Day: January 21, 2024

Over the last 25 years, the global software industry underwent a profound shift. It was the move from per-seat licensed, locally installed software to the software as a service (SaaS) model. It was a change that altered everything about the software market. For one thing, it gave even small businesses access to both industry-standard and cutting-edge software tools. For another, it eliminated burdensome hardware replacement cycles by moving the bulk of business computing to the cloud.

However, here in 2024, it's becoming quite clear that the SaaS market itself is losing momentum. Starting in 2022, investment activity in new SaaS startups plummeted. Since then, the valuations of public SaaS companies have, too. For the average developer, this is a worrisome trend. After all, SaaS has been a massive job creator for programmers. And if it's on its way out, what are all of the displaced developers to do?

JSON is a lightweight, text-based data exchange format that humans and machines can read and write. Over the years, it has become the industry standard for sharing data across multiple applications and systems, especially in web and JavaScript-based applications.

JSON makes it easy to store and exchange data, but it lacks the ability to communicate additional information, such as the shape of the data, which fields are missing, or what similar data is supposed to look like. It also cannot provide additional context to its properties, which leaves room for too many assumptions and can make it difficult to work with. Let’s take a look at the JSON object below:

Cybersecurity has become a paramount concern for individuals and organizations alike. As technology advances, the techniques employed by cybercriminals also grow more sophisticated. Understanding the different types of cyber attacks and implementing robust security measures is crucial in safeguarding sensitive data and systems from malicious intent.

Types of Cybersecurity Threats

In the dynamic landscape of cyberspace, threats to digital security continue to evolve, presenting a multitude of challenges for individuals, businesses, and governments. Understanding the diverse array of cyber threats is pivotal in fortifying defenses and preempting potential vulnerabilities.

Base64 encoding was originally conceived more than 30 years ago (named in 1992). Back then, the Simple Mail Transfer Protocol (SMTP) forced developers to find a way to encode e-mail attachments in ASCII characters so SMTP servers wouldn't interfere with them.

All these years later, Base64 encoding is still widely used for the same purpose: to replace binary data in systems where only ASCII characters are accepted. E-mail file attachments remain the most common example of where we use Base64 encoding, but it’s not the only use case. Whether we’re stashing images or other documents in HTML, CSS, or JavaScript, or including them in JSON objects (e.g., as a payload to certain API endpoints), Base64 simply offers a convenient, accessible solution when our recipient systems say “no” to binary.   

In today’s world, companies often find themselves grappling with unpredictable surges in workloads, especially during pivotal events. Think about retailers gearing up for Black Friday, sports betting companies preparing for specific games, or marketing teams orchestrating major campaigns. These organizations face a common challenge – how much infrastructure do they need to ensure optimal performance without overprovisioning – which can become very costly, very quickly. This poses a significant challenge for businesses since miscalculations can lead to latency, lost customers, and significant financial losses, even as much as hundreds of thousands of dollars per minute.

Remember when the Game of Thrones spinoff had technical difficulties during its premiere? Millions of viewers were left disappointed, and HBO's reputation took a hit. This incident serves as a stark illustration of insufficient infrastructure planning during a critical event. Similarly, numerous instances of customer backlash during major shopping events have highlighted the urgency for effective solutions in managing fluctuating workloads. Even retail giants like Amazon have faced customer dissatisfaction during events like Prime Day when the website couldn't handle the traffic.

Artificial Intelligence (AI) has witnessed remarkable growth and advancement in recent years. Its potential to analyze vast amounts of data, detect patterns, automate tasks, and make accurate predictions has made it an essential tool in numerous industries. One sector that has embraced AI technology and witnessed significant transformation is the mortgage industry. With complexities and challenges associated with mortgage processing, AI has emerged as a game-changer, helping lenders and borrowers alike.

Real-Time Data Analysis

AI facilitates real-time data analysis, enabling lenders to access and interpret real estate market data quickly. By analyzing market trends, property valuations, and demographic data, AI algorithms can help lenders make informed decisions on loan approvals, interest rates, and investment strategies. This real-time analysis empowers lenders with the ability to respond rapidly to changing market dynamics and provide competitive mortgage products to borrowers.

CRUD translates to Create, Read, Update, and Delete and is the axiom around which your data exists. If you've solved CRUD, you've "solved data".

CRUD isn't enough though. Just because you're able to handle data doesn't imply you can create an app - Even though it's a huge part of it. This is why we created workflows and actions based on the declarative programming parts of Hyperlambda.