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Google Cloud’s revenue growth driven by AI capabilities, outpaces expectations and positions it amongst AWS and Microsoft Azure in global cloud evolution. By Kitty Wheeler.

The momentum across the company is extraordinary. Our commitment to innovation, as well as our long-term focus and investment in AI, are paying off with consumers and partners benefiting from our AI tools.

Chief Executive Officer of Google, Sundar Pichai

Technology companies vying for dominance across the world is no secret, especially in a market that is increasingly critical to global business operations. The integration of AI capabilities into these cloud services has further accelerated growth and innovation in the sector. In this context, the cloud computing market has been dominated by three main players: Amazon Web Services (AWS), Microsoft Azure and Google Cloud.

Key benefits of Google’s Dialogflow Enterprise Edition include:

* Businesses to build communication tools like chatbots for websites and messaging applications
* Uses a machine learning foundation
* Allows chatbots to better recognize context and intent during conversations with users
* Provides more accurate and natural responses

Alphabet, like its competitors, is additionally investing heavily in AI and cloud infrastructure. The company has announced plans to spend billions on opening data centres worldwide to support its cloud and AI initiatives. Google has also integrated its generative AI chatbot, Gemini, into its cloud services, offering features such as AI-driven code generation, data processing and cybersecurity threat analysis. Good read!

Triton is a language and compiler for parallel programming. Specifically it is currently a Python-based DSL (Domain Specific Language) along with associated tooling, that enables the writing of efficient custom compute kernels used for implementing DNNs (Deep Neural Networks) and LLMs (Large Language Models), especially when executed on AI accelerators such as GP. By Sanjeev Rampal.

Triton is a DSL for writing GPU kernels that aren’t tied to any one vendor. Additionally, it is architected to have multiple layers of compilers that automate the optimization and tuning needed to address the memory vs compute throughput tradeoff noted above without requiring the kernel developer to do so. This is the primary value of Triton. Using this combination of device-independent front-end compilation and device-dependent backend compilation, it is able to generate near-optimal code for multiple hardware targets ranging from existing accelerators to upcoming accelerator families from Intel, Qualcomm, Meta, Microsoft and more without requiring the kernel developer to know details of or design optimization strategies for each hardware architecture separately.

Triton is an important initiative in the move towards democratizing the use and programming of AI accelerators such as GPUs for Deep Neural Networks. In this article we shared some foundational concepts around this project. In future articles, we will dive into additional Triton details and illustrate its use in enterprise AI platforms from Red Hat. Nice one!

Imagine that you’re an engineer at the company Acme Corp and you’ve been tasked with some big projects: integrating and delivering software using CI/CD and automation, as well as implementing data-driven metrics and observability tools. But many of your fellow engineers are struggling because there’s too much cognitive load — think deploying and automating Kubernetes clusters, configuring CI/CD pipelines, and worrying about security. By Darren Evans and Yuriy Babenko.

Platform engineering is a practice that helps companies deliver software and services more efficiently by providing a platform for developers to use. Platform engineers are responsible for building and maintaining this platform, as well as providing support to developers.

The document deep dives into:

• Common attributes of a platform engineer
• The design loop and the significance of customer focus
• What does a platform engineer actually do?
• What platform engineers should avoid?
• Platform engineers are the backbone of modern software delivery

If platforms are first and foremost a product, as the CNCF Platforms White Paper suggests, the focus is on its users. From the Google DORA Research 2023 we know that user focus is key: “Teams that focus on the user have 40% higher organizational performance than teams that don’t.” For example, you might decide to adopt Google’s HEART (Happiness, Engagement, Adoption, Retention, Task Success) framework. Follow the link to the full article to get access to further reading, links and whitepapers. Excellent read!

There are many success stories out there about using Elixir in production that not only prove that the language is mature enough to be a solid choice, but it can be even more effective than the usual frequently used languages and frameworks thanks to the features provided by BEAM and OTP. By RisingStack Engineering.

From startups to established enterprises, our examples clearly outline Elixir’s strengths:

• Scalability – Effortlessly handles sudden surges in traffic and data.
• Fault Tolerance – Maintains stability and uptime even during system failures.
• Cost Efficiency – Reduces infrastructure needs.

The article then also mentions companies using Elixir in production and the reasons behind it:

• Incredible developer productivity with Elixir at Remote.com
• “It just works” – Elixir by accident at Multiverse
• Less servers, same performance at Pinterest
• How two elixir nodes outperformed 20 Ruby nodes by 83x at Veeps.com
• Elixir powers emerging Markets – Literally as SparkMeter.io
• Multiplayer with Elixir: 10000 players in the same session
• How PepsiCo uses Elixir
• 4 Billion messages every day on Discord with Elixir

… and more. Follow the link to article to get details about case studies mentioned above. As seen from these case studies, most companies needed not only scalability but also ease of maintenance and future-proofing – Elixir was able to provide all of these, thus proving its maturity for an environment that is more than ready for use in production. Nice one!

In recent times, JavaScript and signals have gained attention as a powerful new tool for managing reactive states. But how did that come about? By Hrvoje D. In this blog post, we’ll dive into what signals are, explore how this “new” approach to development really works, and compare it to previous state management solutions.

