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Implementing an event-driven architecture (EDA) is a road riddled with challenges. Among them is choosing the right tooling for the job. Many event-driven tools seem quite similar, at least at first glance, and you’d expect they could be used equally well for the same purposes. But that’s often not the case and choosing the solution best suited to your needs can be tricky. By Tun Shwe.

Article focuses specifically on message queueing and event streaming, highlighting their differences, common denominators and suitability for various use cases:

• Understanding messages queues and event streams
• Message queueing vs. event streaming tech: Comparing capabilities
• Message queueing and event streaming use cases
• Message queueing is sometimes a stepping stone to event streaming

If you’re dealing with small and medium workloads, you’re looking to reliably and flexibly route messages between components and your system is primarily interested in the current state, a message queueing technology is an adequate choice.

On the other hand, if you want to handle high-volume, high-frequency event streams in a scalable and reliable way, you need to do complex processing on data as it arrives to gain actionable real-time insights and your system is concerned not only with the current state, but a historical record of state changes, then event streaming is the right way to go. Nice one!

Intel’s momentus “AI Everywhere” campaign to show it is a strong competitor in the AI chip arena moved into high gear Thursday. CEO Pat Gelsinger and top executives argued the company can help customers with AI training and inference jobs across huge data centers as well as aboard AI PCs and even on smaller mobile devices. By Matt Hamblen.

The document says that Intel is making new chips for data centers and mobile devices to handle the increasing demand for AI workloads. The company is launching Core Ultra and 5th Gen Xeon processors. Intel is also working on software tools to make it easier for developers to use AI. Some analysts believe that Intel’s AI Everywhere strategy could be successful, but it will take time for the market to mature.

While most of the application merits of AI PCs from various chipmakers are yet to be realized, Intel also briefed reporters on side-by-side performance attributes of its Fifth Generation Xeon Scalable server processors, code-named Emerald Rapids, and compared them head-to-head with what AMD offers, while also asserting the qualitative value of its CPUs next to wildly successful GPUs from Nvidia. Good read!

Transitioning to the cloud means more than just migrating your workloads or building innovative new apps. While it’s a great first step for your organization, it’s just the beginning. To truly maximize the value of your cloud investments requires a fundamental shift in mindset and culture to rethink how your organization operates when imbued with technology. By Eric Lam and Pathik Sharma.

There are five core building blocks of Cloud FinOps:

• Accountability and enablement: Sets up a cross-functional team, standards, and enablement for managing cloud spend at scale
• Measurement and realization: Promotes cost transparency, using KPIs and value metrics to drive success
• Cost optimization: Drives continuous cost optimization effort focusing on resources, pricing, and architecture on cloud
• Planning and forecasting: Modernizes budgeting, allocation, forecasting, and chargeback methods to allow for cost effective development practices
• Tools and accelerators: Enables data-driven business decisions with near real-time cost reporting, automation, and tool integration

Cloud FinOps is an operational framework and cultural shift that brings technology, finance, and business together to drive financial accountability and accelerate business value realization through cloud transformation. Said differently, it’s a culture shift that promotes cost consciousness and agility in the cloud. Contrary to popular belief, Cloud FinOps isn’t just a practice in saving money. Rather, it’s about getting the most value out of the cloud to drive efficient growth. referred to by the FinOps Foundation. Good read!

Arduino is a popular open-source electronics platform that has revolutionised the world of DIY projects. With its powerful microcontrollers and versatile programming environment, Arduino allows enthusiasts and professionals alike to bring their creative ideas to life. One of the key features that make Arduino even more powerful is the availability of shields. By Jack Portley.

Further in the article:

• Introduction to Arduino shields
• Why do we need shields?
• The types of Arduino shields
  • Ethernet shield
  • Proto shield
  • Relay shield
  • Motor shield
  • LCD shield
  • Bluetooth shield
  • Xbee shield
  • Capacitive Touchpad shield
• How to use Arduino shields
• Choosing the right Arduino shield for your project

Shields allow you to add new features and capabilities to your projects without the need for complex wiring or additional components. They provide an easy and convenient way to enhance the functionality of your Arduino board. Good read!

The crypto space is vast, and it can be easy to get lost in a sea of terms and definitions. Consider this a brief introduction into this ever-changing universe. By @pwc.com.

Digital assets like cryptocurrencies, NFTs and other tokens are past “emerging” — they’re here to stay.

Blockchains are the technology solutions that enable digital assets. A blockchain is a method of securely recording information on a peer-to-peer network. It’s a shared public database, duplicated across computer systems, in which new entries can be added but existing entries can’t be altered.

The article also deals with:

• Digital asset types
  • Crypto assets
  • Stablecoins
  • Non-fungible tokens (NFTs)
  • Central bank digital currencies (CBDCs)
  • Security tokens
• Digital asset storage
• The world of digital assets
• Access layer
• Layer 1
• Layer 2
• Functional layer

… and much more. Financial opportunities being built into the options on purchase of a digital asset. Being able to get a loan, insurance or other financial instrument automatically agreed to by a provider via the blockchain. Nice one!

Solar cells that combine traditional silicon with cutting-edge perovskites could push the efficiency of solar panels to new heights. By Emma Foehringer Merchant.

In November 2023, a buzzy solar technology broke yet another world record for efficiency. The previous record had existed for only about five months—and it likely won’t be long before it too is obsolete. This astonishing acceleration in efficiency gains comes from a special breed of next-­generation solar technology: perovskite tandem solar cells. These cells layer the traditional silicon with materials that share a unique crystal structure.

