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Microsoft previews AI ‘building blocks’ for .NET

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Microsoft previews AI ‘building blocks’ for .NET
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Core benefits of the Microsoft.Extensions.AI libraries include:

  • Providing a consistent set of APIs and conventions for integrating AI services into .NET applications.
  • Allowing .NET library authors to use AI services without being tied to a specific provider.
  • Enabling .NET developers to experiment with different packages using the same underlying abstractions, maintaining a single API throughout an application.
  • Simplifying the addition of new capabilities and facilitating the componentization and testing of applications.

Instructions on getting started with the Microsoft.Extensions.AI packages can be found in the October 8 blog post. Microsoft’s current focus is on creating abstractions that can be implemented across various services, the company said. There is no plan to release APIs tailored to any specific provider’s services. Microsoft’s goal is to act as a unifying layer within the .NET ecosystem, enabling developers to choose preferred frameworks and libraries while ensuring integration and collaboration across the ecosystem.

In explaining the libraries, Microsoft’s Luis Quintanilla, program manager for the developer division, said AI capabilities are rapidly evolving, with common patterns emerging for functionality such as chat, embeddings, and tool calling. Unified abstractions are crucial for developers to work across different sources, he said.

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Serverless computing’s second act | InfoWorld

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Serverless computing’s second act | InfoWorld
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Despite these issues, the hype train was at full speed. For example, a large provider took issue with me pointing out some of the shortcomings of this “new” serverless technology. Instead of addressing the problems, they called for my immediate firing due to blasphemous comments. I hit a nerve. Why was that? The cloud providers promoting serverless should have had more confidence in their technology. They knew the challenges. I was right about serverless then, right when I wrote its decline. However, I’m always willing to reevaluate my position as technology evolves. I believe in redemption.

A technological comeback

Despite its early hurdles, serverless computing has bounced back, driven by a confluence of evolving developer needs and technological advancements. Major cloud providers such as AWS, Microsoft Azure, and Google Cloud have poured substantial resources into serverless technologies to provide enhancements that address earlier criticisms.

For instance, improvements in debugging tools, better handling of cold starts, and new monitoring capabilities are now part of the serverless ecosystem. Additionally, integrating artificial intelligence and machine learning promises to expand the possibilities of serverless applications, making them seem more innovative and responsive.

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Java app security would get a boost through quantum resistance

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Java app security would get a boost through quantum resistance
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Java application security would be enhanced through a couple of proposals to resist quantum computing attacks, one plan involving digital signatures and the other key encapsulation.

The two proposals reside in the OpenJDK JEP (JDK Enhancement Proposal) index. One proposal, titled “Quantum-Resistant Module-Lattice-Based Digital Signature Algorithm,” calls for enhancing the security of Java applications by providing an implementation of the quantum-resistant Module-Latticed-Based Digital Signature Algorithm (ML-DSA). Digital signatures are used to detect unauthorized modifications to data and to authenticate the identity of signatories. ML-DSA is designed to be secure against future quantum computing attacks. It has been standardized by the United States National Institute of Standards and Technology (NIST) in FIPS 204.

The other proposal, “Quantum-Resistant Module-Lattice-Based Key Encapsulation Mechanism,” calls for enhancing application security by providing an implementation of the quantum-resistant Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM). KEMs are used to secure symmetric keys over insecure communication channels using public key cryptography. ML-KEM is designed to be secure against future quantum computing attacks and has been standardized by NIST in FIPS 203.

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IBM: APIs getting AI boost

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IBM: APIs getting AI boost
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IBM sees a confluence of artificial intelligence and APIs, with AI powering APIs in a way that improves the productivity of API teams.

AI is augmenting skills that API teams may just be starting to learn, said Rashmi Kaushik, director of product management for the integration portfolio at IBM, during a presentation at the API World conference in Santa Clara, California, on November 6. “It’s able to help them complete their API projects faster.”  Also, APIs are powering AI, she added. AI empowering APIs and the rise of AI assistance are truly beneficial to API teams, Kaushik said.

Companies such as IBM have released API testing capabilities on traditional AI. But AI is not magic. It has been a technology in the making for many years now and it is here to transform the way business is done, Kaushik said. Regardless of how much AI is leveraged, users want to make sure that it is safe, responsible, and ethical, she said.

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GitHub Copilot learns new tricks

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GitHub Copilot learns new tricks
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You start with an existing project and the details of build tools and frameworks, along with a target Java version (for example upgrading from Java 8 to Java 21). The Copilot upgrade assistant analyses your code base and generates a list of the necessary steps to run your upgrade, presenting it as a set of GitHub issues that you can check before running the update.

Once you’re happy with the tasks, the tool takes you to a dashboard where you can watch the update process, including how Copilot rewrites code for you. You can stop and start the process at any time, drilling down into tasks for more information on just how the AI-based code is working. It’s good to have this level of transparency, as you need to be able to trust the AI, especially when it’s working on business-critical software.

As this is an agentic AI process, the service can detect errors and fix them, launching sub-agents that make changes, rebuild, and retest code. Interestingly if a fix doesn’t work, it’ll take another approach, using the shared knowledge of the Java developers whose work has been used to train the Copilot Java model. Like other GitHub Copilots, changes that work are used to fine-tune the model, reducing the risk of errors in future runs. That goes for manual updates and changes too.

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‘Package confusion’ attack against NPM used to trick developers into downloading malware

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‘Package confusion’ attack against NPM used to trick developers into downloading malware
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In other words, there is no single address, IP, or server to block. That said, there are downsides to the technique that are not mentioned by Checkmarx, including the fact that blockchain communication is slow, as well as public. The blockchains can’t be edited, or blocked easily, but they can be tracked once their use as part of malware C2 has been uncovered. 

