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Many AI systems are designed for collaboration: Copilot is one of them. Copilot—powered by Microsoft Azure OpenAI Service—allows you to simplify how you design, operate, optimize, and troubleshoot apps and infrastructure from cloud to edge. It utilizes language models, the Azure control plane, and insights about your Azure and Arc-enabled assets. All of this is carried out within the framework of Azure’s steadfast commitment to safeguarding data security and privacy. By Andy Beatman Senior Product Marketing Manager, Azure AI.

The article then captures:

• A brief history of AI collaboration with copilots
• AI at the service of others
• Prioritizing human agency
• Microsoft commitment to responsible AI

With Responsible AI tools in Azure, Microsoft is empowering organizations to build the next generation of AI apps safely and responsibly. Microsoft has announced the general availability of Azure AI Content Safety, a state-of-the art AI system that helps organizations keep AI-generated content safe and create better online experiences for everyone. Nice one!

Amazon CloudWatch lets customers collect monitoring and operational data in the form of logs, metrics, and events, providing an easy way to monitor and receive notifications regarding their workload health and often integrate directly with other systems, such as JIRA Service Desk and ServiceNow. By Chaitanya Gummadi.

The CloudWatch alarms feature lets you monitor CloudWatch metrics and receive notifications when the metrics fall outside a certain threshold. This threshold can be a predefined static threshold, an anomaly detection model, metric math expression, and composite alarm. When you create an alarm, you can specify what actions an alarm takes when it changes the state between OK, ALARM, and INSUFFICIENT_DATA states. The most common type of alarm action is to notify one or more people by sending a message to an Amazon Simple Notification Service (Amazon SNS) topic.

Further in the article:

• Solution overview
• Prerequisites
• Creating CloudWatch Alarm (Optional)

As an example, authors provided an overview of how to create CloudWatch Alarm in Sender Accounts(A&B) and centralize it in the Receiver Account(C) by using CloudFormation to deploy AWS EventBridge Event Buses, Rules and Targets. Good read!

Modern serverless applications on AWS are complex with a lot of moving parts. Mapping a developer workflow onto those applications can be difficult. This article discusses the developer workflow I have developed for complex serverless applications at aleph0, with example CloudFormation template and GitHub Action snippets to illustrate the concepts. By Andy Boothe.

The article then focuses on certain goals for developer workflow and how to architect with this goals in mind:

• An efficient developer workflow compatible with team growth and modern practices, e.g., CI/CD, Infrastructure as Code, etc.
• Separate environments for staging, production, etc. are a requirement
• Example architecture
• Tools and techniques
  • Continuous integration and continuous delivery
  • Infrastructure as code
  • Developer workflow
  • Lambda function workflow
  • Step function workflow
  • REST API
  • Promoting to Production

… and more. You will also find example architecture in the article. The architecture uses an API Gateway REST API to define endpoints that use StartSyncExecution to invoke an Express Step Function, which in turn invokes Lambda function(s) and/or other AWS services. There are also examples for both Github actions CD workflow and CloudFormation template for respective workflows. Good read!

In this article authors have compiled a full list of Google algorithm launches, updates, and refreshes that have rolled out over the years, as well as links to resources for SEO professionals who want to understand each of these changes. By @searchenginejournal.com.

Google’s algorithms are a complex system used to retrieve data from its search index and instantly deliver the best possible results for a query. The search engine uses a combination of algorithms and numerous ranking factors to deliver webpages ranked by relevance on its search engine results pages (SERPs).

In its early years, Google only made a handful of updates to its algorithms. Now, Google makes thousands of changes every year. Most of these updates are so slight that they go completely unnoticed. However, on occasion, the search engine rolls out major algorithmic updates that significantly impact the SERPs. The article then lists all the main updates together with link to detailed description of the changes. The list is searchable by year. Nice one!

Swift’s deliberate design choices over the years has resulted in a language that showcases how flexibility and compatibility does not need to come at the cost of usability. One of these design choices was Swift’s focus on native interoperability with other languages. The flexibility that this enables makes it a joy to build rich, native experiences in Swift across a variety of environments. By Saleem Abdulrasool.

Swift embeds a copy of clang, the C and C++ compiler, which is able to directly translate between the languages avoiding penalties in code size and runtime performance. This level of interoperability composes wonderfully with existing systems and enables building complex software atop existing C libraries. You will also learn about:

• The Windows API
• Component Object Model (COM)
• COM support in Swift with C Interop
• COM support in Swift with C++ Interop
• Improving Swift language support for COM
• Interoperability on Windows

Swift’s arsenal of interoperability tools makes it a potent language for building rich, native applications and libraries on existing platforms, and provides a great alternative to C and C++ with its improved memory safety and ergonomics. On Windows particularly, the interoperability features allow us to gain access to a very large set of the system’s API. Best of all, since COM is used outside of the Windows ecosystem, improvements to Swift’s integration with Windows system APIs, such as the native COM bridging described above, would also help other platforms! Nice one!

