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A significant 80% of organizations now release code on a daily or weekly basis, indicating a growing reliance on these frequent updates. Traditional testing methods are struggling to keep up with the fast-paced nature of modern Agile practices as they bring several disadvantages, including slowness, high costs, and a lack of adaptability. By KMS Solutions.

The article the explains:

• Challenge 1: Legacy systems
• Challenge 2: Test data management
• Challenge 3: Ineffective CI/CD integration
• Challenge 4: Selecting the right automation testing tool

To overcome these challenges, it’s important to have a well-defined automation strategy, invest in appropriate tools and technologies, collaborate with cross-functional teams, and continuously monitor and adapt your automation framework to the evolving needs of the banking application and the regulatory environment. Good one!

Gemini got its first mention at I/O in 2023, when we briefly shared our first natively multimodal model was in training and already showing impressive capabilities. One year later, we’ve brought Gemini models to Search, Ads, Workspace, Pixel and more. As Gemini continues to make a name for itself, we wondered, where did that name come from? To find out, we asked the Google DeepMind team to share the origin story behind why they called it “Gemini.” By Ari Marini.

Early on, a placeholder title for the project was “Titan,” the name of Saturn’s largest moon. “I wasn’t a huge fan of that name,” says Jeff Dean, Gemini’s co-technical lead. But it gave him an idea — or perhaps a sign — for a name grounded in space. Developed by DeepMind and Google Brain teams, Gemini aims to advance AI research.

Gemini is Latin for “twins.” In astronomy, it’s the name of a constellation associated with Greek mythological twins Castor and Pollux, for which its two brightest stars are named. Naturally, then, the meaning behind our AI model’s name is two-fold. For one, a key characteristic of the Gemini zodiac sign is a dual-natured personality, capable of adapting quickly, connecting to a wide range of people, and seeing things from multiple perspectives — themes well suited for what was happening at Google at the time.

“The Gemini effort came about because we wanted to bring our teams working on language modeling closer together,” Jeff says. “I felt the twins aspect of the name ‘Gemini’ was a great fit. The twins here are the folks in the legacy Brain team and the legacy DeepMind team, who started to work together on this ambitious multimodal model project.” The other inspiration for the name is also space-related: NASA’s early moonshot program, Project Gemini, which lasted from 1965 to 1968. Interesting read!

The Fintech Open Source Foundation onboarded seven new members, including hyperscaler AWS, market infrastructure provider DTCC and chipmaker Intel, the organization announced Wednesday. By Matt Ashare.

“Financial services value streams are rapidly evolving and driving greater specialization in capabilities,” David Tomljenovic, principal research director at Info-Tech Research Group, told CIO Dive. “As a result, there is a movement towards composable systems and a movement away from integrated monolithic systems.”

AWS joined its hyperscale competitors Microsoft and Google Cloud in the FINOS alliance. The nonprofit organization has membership support from numerous financial services companies, including Capital One, Citi, Goldman Sachs, JPMorgan Chase and Morgan Stanley, as well as from technology providers Red Hat, Databricks and CloudBees.

Open source software needs buy-in across providers and businesses to deliver on its promise of reducing cost, shoring up security and limiting vendor lock-in. Adding the largest hyperscaler to its ranks brings the growing FINOS alliance one step closer to achieving that goal. Interetsing read!

A podcast episode featuring Tony Clark, CEO of NextWave Consulting, discussing the significance of digitalisation in fintech. By Maya Kokerov. In the podcast’s 20th episode, NextWave Consulting Founder and CEO Tony Clark is interviewed by Reeve Boyd, an associate in CMS’s Finance Disputes team. Tony and Reeve delve into the significance of digitalisation in driving strategic competitive advantage for fintech companies. They also provide a detailed guide on the steps to take to succeed in a constantly evolving digital landscape.

Here are the key points:

• Digitalisation for Competitive Advantage: Tony Clark emphasizes the importance of digitalisation in driving strategic competitive advantage for fintech companies
• NextWave’s Role: NextWave helps financial institutions modernise through technology, focusing on automation, AI, and data
• SPEED Framework: The framework includes Strategy, Platforms, Ecosystems, Engagement, and Data, guiding firms in their digital transformation
• Challenges and Opportunities: The discussion covers the opportunities and risks of digitalisation, including regulation, operational resilience, and ethical AI

For financial institutions, the rule book is evolving and it’s crucial to play by the new digital rules while keeping a close eye on regulation. Nice one!

A comprehensive guide covering nine types of lengths that CSS uses to size elements in terms of dimensions, space, time, and even sound. By Geoff Graham.

Many CSS properties accept numbers as values. Sometimes those are whole numbers. Sometimes they’re decimals and fractions. Other times, they’re percentages. Whatever they are, the unit that follows a number determines the number’s computed length. And by “length” we mean any sort of distance that can be described as a number, such as the physical dimensions of an element, a measure of time, geometric angles… all kinds of things!