In the decade that scientists have been toying with perovskite solar technology, it has continued to best its own efficiency records, which measure how much of the sunlight that hits the cell is converted into electricity. Perovskites absorb different wavelengths of light from those absorbed by silicon cells, which account for 95% of the solar market today. When silicon and perovskites work together in tandem solar cells, they can utilize more of the solar spectrum, producing more electricity per cell.

Technical efficiency levels for silicon-­based cells top out below 30%, while perovskite-only cells have reached experimental efficiencies of around 26%. But perovskite tandem cells have already exceeded 33% efficiency in the lab. That is the technology’s tantalizing promise: if deployed on a significant scale, perovskite tandem cells could produce more electricity than the legacy solar cells at a lower cost.

However the electrochemical makeup of perovskites means they’re sensitive to sucking up water and degrading in heat, though researchers have been working to create better barriers around panels and shifting to more stable perovskite compounds. Super interesting!

It’s no secret that Android processors, whether Qualcomm or MediaTek, have consistently lagged behind Apple in terms of raw performance. However, a recent report from Moor Insights and Strategy suggests that after decades of trailing, ARM could narrow the gap and potentially surpass Apple’s offerings with the upcoming Cortex X5 CPU core. By Hisan Kidwai.

Codenamed Blackhawk, ARM’s new core design reportedly delivers the “largest year-over-year IPC (instructions per cycle/clock) performance increase in 5 years,” making it one of the most significant upgrades since the introduction of the Cortex-X series of processors in 2020. Additionally, aligning with industry trends, the core also boasts impressive Large Language Model (LLM) performance and generative AI capabilities.

Apple uses ARM’s technology to design its own chips, retaining exclusive rights to these designs. This distinction has led to a substantial performance gap, enabling iPhones to run full-desktop-level games while their Android counterparts often struggle even with regular mobile games.

Although specific timelines are still unclear, the technology could debut in phones, hitting the market towards the end of this year. Regarding specific processors, both the upcoming MediaTek Dimensity 9400 and Samsung Exynos 2500 are slated to integrate the new Cortex-X CPU core, bringing them closer in performance to Apple’s A18 Pro and Bionic SoCs. Good read!

ChatGPT is an amazing tool – millions of people are using it to do everything from writing essays and researching holidays to preparing workout programs and even creating apps. The potential of generative AI feels endless. By Niamh O’Connor.

But when it comes to using generative AI for customer service, which means sharing your customers’ data, queries, and conversations, how much can you really trust AI? Generative AI chatbots are powered by large language models (LLMs) trained on a vast number of data sets pulled from the internet. While the possibilities that come from access to that much data are groundbreaking, it throws up a range of concerns around regulation, transparency, and privacy.

The General Data Protection Regulation (GDPR) is one of the most stringent regulatory forces covering personal data in the world. Now that generative AI has changed the game, where does it sit within the GDPR framework? According to a study on the impact of GDPR on AI carried out by the European Parliamentary Service, there is a certain tension between GDPR and tools like ChatGPT, which process massive quantities of data for purposes not explicitly explained to the people who originally provided that data. More info in the article!

CI is a software development practice where engineers merge their code into a central repository multiple times a day. After a pull request is open and after a merge is done, automated builds and tests are triggered automatically to ensure the functionality of the code. This approach promotes a collaborative environment and also helps in identifying issues early on, minimizing the risk of having issues propagated through different environments. By Flavius Dinu, Sumeet Ninawe.

CD is the natural extension of CI, focusing on automating the deployment of the software updates that are done to designated environments after the CI builds and tests finish successfully. By minimizing manual interventions and automating the release process, CD helps organizations achieve quicker feedback loops and ensures the transition between environments and releases goes smoothly.

The article then briefly describes:

• What are CI/CD tools?
• Azure DevOps
• GitHub Actions
• Spacelift
• Jenkins
• Buddy
• TeamCity
• CircleCI
• AWS CodePipeline

… and more. In this post, authors have reviewed the most popular CI/CD tools on the market and some best practices when it comes to choosing the right CI/CD tool for your needs. There are many tools in the DevOps ecosystem, and the ecosystem is constantly growing and improving, so choosing the right CI/CD tool can be hard if you don’t evaluate it specifically for your use case. Good read!

Burgeoning machine learning and AI emerging use cases promise to create significant value for industries via accelerated information processing and increased accuracy of decision-making. But machine learning models are compute-intensive, demand high-frequency, and real-time AI analysis scenarios, which has led enterprises to lean on performance guidance using the metric Trillions of Operations Per Second (TOPS). TOPS captures “how many mathematical operations can an accelerator deliver in one second?” to compare and identify the best accelerator for a given inference task. By Rehan Hameed.

The article pays close attention to:

• Benchmarking with TOPS AI
• TOPS AI as a performance measure
• Higher TOPS does not equal higher performance
• Computational types
• Inference latency

For businesses making investments in Edge AI, calculating performance through benchmarking offers a reliable way to account for computational hardware structures versus TOPS. With most real-world applications requiring a blazing fast inference time, the best way to measure performance is to run a specific workload, typically ResNet-50, EfficientDet, a Transformer or a custom model to understand an accelerators efficiency. This is an older but still relevant article from 2022. Good read!