Signals are basic data units that can automatically alert functions or computations when the data they hold changes. So signals can work two ways, they can receive data and they can transmit data down the line. There are plenty of similar examples being used in different frameworks, which all have their little changes in implementation, but in fact are used to get the same thing in the end.

The article mentions examples of similar signal usages in JavaScript:

• React hooks comparison
• Angular RxJS comparison
• Simpler state management
• Signals in the JavaScript ecosystem

The JavaScript Signals proposal is an initiative by TC39 to establish a standard for managing reactive states across JavaScript applications. While JavaScript previously introduced a standard for promises, this proposal differs by focusing on a foundational reactive model that frameworks can adopt, facilitating interoperability across libraries like React, Angular, and Vue. By focusing on features like automatic dependency tracking, lazy evaluation, and memoization, Signals aim to simplify functional reactive programming and enable efficient, glitch-free state updates. Good read!

Kubernetes is inarguably an elegant, refined, well-designed edifice of open source enterprise software. It is known. Even so, the internal machinations of this mighty platform tool are shrouded in mystery. Friendly abstractions, like “resource requests” for CPU and memory, hide from view a host of interrelated processes — precise and polished scheduling algorithms, clever transformations of friendly abstractions into arcane kernel features, a perhaps unsurprising amount of math — all conjoining to produce the working manifestations of a user’s expressed intent. By Reid Vandewiele.

By the time you reach the end of this article, you will learn:

• Big picture view: Layers in the looking glass
  • Pod spec (kube-api)
  • Node status (kubelet)
  • Container configuration for CPU (container runtime)
  • Container configuration for memory (container runtime)
  • Node pressure and eviction (kubelet)

A node becomes “full” and unable to accept additional workloads based on resource requests. The actual CPU or memory used on the node doesn’t matter in deciding whether the node can handle more pods. If you want a node being “full” to mean its actual CPU and memory resources are being used efficiently, you need to make sure CPU and memory requests match up with actual usage. Interesting read!

Absorbing and then summarizing very large quantities of content in just a few seconds truly is a big deal. As an example, a while back I received a link to the recording of an important 90 minute business video conference that I’d missed a few hours before. By David Clinton.

The article explains:

• How to use GPT for business analytics
• How to prepare a dataset
• How to apply LlamaIndex to the problem
• Wrapping up

GPT – and other large language models (LLMs) – are capable of independently parsing, analyzing, and deriving insights from large data sets. But, overall, you can accomplish more and faster than you can probably imagine right now. While all that greatly simplifies the data analytics process, success still depends on understanding the real-world context of your data and coming up with specific and clever prompts. That’ll be your job. Nice read!

Following the announcement at I/O 2024, Google is now rolling out Contextual Smart Replies to Gmail for Android and iOS. Compared to Smart Reply, which dates back to 2017, the contextual version is “meant for detailed responses.” Gemini “can analyze the context of an email” and suggest replies that are “tailored to your conversation.” By Abner Li.

The three Smart Replies appear in a carousel, with the first line noting the high-level direction of each generated option and how they differ from one another. You can long-press on “each response to get a quick preview of the text.” Tapping inserts the suggestion, which includes a greeting and closing, into the body of the email for you to make further edits.

As of launch, only English is supported, while you need to be on one of the following paid tiers:

• Google One AI Premium
• Gemini Business, Enterprise, Education, Education Premium

Contextual Smart Replies are rolling out starting today and will be fully available in the coming weeks. Interesting read!

An approach for creating masonry layouts in vanilla CSS is one of those “holy grail” aspirations. I actually tend to plop masonry and the classic “Holy Grail” layout in the same general era of web design. They’re different types of layouts, of course, but the Holy Grail was a done deal when we got CSS Grid. By Geoff Graham.

That leaves masonry as perhaps the last standing layout from the CSS 3 era that is left without a baked-in solution. I might argue that masonry is no longer en vogue so to speak, but there clearly are use cases for packing items with varying sizes into columns based on available space. And masonry is still very much in the wild.

The article explains:

• What is masonry layout?
• Examples, please?
• There are two competing proposals
• The case for merging masonry and grid
• The case for keeping masonry separate from grid

There are two competing proposals for CSS masonry at the time of writing and many opinions are flying around advocating one or the other. Both proposals have merits and come with potential challenges — it’s a matter of what you prioritize which, in this case, I believe is a choice between leveraging existing CSS layout features and the ergonomics of a fresh new approach. Interesting read!

Explore the job-leveling matrix for Web Development. Understand the roles and skills needed to advance from beginner to leader. By Coursera.

The article does a good job explaining:

• What is a job leveling matrix for web development

• How to use this job leveling matrix for web development

• Example of a web development job leveling matrix

• Typical web development career progress and roles

• Educational requirements for a career in web development

• Recommended web development courses

Web development is a dynamic and creative field that requires a blend of artistic flair and technical prowess to build and maintain websites and web applications. This guide provides a structured pathway for career development in web development, detailing essential roles and skills needed to progress from an entry-level developer to a senior leader. Good read!