The article then explains:

• Types of numbers
• Absolute units
• Relative units
• What about unit-less numbers?
• We can create our own custom units!
• When to use one unit over another
• Use container units for responsive design, where possible

In some cases, we may want to work with a numeric value, but CSS doesn’t exactly recognize it as one. In these cases, the number is recognized as a “string” value instead, regardless of whether or not it contains alphabetical characters. That’s where we can use @property to create what’s called a “custom property” that evaluates a numeric value in a certain way.

Container queries in general are so gosh-darn great for responsive layouts because they look at the size of the container and let us apply styles to its descendants when the container is a certain size. Super interesting read!

Did you miss a critical bug? Spent hours troubleshooting a failed deployment? DevOps professionals have been there more often than they’d like.AI in DevOps offers a powerful solution. It’s not a magic bullet, but by integrating machine learning and intelligent automation, you can streamline your workflow, identify potential issues early on, and make data-driven decisions. By @clickup.com.

This article explores how AI can transform your DevOps practices, making software building and management more efficient and reliable, including:

• Understanding AI in DevOps
• Benefits of using AI in DevOps
• How to use AI for DevOps in CI/CD
  • Intelligent code analysis
  • Predictive testing
  • Self-healing deployments
  • Proactive monitoring and alerting
  • Resource optimization and cost management
  • Enhanced collaboration and communication
• Using AI software for DevOps
  • Build a live knowledge base: Information at your fingertips
  • Automate documentation: Ditch the tedious tasks
  • Level up communication: Write with clarity
  • Personalized communication: Reports tailored for each audience
• Managing DevOps Teams with innovative tools

… and much more. DevOps teams, by their very nature, require a unique approach to project management that blends traditional project management principles with the collaborative and fast-paced world of DevOps. Nice one!

Major Chinese tech companies last week announced extensive price cuts for their large language model (LLM) products used for generative artificial intelligence, a move that experts say is tipped to speed up the application of AI models in the domestic market and in research. While price wars are not uncommon in the world of LLMs, this comes as Chinese developers continue to make progress in the commercialisation of AI technology in recent months. By Amber Wang.

Lin Yonghua, deputy director and chief engineer of the Beijing Academy of Artificial Intelligence (BAAI), a leading non-profit in AI research and development, said the “bottleneck” around training data is looming large.

The article then explains:

• Need for localised models
• Filling the data gap
• The GPU bottleneck

Industry discussions also focus on the strong demand for computing power. Lack of computing power inhibits the implementation of LLMs and delays product deployment, they say. Good read!

QCOM’s entry into the AI chips market seems promising, with MSFT endorsing the ARM-based Snapdragon X Elite CPU chips as the “fastest, most AI-ready PC ever built.” By Juxtaposed Ideas.

MSFT has recently launched new AI-powered personal computers with QCOM, namely Copilot+ PCs, powered by the latter’s latest ARM-based Snapdragon X Elite CPU chips. With OpenAI’s multimodal GPT-4o model embedded on these PCs while supposedly boasting faster processing speeds and more efficient batteries than AAPL’s most advanced MacBook Air with the M3 processor, it is unsurprising that MSFT has touted these as the “fastest, most AI-ready PC ever built.”

For context, the Neural Processing Unit [NPU] is touted to be the key in enhancing AI performance in a PC, given that it simulates the “human brain’s neural network” in processing “large amounts of data in parallel, performing trillions of operations per second.” Interesting read!

Shift left and shift right are core testing concepts of the agile DevOps methodology, which speeds up application development by releasing small builds frequently as code evolves. As part of the continuous cycle of progressive delivery, DevOps teams are also adopting shift left and shift right principles to ensure software quality in these dynamic environments. By Saif Gunja.

The article then explains:

• In DevOps, what is shift left? And what is shift right?
• Why is shift left important?
• What does shift right mean in DevOps?
• Why shift right and shift left are critical for microservices architecture
• Types of shift right tests
• Types of shift left tests
• The application security dividend of shift right and shift left

… and more. Shift right is the practice of performing testing, quality, and performance evaluation in production under real-world conditions. Shift right methods ensure that applications running in production can withstand real user load while ensuring the same high levels of quality. Nice one!

Do I have “big data”? Oddly, this is not actually a straightforward question for two reasons. By @practicaldatascience.org.

As a result, the fact that you have 16GB of RAM doesn’t mean you can easily work with a 14GB file. As a general rule, you need at least two times as much memory as your file takes up when first loaded.

If you have big data, you basically have four options:

• Use chunking to trim and thin out your data so it does fit in memory (this works if the data you were given is huge, but the final analysis dataset you want to work with is small)
• Buy more memory. Seriously, consider it
• Minimize the penalties of working off your hard drive (not usually practical for data science)
• Break your job into pieces and distribute over multiple machines

If your program starts using more space than you have main memory, your operating system will usually just start using your hard drive for extra space without telling you (this is called “virtual memory”, and is nice in that it prevents your computer from crashing, though it will slow stuff down a lot). As a result, you won’t always get an error message if you try and load a file to is much bigger than main memory. More details on how do I check to see if my data is fitting in memory in the article. Interesting read!