Despite past predictions that the technique would take off, this is probably why using blockchains for C2 remains the experimental preserve of specialist malware.

Package confusion

Perhaps the more significant part of the story is that the technique is being used to target testing tools distributed via NPM, the largest open source JavaScript registry. Targeting testing tools is another way to get inside the privileged developer testing environments, and any deeper access to the CI/CD pipelines that they reveal.

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.NET vector data abstractions library now available in preview

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.NET vector data abstractions library now available in preview
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Microsoft has introduced its Microsoft.Extensions.VectorData.Abstractions library, now in preview. The library provides abstractions to help integrate vector stores into .NET applications and libraries.

The vector data abstractions library, introduced October 29, provides library authors and developers with the ability to perform create-read-update-delete (CRUD) operations and use vector and text search on vector stores.

Vector databases are important for search tasks and grounding AI responses, Microsoft said. These databases are built to store, index, and manage data represented as embedding factors. As a result, the indexing algorithms used by vector databases are optimized to retrieve data that can be used downstream in applications.

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Dataframes explained: The modern in-memory data science format

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Dataframes explained: The modern in-memory data science format
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import pandas as pd
data = {
    "Title": ["Blade Runner", "2001: a space odyssey", "Alien"],
    "Year": [1982, 1968, 1979],
    "MPA Rating": ["R","G","R"]
}
df = pd.DataFrame(data)

Applications that use dataframes

As I previously mentioned, most every data science library or framework supports a dataframe-like structure of some kind. The R language is generally credited with popularizing the dataframe concept (although it existed in other forms before then). Spark, one of the first broadly popular platforms for processing data at scale, has its own dataframe system. The Pandas data library for Python, and its speed-optimized cousin Polars, both offer dataframes. And the analytics database DuckDB combines the conveniences of dataframes with the power of a full-blown database system.

It’s worth noting the application in question may support dataframe data formats specific to that application. For instance, Pandas provides data types for sparse data structures in a dataframe. By contrast, Spark does not have an explicit sparse data type, so any sparse-format data needs an additional conversion step to be used in a Spark dataframe.

To that end, while some libraries with dataframes are more popular, there’s no one definitive version of a dataframe. They’re a concept implemented by many different applications. Each implementation of a dataframe is free to do things differently under the hood, and some dataframe implementations vary in the end-user details, too.

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12 Java Enhancement Proposals changing Java

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12 Java Enhancement Proposals changing Java
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Java’s internal systems and syntax are constantly evolving, and these changes happen primarily through the Java Community Process (JCP) and Java Enhancement Proposals (JEPs). Together, the JCP and JEPs define the path by which new features can be described, designed, and—hopefully—introduced into the JVM. They keep the Java language and platform dynamic and the community engaged. With JDK 24 so close to its planned release date, now is a good time to take a look at the upcoming JEPs making their way through the process.

Stages of the JEP process

You can check the JEP Index on the OpenJDK homepage for a catalog of all the JEPs, past and present, submitted for Java. The sheer size and scope of the index can be overwhelming at first. It’s not immediately clear what each JEP is about and which ones are more significant. You might expect major projects like virtual threads would be distinguished from proposals of smaller scope, but there is no such distinction. All the JEPs are listed, past and present, showing the complete history of Java’s evolution.

Instead of scope, the JEPs are organized according to their stage of development:

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Why scrum is dumb | InfoWorld

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Why scrum is dumb | InfoWorld
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Why this is true is a mystery to me. I fail utterly to see how scrum is a manifestation of the Agile Principles. In my view, scrum is in direct opposition to them, and in the end, doesn’t even remotely fulfill their purpose.

Cramming it with scrum

Everything about scrum tries to squeeze a large peg into a small hole. Scrum creates a fixed-sized hole of a given chunk of time, takes a large wooden block of effort, and tries to make a series of small, properly sized blocks fit into that hole.

Never mind that the size of the hole may or may not fit the blocks that you have. You are supposed to whittle them down so they fit, even if what you end up with isn’t really what you want or need. You are not allowed to change the size of the hole that the blocks are supposed to fit into. That block must fit into that hole, and that hole ain’t getting any bigger.

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WSO2 API managers manage AI APIs

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WSO2 API managers manage AI APIs
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  • Enhancements for WSO2 API Microgateway, a cloud-native, developer-centric decentralized gateway for microservices. The newest version has been updated to align with the latest WSO2 API Manager release for enhanced scalability while maintaining governance, security, and reliability, WSO2 said.
  • gRPC API support for WSO2 API Platform for Kubernetes Gateway, providing capabilities for developers and engineers building microservices architectures with gRPC. By aligning with the Kubernetes Gateway API specification, the support improves integration with Kubernetes environments and offers greater control over gRPC services, WSO2 said.
  • New traffic filters for HTTP routes, available with the newest WSO2 WSO2 API Platform for Kubernetes Gateway release. These offer flexibility in routing and transforming HTTP traffic.

WSO2 added that the newest WSO2 API Platform for Kubernetes release now includes support for subscription rate limit policies, already available in WSO2 API Manager. API providers can define and enforce usage quotas for API consumers.

WSO2 also said the unified control plane in WSO2 API Manager introduces an ability to search for APIs using content from API definition files such as Swagger, GraphQL, and WSDL, directly from the Developer Portal and Publisher Portal, to improve API discovery and usability. WSO2 API Manager also adds personal access token support, enabling secure, time-limited authentication access to WSO2 API Manager system APIs without exposing a user name and password.

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WSO2 API Manager 4.4, WSO2 API Platform for Kubernetes 1.2, and WSO2 API Microgateway 3.2 are generally available now as open source products.

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