Mendix has been working on adding generative AI features to its low-code platform for months now and expects to release them to enterprises by the end of the year, according to a company executive.

“We are working on adding generative AI to our platform and hope that we have something by the end of the year,” said Amir Piltan, senior product manager of AI at Mendix.

The company, according to Piltan, had access to GPT (Generative Pretrained Transformer) technology in beta, even before it was released to the public by OpenAI and it has been running experiments with the technology to see how best it can be incorporated to its low-code platform.

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Relational database provider EnterpriseDB (EDB) on Tuesday said it has started offering a new Oracle -to- Postgres migration service, dubbed EDB Guaranteed Postgres Migration program.

The new migration program will ensure faster migration while providing a “zero risk” guarantee that allows enterprises to not pay for the entire cost of migration if their expectations are not met, the company said.

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As you likely know by now, WebAssembly, or wasm, is an efficient, cross-platform, cross-language way to run code almost anywhere, including in a browser and on a server—even in a database . Cosmonic is a commercial platform-as-a-service (PaaS) for wasm modules. It builds on the open-source wasmCloud. This technology preview starts with a quick overview of wasm, then we'll set up wasmCloud and Cosmonic and see what we can do with them.

What is wasm?

WebAssembly (wasm) is a “binary instruction format for a stack-based virtual machine.” It’s a portable compilation target for programming languages, including C, C++, C#, Rust, Go, Java, PHP, Ruby, Swift, Python, Kotlin, Haskell, and Lua; Rust is often the preferred language for wasm. There are three wasm-specific languages: AssemblyScript, Grain, and Motoko. Wasm targets include browsers (currently Chrome, Firefox, Safari, and Edge), Node.js, Deno, Wasmtime , Wasmer , and wasm2c .

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It’s 9:00 Monday morning, and you arrive at the office for your first week of hybrid work to learn that your cloud application has been shut down by the security team. You later find out that the security team leader did not feel respected enough during the development process and went to the CIO and got your stuff shuttered until he or she could issue approval. It caused a three-week delay in launching a needed cloud-based system.

A normal response to this situation would be anger, but you likely made mistakes here. Clearly, there was some requirement or policy that you needed approval from the security team, but you chose not to get it. Of course, the security team knows nothing about cloud-based systems and security, so you figured they would be no help. Maybe that’s true. However, the end result is wasted time and money negatively impacting the business.

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Code and data integrity and encryption support in Java would get boosts from two different proposals floating in the open source Java community. One proposal would tighten strong encapsulation of Java APIs, and the second would deliver an API for Key Encapsulation Mechanism, an encryption technique that promises to provide protection against quantum computers.

A draft Java Enhancement Proposal (JEP) for integrity and strong encapsulation , posted in openjdk.org, is intended to restrict APIs to prevent them from breaking strong encapsulation, while still accommodating use cases that need to operate beyond encapsulation boundaries. Strong encapsulation is one of the Java platform features that assures code and data integrity by default, the JEP notes. However, this can be circumvented by some APIs, causing headaches for maintenance and performance.

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In a recent research survey from TechTarget's Enterprise Strategy Group, we found that only 4% of organizations have the ability to get real-time insights from their data. For another 28%, it takes days, and 45% report that it takes weeks or longer for them to gain insight from their data.1 There is a rapidly growing competitive and productivity gap building between companies leveraging data effectively and delivering actionable data for automated business processes and consumer actions and those not yet able to glean these insights from their data. While the pioneers of real-time analytics tended to be large organizations with substantial resources, cloud-native platforms now allow companies of any size to have cost-effective analytic capabilities to close the gap and accelerate innovations and time to value.

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All the work has paid off. Altogether, we improved Redis performance by up to four times the previous speed.

Redis’s GEO commands (formally geospatial indices ) are a powerful tool for working with geographic data. Redis can store an object’s geographic coordinates, such as store locations or delivery trucks on the move. Then Redis can perform math and coordinate-based operations on that data, such as determining the distance between two points (How far away is the delivery car with my lunch order?) or finding all registered points within a given radius of another point (Where is the closest pet store to my current location?).

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Redis assumes the default GCC compiler on supported operating system distributions for reasons of portability and ease of use. The build compiler flags, present since the early stages of the project, remained quite conservative across the years, with -O2 as the default optimization level. These flags have worked well. They provide consistent results and good performance without significantly increasing the compilation time or code size of Redis server binaries. Nobody wants to spend a lot of time on compilation.

However, could we do a bit better? (We are optimization specialists. We always ask this question.)

In this post, we describe our analysis of the impact on Redis performance by changing compiler versions (GCC 9.4, GCC 11, and Clang-14) and compiler flag optimizations. We evaluated the performance impact of compilers and flags using performance automation, as we discussed in Introducing the Redis/Intel Benchmarks Specification for Performance Testing, Profiling, and Analysis . As a result of this work, we updated the default Redis compiler flags because they ensure better performance.

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Cloud Service Providers (CSPs) offer an ever-expanding array of instance types, ensuring that for any given workload there exists the perfect hosting option that matches the exact needs of that app or business service. But with this expansion comes an ever-increasing challenge to match the workloads to the offerings–there are many things to consider:

• Capacity: Total CPU, Memory, and I/O resources needed
• Ratio of resources: CPU optimized? Memory optimized?
• Workload pattern: Burstable? Horizontal elasticity?
• Storage: Local disk? EBR? NVMe?
• Hypervisor: Compatibility between generations
• Regional availability
• Volume commitments

Intel Cloud Optimizer (ICO) is designed to consider these factors and more, analyzing workloads against the provider offerings in detail to find the optimal hosting option. This eliminates resource risk, keeps instances on the latest offerings, avoids stranded capacity that causes unnecessary cost, and optimizes the price/performance for an organization’s full set of workloads. The precision and depth of ICO means that the recommendations generated are actionable and can be automated when an organization is ready.

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