mundane tasks, and fundamentally alters the software development life cycle.
The Java Virtual Machine enables Java applications to be platform-independent while optimizing performance. One crucial component to understand when considering performance, especially memory utilization, is how the Java Collections Framework, specifically the ArrayList, handles size and capacity.
In this article, we will concentrate on the overhead caused by lists that contain two or three elements. The reason for this is that it’s a more common situation, and it can be easily overlooked.
The three issues in A2P messaging that are costing brands significant money are:
The modern digital landscape, while providing unprecedented connectivity and convenience, has also given rise to a myriad of complex security challenges. One such covert menace that has been gaining momentum under the radar is Artificially Inflated Traffic (AIT) fraud, particularly in relation to SMS verification practices.
For the last several days, I’ve been working on one of my pet projects for my portfolio. To be precise, I have been creating an analytical dashboard for an airline company. Finding suitable tools turned out to be a challenge. I wanted to use Next.js in the stack, and my goal was to make sure any user could understand the presented statistical data and, secondly, interactively explore the information. So, in this tutorial, I will cut my way through constructing the dashboard and creating a pivot table and charts in the Next.js app on the example of an airline company. Hopefully, it will save you time :)
Here, I would like to share all the things I did to prepare for creating the essential statistics. And we will start with…