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New research claims that locally run bots using specially trained image-recognition models can match human-level performance in this style of CAPTCHA, achieving a 100 percent success rate despite being decidedly not human. By Kyle Orland.

Recent research by ETH Zurich PhD student Andreas Plesner and colleagues has shown that AI can now solve Google’s reCAPTCHA v2 with a 100% success rate. Using a fine-tuned YOLO object-recognition model, the AI was trained on 14,000 labeled traffic images and could identify CAPTCHA images with high accuracy. This breakthrough demonstrates that AI can now match human performance in identifying objects in CAPTCHA grids.

To bypass reCAPTCHA’s defenses, the researchers used additional techniques such as VPNs to avoid detection, mouse movement models to mimic human activity, and fake browser and cookie information. These methods helped the AI appear more human-like and successfully solve the CAPTCHAs. The success of this AI model highlights the growing challenge of creating effective CAPTCHAs. As AI continues to improve, traditional CAPTCHA methods may become obsolete, pushing developers to find new ways to distinguish between humans and bots.

Google has already started shifting towards more subtle methods of user verification, such as reCAPTCHA v3, which analyzes user interactions rather than presenting explicit challenges1. This shift aims to enhance security while minimizing user inconvenience.

This research underscores the ongoing battle between AI advancements and security measures, emphasizing the need for continuous innovation in human verification methods. Good read!

In today’s fast-paced and interconnected digital world, the need for robust and efficient communication between different components of a software system is paramount. Messaging queues have emerged as a vital solution to this challenge, providing a reliable way to manage and coordinate the flow of messages between various parts of an application. By Sean Coughlin.

The article also discusses:

• What are messaging queues?
• How messaging queues work
• Synchronous vs. asynchronous messaging
• Types of messaging queues
• Benefits of using messaging queues
• Common messaging queue implementations
• Messaging queues in the design stack
• Best practices for using messaging queues

Popular messaging queue systems like RabbitMQ, Apache Kafka, and Amazon SQS each have their strengths and are suited to different use cases. Best practices for using messaging queues include ensuring message durability, handling message duplication, and monitoring queue performance. By following these practices, developers can design efficient, resilient, and maintainable systems. Nice one!

Discover how AI is transforming enterprises with key applications, real-world examples, and implementation insights across industries. By Hiren Dhaduk.

Now, generative AI promises a greater technological leap for enterprises. This subset of AI can create new content, ideas, or solutions within business environments. It is transforming even knowledge-intensive tasks like enterprise search, taking them from basic automation to more sophisticated, context-aware, and proactive systems.Now, generative AI promises a greater technological leap for enterprises. This subset of AI can create new content, ideas, or solutions within business environments. It is transforming even knowledge-intensive tasks like enterprise search, taking them from basic automation to more sophisticated, context-aware, and proactive systems.

You will also get insights on:

• Key applications of AI in enterprise
  • Streamlining operations
  • Finance and accounting
  • Marketing and sales
  • Customer service and support
  • Human resources
  • IT and security
  • Business strategy and decision making
• Challenges of implementing AI in enterprise

Another major hurdle for implementing generative AI in the enterprise is the shortage of skilled professionals. They may lack the in-house capability to develop, implement, and maintain sophisticated enterprise AI software. Nice one!

Oracle’s leadership was in celebratory mood opening its annual CloudWorld jamboree, and with its announcement of a new cloud partnership with AWS, multicloud futures were high on everyone’s agenda. Oracle top brass led out their annual CloudWorld conference in Las Vegas, Nevada on 10 September with a focus on the multicloud future, but also a customer-centric love-in, as CEO Safra Catz took to the stage to laud the partnerships the firm has built over its near-50 year lifespan to date. By Alex Scroxton.

“Multicloud interconnect … connecting the Oracle Cloud to AWS would make that possible,” he continued. “We can interconnect clouds, and in some cases we have. But [that] is not optimal, it’s not the ideal solution. A much better approach would be if we embedded an Oracle Cloud datacentre right inside AWS.”

Larry Ellison

Later in the day, Larry Ellison, who turned 80 earlier this year, expanded on the theme of turning longstanding rivals into best multicloud buddies. He lamented that the move to cloud at first caused the industry to lose sight of the idea that systems could be interoperable, but this, he continued, is now rightly changing – as Oracle’s newly minted partnership with AWS shows, he claimed. The growth of true multicloud propositions heralds a step change in enterprise computing, bringing convenience and choice for Oracle customers with connections into the major hyperscalers, and others as well. Good read!

An insightful webinar as we explore strategies for optimizing AWS Lambda performance. In this session, Yan Cui, AWS Serverless Hero, and Aviad Mor, Co-founder and CTO of Lumigo, will discuss three approaches that can significantly enhance the efficiency and scalability of your serverless applications. By Aviad Mor and Yan Cui.

Key Topics:

• Function Configuration: Learn how to fine-tune AWS Lambda settings for optimal performance, including memory allocation, timeout configurations, and concurrency settings.
• Code Optimization Techniques: Explore effective coding practices and performance optimizations to boost the efficiency of your Lambda functions, including minimizing dependencies, leveraging caching mechanisms, and optimizing cold start times.
• Monitoring and Debugging: Gain insights into monitoring techniques to identify performance bottlenecks, troubleshoot issues, and ensure the smooth operation of your serverless applications on AWS Lambda.

