Info World

Rust has leaped to its highest position ever in the monthly Tiobe index of language popularity, scaling to the 13th spot this month, with placement in the top 10 anticipated in an upcoming edition.

Previously, Rust has never gone higher than 17th place in the Tiobe Programming Index. Tiobe CEO Paul Jansen attributed Rust’s ascent in the just-released July index to a February 2024 US White House report recommending Rust over C/C+, for safety reasons. He also credited the growing community and ecosystem support for the language. “Rust is finally moving up. After the tailwind of the US government, which recently announced to recommend moving from C/C++ to Rust for security reasons, things are going fast for Rust,” Jansen said. “The community is growing, including the number of third-party libraries and tools. In short, Rust is preparing itself for a top 10 position in the Tiobe index.”

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In a highly-anticipated federal ruling on July 3, US District Court Judge Ada Brown determined that the US Federal Trade Commission (FTC) did not have the authority to issue a nationwide ban of non-compete agreements. Although the judge’s decision was preliminary, employment lawyers watching the case agree that the FTC non-compete move is effectively dead.

Brown, of the US District Court for the Northern District of Texas, said that she would issue a final ruling on Aug. 30, the day before the FTC ban was slated to take effect. But based on the strong wording of her preliminary decision, there seemed little doubt that she would ultimately block the ban. 

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As agents using artificial intelligence have wormed their way into the mainstream for everything from customer service to fixing software code, it’s increasingly important to determine which are the best for a given application, and the criteria to consider when selecting an agent besides its functionality. And that’s where benchmarking comes in.

However, a new research paper, AI Agents That Matter, points out that current agent evaluation and benchmarking processes contain a number of shortcomings that hinder their usefulness in real-world applications. The authors, five Princeton University researchers, note that those shortcomings encourage development of agents that do well in benchmarks, but not in practice, and propose ways to address them.

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In 2007, some of the programmers at Google looked at their options for writing software and didn’t like what they saw. They needed to manage millions of lines of code that would be constantly storing and shipping data for the world wide web. The code would juggle thousands or maybe millions of connections on networks throughout the globe. The data paths were full of challenges from race cases and concurrency.

The existing programming languages weren’t much help. They were built for games or managing desktops, or many of the other common tasks from a world before the web browser. Their rough edges and failure modes drove coders at Google crazy enough to start asking if there might be a better way. Was there something that could handle the I/O chores in just a few lines with all of the safety and security that Google needed?

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Well, that didn’t take long. After all the “this time it’s different” comments about artificial intelligence (We see you, John Chambers!), enterprises are coming to grips with reality. AI isn’t going to take your job. It’s not going to write your code. It’s not going to write all your marketing copy (not unless you’re prepared to hire back the humans to fix it). And, no, it’s nowhere near artificial general intelligence (AGI) and won’t be anytime soon. Possibly never.

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The landscape of cloud computing is changing significantly as enterprises question the value of public cloud solutions. This shift marks a departure from previous years when the public cloud was widely regarded as the panacea for all technology and infrastructure needs. Companies are now reconsidering the efficacy, cost efficiency, and strategic alignment of public cloud computing in their IT frameworks. We’ve also been discussing this topic here in recent years.

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Oracle celebrated the beginning of July with the general availability of three releases of its open source database, MySQL: MySQL 8.0.38, the first update of its long-term support (LTS) version, MySQL 8.4, and the first major version of its 9.x innovation release, MySQL 9.0.

While the v8 releases are bug fixes and security releases only, MySQL 9.0 Innovation is a shiny new version with additional features, as well as some changes that may require attention when upgrading from a previous version.

The new 9.0 versions of MySQL Clients, Tools, and Connectors are also live, and Oracle recommends that they be used with MySQL Server 8.0, and 8.4 LTS as well as with 9.0 Innovation.

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Refit is an open-source library for .NET, .NET Core, and Xamarin that makes consuming REST APIs simpler and easier by enabling you to define the API endpoints as C# interfaces, thereby eliminating the need to create HTTP requests and parse HTTP responses manually.

In this article we will delve into the Refit library for .NET and see first-hand how it simplifies the development of APIs. To use the code examples provided in this article, you should have Visual Studio 2022 installed in your system. If you don’t already have a copy, you can download Visual Studio 2022 here.

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The latest update of Visual Studio Code is previewing an alternative visualization of incoming and outgoing changes. The update also includes a new tool that enhances environment discovery for Python installations.

Introduced July 3, Visual Studio Code 1.91, also known as the June 2024 release of the editor, can be downloaded for Windows, Linux, and macOS at code.visualstudio.com.

The experimental incoming/outgoing changes graph provides a graph view of the current branch, the current branch’s upstream branch, and an optional base branch. The root of the graph is the common ancestor of these branches. Users can enable the new visualization with the scm.experimental.showHistoryGraph setting. 

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One of the key components of Microsoft’s Copilot Runtime edge AI development platform for Windows is a new vector search technology, DiskANN (Disk Accelerated Nearest Neighbors). Building on a long-running Microsoft Research project, DiskANN is a way of building and managing vector indexes inside your applications. It uses a mix of in-memory and disk storage to map an in-memory quantized vector graph to a high-precision graph help on disk.

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ECMAScript 2024, the latest version of ECMA International’s standard for JavaScript, has been officially approved, with features including transferring ArrayBuffers and advanced capabilities for working with string sets.

