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Texas Property Tax Debate’s Impact On Homeowners

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Texas Property Tax Debate’s Impact On Homeowners
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The Texas property tax war between Gov. Greg Abbott and Lt. Gov. Dan Patrick continues — and $17.6 billion in Texas tax relief is on the line. Abbott promised big property tax cuts for Texans. Lawmakers have not come to an agreement yet, but reportedly, Abbott may be willing to compromise. 

Each side’s proposed property tax plans cut taxes, but how Texans could benefit will depend on the type and amount of property owned. After all, you can only take one residence property homestead exemption (aka property tax deduction for your primary residence) in Texas.

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AI accelerating API development, IBM says

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AI accelerating API development, IBM says
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IBM sees a confluence of generative 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. APIs empowering AI 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.

IBM offers the API Assistant for IBM API Connect, powered by the watsonx.ai integrated AI platform. It uses generative AI to help API teams accelerate API life-cycle activities for a quicker time to market, the company said. IBM API Assistant automates tasks, enabling teams to focus on higher-value work and innovation, according to IBM. API assistants are able to augment API teams, so they progress faster, Kaushik said.

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Java proposals would boost resistance to quantum computing attacks

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Both proposals warn of the threat posed to information security by advancements in the field of quantum computing. A future large-scale quantum computer could use Shor’s algorithm to compromise the security of widely deployed public-key-based algorithms. Such algorithms are used by the Java platform for activities such as digitally signing JAR (Java archive) files and establishing secure network connections. An attack could be accomplished by a quantum computer using Shor’s algorithm in hours. Cryptographers have responded to this threat by inventing quantum-resistant algorithms that cannot be defeated by Shor’s algorithm. Switching to quantum-resistant algorithms is urgent, even if large-scale quantum computers do not yet exist.

Each of the two proposals is eyed for the Standard Edition of Java, but neither is targeted for a specific version at this point. Both proposals were created August 26 and updated November 6.

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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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