Discover how to streamline your AWS Lambda functions to achieve better performance, reduce latency, and minimize costs in this free video seminar. Nice one!

The Memorystore team is thrilled to announce the preview launch of Valkey 7.2 support on Memorystore to provide users with a truly open-source and high performance key-value service. The introduction of Memorystore for Valkey into the Memorystore portfolio alongside Memorystore for Redis Cluster and Memorystore for Redis is a direct response to customer demand, and is a game-changer for organizations seeking high-performance data management solutions relying on 100% open source software. By Kyle Meggs, Ping Xie.

Valkey is a fully open source successor built by long standing Redis contributors and maintainers. Fostering open collaboration that benefits all and not just a single organization is critical in building long term, sustainable open source communities

Valkey has rapidly become an open-source key-value datastore chosen for its exceptional performance, scalability, and adaptability. Valkey 7.2 is fully compatible with Redis 7.2 and the most popular Redis clients such as Jedis, redis-py, node-redis, and go-redis, offering Google Cloud customers a truly open source option through the Linux Foundation.

Launching Memorystore for Valkey 7.2 expands Memorystore’s portfolio of offerings beyond Redis Cluster, Redis, and Memcached. And we’re even more excited for the game-changing features coming in Valkey 8.0. In the first release candidate for Valkey 8.0, the community launched major enhancements across five key dimensions of performance, reliability, replication, observability, and efficiency. Nice one!

Serverless, or serverless computing, is an approach to software development that empowers developers to build and run application code without having to worry about maintenance tasks like installing software updates, security, monitoring and more. With the rise of cloud computing, serverless has become a popular tool for organizations looking to give developers more time to write and deploy code. By Mesh Flinders.

The article then explains:

• How does serverless work?
• Enterprise benefits of serverless
• Enterprise use cases for serverless
  • Artificial intelligence (AI) and machine learning (ML)
  • Microservices
  • Hybrid cloud
  • Big data analytics
  • Internet of Things (IoT)
  • API gateways
  • Chatbots
  • Embarrassingly parallel tasks

As organizations of all sizes, and across a wide range of industries, seek to leverage cloud computing to help achieve their digital transformation goals, serverless computing is playing a pivotal role. By freeing developers from mundane tasks like installing and updating OS, provisioning servers and scaling infrastructure, serverless environments help developers to focus on business logic and writing and deploying code. Good read!

After an initial euphoric rush to the cloud, administrators are questioning the value and promise of the tech giants’ services. According to a report published by UK cloud outfit Civo, more than a third of organizations surveyed reckoned that their move to the cloud had failed to live up to promises of cost-effectiveness. Over half reported a rise in their cloud bill. By Richard Speed.

In the IT world, there is an expectation that bang for buck increases as time goes by, but in this example, prices are rising faster than the rate of inflation, and what customers receive for their money remains unchanged. Cost control, based on operating datacenters at massive scale, was part of the early sales pitch and in the intervening 15 years, it had proven out – cloud product costs were stable, and either went down in price or more features were added at the same price.

So, are prices increasing? The answer has to be yes. How much of those rises are down to the major vendors opportunistically adding of a few percentage points versus an increase in fixed costs, such as electricity, is pretty much irrelevant. The advice remains the same: the cloud is here to stay although its luster has dulled over time. Interesting read!

Will designing tools and workflows to bring self-service to software development help developers work more efficiently? A growing number of adopters think so. By John Edwards.

Platform engineering focuses on tasks designed to help application developers work more efficiently, such as setting up staging environments, readying CI/CD pipelines, and configuring infrastructure as code (IaC) to automate cloud resource provisioning.

With a renewed focus on cost efficiency, organizations have come to realize that significant value can be derived from improved developer productivity and engineering effectiveness, especially in making R&D dollars go further. Platform engineering’s primary benefit is giving developers an experience that features the same ease-of-use intended for business users and consumers, ultimately making them more productive.

Since platform engineering draws from DevOps and CI/CD practices, a good way to begin is to become adept in managing DevOps tools, frameworks and technologies. As platform engineering tends to align with cloud computing, microservices, and orchestration, organizations should focus on building a portfolio focused on automation in AWS, Azure, or Google Cloud Platform. Good read!

The ability to process high volumes of data (big data) in real-time has become crucial for many organizations, and this is where data streaming technologies come into the picture. These technologies allow large amounts of data to be processed in real-time or near real-time as it is generated, enabling businesses to gain immediate insights and make time-sensitive data-driven decisions. By Darryn Campbell.

At the heart of these technologies is the concept of data streams, also known as event streams. Data streams are sequences produced by various sources, such as social media feeds, Internet of Things (IoT) devices, log files, scientific data sets, and more. These streams of data are then ingested and processed by data streaming technologies. The blog post then explains:

• Basic concepts of data streaming technologies
• Data streaming architecture
• Data consistency
• Tools for data streaming technologies

Data consistency is a significant concern in data streaming. Data streaming technologies use various techniques such as event ordering, exactly-once processing, and fault tolerance to ensure consistency. These techniques ensure that the data is processed in the correct order, no data is lost or processed multiple times, and the system can recover from failures without data loss. Nice one!