The ECMAScript 2024 specification, also generally known as ECMA-262, was approved on June 26. Among the features are added facilities for resizing and transferring ArrayBuffers and SharedArrayBuffers. ArrayBuffers have previously enabled in-memory handling of binary data. The new feature extends ArrayBuffer constructors to take an additional maximum length that allows in-place growth and shrinking of buffers. SharedArrayBuffer was also extended to take an additional maximum length that allows in-place growth.

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JetBrains has released an on-premises edition of Qodana, the company’s code quality platform based on the static code analysis engine of JetBrains IDEs. With Qodana Self-Hosted, users can manage and maintain the platform on their own infrastructure.

With Qodana Self-Hosted, announced July 3, developers gain complete control over analysis reports, user accounts, and other sensitive data, as well as Qodana maintenance and upgrades, JetBrains said. Qodana Self-Hosted offers a suite of features of the company’s advanced cloud plan, Qodana Ultimate Plus, that make static code analysis effective for organizations of any size, JetBrains said. The feature set includes an issues overview, integrations with JetBrains IDE and Visual Studio Code, quick fixes, CI/CD integrations, license auditing, rule-based custom code inspections, and other capabilities.

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AWS’ new theory on designing an automated RAG evaluation mechanism could not only ease the development of generative AI-based applications but also help enterprises reduce spending on compute infrastructure.

RAG or retrieval augmented generation is one of several techniques used to address hallucinations, which are arbitrary or nonsensical responses generated by large language models (LLMs) when they grow in complexity.

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The JavaScript language is one of the wonders of the software world. It is incredibly powerful, flexible, and versatile. One limitation of its fundamental design, however, is its single-threaded nature. Traditional JavaScript appears to handle parallel tasks, but that is a trick of syntax. To achieve true parallelism, you need to use modern multithreading approaches like web workers and worker threads.

The most basic way to understand the difference between parallelism and concurrency is that concurrency is semantic whereas parallelism is implementation. What I mean is that concurrency lets you tell the system (semantics) to do more than one thing at once. Parallelism simply performs multiple tasks simultaneously (implementation). All parallel processing is concurrent, but not all concurrent programming is parallel.

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Developed by Facebook and released as an open standard for all to use, GraphQL is intended as an alternative to REST APIs. Like REST, GraphQL provides a way to create and consume web-based APIs, but queries and returned data use formal schemas and a type system to guarantee consistency.

In this article, we’ll walk through the basics of designing and implementing a GraphQL API and discuss many of the key considerations and decisions you’ll make during the process.

If you’re planning to use GraphQL as your web application API, there is a very good chance the language and data components you’re already using will support your efforts. GraphQL libraries are available for almost every major language in production use. Clients are available for C#/.NET, Go, Java and Android, JavaScript, Swift/Objective-C, and Python, and the server libraries cover even more ground. 

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The Rust types team has made significant progress on the next-generation trait solver, according to a June 26 blog post.

Work done to stabilize the use of the next-generation trait solver in coherence checking surfaced small behavior regressions and hangs, which caused delays. But the team is close to compiling the standard library and the compiler with the new solver enabled everywhere. They expect a long tail of minor issues and behavioral differences from the existing implementation. There also are open design questions that will need to be resolved prior to stabilizing the new implementation. The goal is to use the next-generation trait solver everywhere by default.

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There are times when you need to associate metadata, or data that describes data, with classes, methods, or other elements in your Java code. For example, your team might need to identify unfinished classes in a large application. For each unfinished class, the metadata would include the name of the developer responsible for finishing the class and the class’s expected completion date.

Before Java 5, comments were Java's only flexible mechanism for associating metadata with application elements. But because the compiler ignores them, comments are not available at runtime. And even if they were available, you would have to parse the text to obtain crucial data items. Without standardizing how the data items were specified, they might be impossible to parse.

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Open-source vector database provider Qdrant has launched BM42, a vector-based hybrid search algorithm intended to provide more accurate and efficient retrieval for retrieval-augmented generation (RAG) applications. BM42 combines the best of traditional text-based search and vector-based search to lower the costs for RAG and AI applications, Qdrant said.

Qdrant’s BM42 was announced July 2. Traditional keyword search engines, using algorithms such as BM25, have been around for more than 50 years and are not optimized for the precise retrieval needed in modern applications, according to Qdrant. As a result they struggle with specific RAG demands, particularly with short segments requiring further context to inform successful search and retrieval. Moving away from a keyword-based search to a fully vectorized based offers a new industry standard, Qdrant said.

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AI research is hurtling forward, but our ability to assess its capabilities and potential risks appears to be lagging behind. To bridge this critical gap, and recognize the current limitations in third-party evaluation ecosystems, Anthropic has started an initiative to invest in the development of robust, safety-relevant benchmarks to assess advanced AI capabilities and risks.

“A robust, third-party evaluation ecosystem is essential for assessing AI capabilities and risks, but the current evaluations landscape is limited,” Anthropic said in a blog post. “Developing high-quality, safety-relevant evaluations remains challenging, and the demand is outpacing the supply. To address this, today we're introducing a new initiative to fund evaluations developed by third-party organizations that can effectively measure advanced capabilities in AI models.”

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It is becoming increasingly evident that serverless technology is losing relevance and will soon fade away in the cloud technology space. Why did this happen? What can we learn from this evolution of technology?

When serverless computing first hit the streets over a decade ago as a cloud computing paradigm, it saved us from needing to handle detailed compute and storage configurations. Everything was done automatically at the time of execution. This seemed more evolution than revolution because PaaS systems were already doing an aspect of this type of computing.

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