Until recently, the applications of artificial intelligence have remained in the hands of scientists, engineers and big business. But for the first time in history, anyone with an internet connection and keyboard can harness this technology. AI-powered tools – like OpenAI’s ChatGPT – have well and truly captured the public’s attention. By Alex von Schirmeister.

In 2024 and beyond,four factors will underpin the development of AI and its impact on small businesses:

• AI costs will need to be managed
• Consolidation could impact accessibility
• Control will lead to questions on accountability
• Copyright, creativity and commercially safe AI

The likes of Microsoft and Google are heavily subsidising the generative tools we play around with, without them making any money from AI – yet. On top of this, 2023 set a record for investment in generative AI startups, with equity funding exceeding $14 billion across 86 deals as of the second quarter of the year, much of which startups are spending on building the infrastructure needed to build, train and run their AI models. AI must now become a self-funding endeavour, as its economics are currently higher than the cost of labour. Good read!

HGX H200 systems and cloud instances coming soon from world’s top server manufacturers and cloud service providers. NVIDiA supercharged the world’s leading AI computing platform with the introduction of the NVIDIA HGX™ H200. Based on NVIDIA Hopper™ architecture, the platform features the NVIDIA H200 Tensor Core GPU with advanced memory to handle massive amounts of data for generative AI and high performance computing workloads. By @nvidia.

The NVIDIA H200 is the first GPU to offer HBM3e — faster, larger memory to fuel the acceleration of generative AI and large language models, while advancing scientific computing for HPC workloads. With HBM3e, the NVIDIA H200 delivers 141GB of memory at 4.8 terabytes per second, nearly double the capacity and 2.4x more bandwidth compared with its predecessor, the NVIDIA A100.

The introduction of H200 will lead to further performance leaps, including nearly doubling inference speed on Llama 2, a 70 billion-parameter LLM, compared to the H100. Additional performance leadership and improvements with H200 are expected with future software updates. Amazon Web Services, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure will be among the first cloud service providers to deploy H200-based instances starting next year, in addition to CoreWeave, Lambda and Vultr. Interesting read!

The rapid proliferation and increased volume of data across industries has magnified the need for organizations to have a solid strategy in place for processing and managing real-time data. Improving overall data capabilities enables teams to operate more efficiently, and emerging technologies have even created a smoother pathway for bringing real-time data closer to business users, which plays a critical role in effective decision-making. By Miguel Garcia.

In this article you will learn:

• Beyond the architecture
  • Data challenges
  • Performance
  • Security and compliance
• Architecture patterns
  • Stream-to-stream
  • Batch-to-stream
  • Stream-to-batch
  • Lambda architecture
  • Kappa architecture
  • Streaming architecture
• Reference Architecture: Streaming architecture with Apache Kafka and Apache Druid

… and more. There are several key data architecture patterns — each one with its strengths, limitations, and use cases. The architecture we choose plays a pivotal role in determining performance, adaptability, and business impacts. However, the knowledge of these architectures alone won’t guarantee success because there are other factors to consider, such as adequate training, team size, cost, or even organizational culture, that may affect decision-making. Good read!

If you are struggling to come up with your app idea, it’s okay. You’re not alone. It is common for app developers to face this type of challenge given the millions of apps in the App Store. Also, thinking of an app idea isn’t always as easy as it sounds. By Pat Enriquez.

Most ideas are born from putting together old and existing ideas. So don’t be discouraged if you find an app in the App Store with the same idea as yours and don’t just scrap that app idea.

The article then describes these strategies:

• The remix strategy
• Scratch your own itch
• Improve what already works

When it comes to app development, oftentimes, apps are made by thinking about how they can solve the problems of app developers themselves. Interesting read!

Spring Supports GraphQL requests over HTTP, Websockets and RSockets. Securing an Spring GraphQL application does not differ from securing a Web application. Mainly, Spring GraphQL needs to ensure context propagates from WebFlux to the data fetching layer so that we can use Security annotations or access the authenticated principal in @SchemaMapping methods. This should work for HTTP and WebSocket. By Ruchira Madhushan Rajapaksha.

In this tutorial, we will develop a Reactive Spring boot application that secures an exposed GraphQL API using JWT authentication. We will be having a separate rest endpoint to generate the JWT, which we will use to validate the GraphQL endpoint. For more information about JWT, just refer to the following guide.

Further in this article you will find information on:

• Application Setup with SpringBoot
• JWT Utility
• Spring Security Configuration
• SecurityContextRepository
• AuthenticationManager

To apply more fine-grained security, we can add Spring Security annotations such as @PreAuthorize or @Secured to service methods involved in fetching specific parts of the GraphQL response. This should work due to Context Propagation that aims to make security, and other contexts, available at the data fetching level. Interesting read!