Month: July 2025

This week’s Java roundup for July 7th, 2025, features news highlighting: the GA release of JobRunr 8.0; the second release candidate of Gradle 9.0; the fifth milestone release of Grails 7.0; point releases of Micronaut and JHipster Lite; and a CVE that affected Apache Tomcat.

OpenJDK

JEP 504, Remove the Applet API, was elevated from Candidate to Proposed to Target for JDK 26. This JEP proposes to remove the Applet API, deprecated in JDK 17, due it’s continued obsolescence since applets are no longer supported in web browsers. The review is expected to conclude on Monday, July 14, 2025.

Version 8.0.0 of the Regression Test Harness for the JDK, jtreg, has been released and ready for integration in the JDK. The most significant change is the bundling of AsmTools 8.1 and JUnit 5.13.3. There was also a fix for the --verify-exclude command line option that failed to abort a test run when failures were discovered. More details on this release may be found in the release notes.

JDK 25

Build 31 of the JDK 25 early-access builds was made available this past week featuring updates from Build 30 that include fixes for various issues. Further details on this release may be found in the release notes.

JDK 26

Build 6 of the JDK 26 early-access builds was also made available this past week featuring updates from Build 5 that include fixes for various issues. More details on this release may be found in the release notes.

TornadoVM

The release of TornadoVM 1.1.1 ships with bug fixes and improvements such as: a refactoring of the Matrix4x4Float class that includes changing the access specifier to public on the get() and set() methods; and performance improvements in dynamic reconfiguration processing in the TornadoTaskGraph class that stores a new execution plan and dispatches the correct execution plan depending on the input policy. Further details on this release may be found in the release notes.

JobRunr

After two beta releases and one release candidate, JobRunr 8.0.0 has been released that introduces Carbon Aware Job Processing, a new feature that optimizes the carbon footprint, that is, the lowest amount of CO2 being generated, when scheduling jobs. Other new features include: improvements in scheduling recurring jobs especially for users of Spring Boot, Quarkus and Micronaut; and a new @AsyncJob annotation that allows for scheduling a method to be executed as a background job. More details on this release may be found in the release notes and the launch webinar. InfoQ will follow up with a more detailed news story.

Micronaut

The Micronaut Foundation has released version 4.9.1 of the Micronaut Framework, based on Micronaut Core 4.9.7, featuring bug fixes and patch updates to modules: Micronaut JAX-RS, Micronaut Data, Micronaut SQL Libraries, Micronaut AOT and Micronaut OpenAPI. Further details on this release may be found in the release notes.

Apache Software Foundation

The release of Apache Camel 4.13.0 delivers bug fixes, dependency upgrades and improvements such as: refactor the BackOffTimer interface and BackOffTimerTask class so that they may be more easily managed and observed; and support for the JetStream API in the Camel NATS component. More details on this release may be found in the release notes.

CVE-2025-53506, disclosed by the Apache Tomcat team, is an Uncontrolled Resource Consumption vulnerability in versions 11.0.0-M1 to 11.0.8, 10.1.0-M1 to 10.1.42 and 9.0.0.M1 to 9.0.106 if “an HTTP/2 client did not acknowledge the initial settings frame that reduces the maximum permitted concurrent streams” that may cause a denial-of-service.

JHipster

The release of JHipster Lite 1.33.0 provides bug fixes, improvements in documentation, dependency upgrades and new features such as: support for the pnpm package manager; and a change in Liquibase configuration by using properties provided by SpringBoot. This version aligns with Spring Boot 3.5.3 and Angular 19.

A breaking change in this release is the renaming of classes prepended with Npm (such as NpmVersions) are now prependended with Node or NodePackages (such as NodeVersions and NodePackagesVersions). Further details on this release may be found in the release notes.

Grails

The fifth milestone release of Grails 7.0.0 features bug fixes and notable changes such as: expose the Maven publication name so that it may be used in the Grails-related publication classes; and an update to Gradle dependency configurations across various modules to replace the use of implementation scope to developmentOnly scope for WebJar dependencies to ensure they are only included in the development environment. More details on this release may be found in the release notes.

JBang

The release of JBang 0.127.15 ships with bug fixes and new features such as: support for JEP 512, Compact Source Files and Instance Main Methods, that generates simple main() methods when using the command, jbang init --java 25; and the ability to copy/paste Java code into JBang that was once only possible with jshell. There was also a fix for Windows keyboard input that didn’t work too well. Further details on this release may be found in the release notes.

Gradle

The second release candidate of Gradle 9.0.0 provides bug fixes and new features such as: a minimal JDK 17 version; the configuration cache is now enabled by default as it has been declared as the preferred mode of execution for developers; and support for Kotlin 2.1 and Groovy 4.0. More details on this release may be found in the release notes.

Available in the Armv9-A architecture, Arm Scalable Matrix Extension 2 (SME2) is a set of advanced CPU instructions designed to accelerate matrix heavy computation. The new Arm technology aims to help mobile developers to run advanced AI models directly on CPU with improved performance and efficiency, without requiring any changes to their apps.

SME2 builds on the previously available SME extension, which introduced matrix operations and streaming vectors, by adding acceleration and support for multi-vector data-processing instructions, load to and store from multi-vectors, and a multi-vector predication mechanism.

While the performance benefits of SME2 are already available on the latest iOS devices and Apple M4-series chips, they will soon reach Android devices as well, says Alex Spinelli, Arm’s VP of AI and Developer Platforms and Services.

Matrix workflows are key for real-time mobile inference tasks such as image and language processing and voice generation. In particular, comparisons between SME2-enabled and non-SME2-enabled workflows shows a significant improvement, says Arm:

On SME2-enabled hardware, Google’s Gemma 3 model delivers 6x faster chat responses, and can start summarizing up to 800 words in under a second on a single CPU core.

Likewise, a 2.6x speed up has been measured for prompt processing on a vivo X200 Pro flagship smartphone running a 3.8B parameter Phi-3 Mini model.

To help developers take advantage of SME2, Arm provides a library called KleidiAI, which is integrated in Google’s XNNPACK. XNNPACK powers several machine learning and AI frameworks, including Alibaba’s MNN, Google’s LiteRT, Microsoft’s ONNX Runtime, and llama.cpp.

When SME2 is enabled and compatible, XNNPACK automatically routes the matrix heavy operations to SME2 via KleidiAI, so developers directly benefit with no changes needed in application logic or infrastructure.

KleidiAI is designed to be integrated easily into C and C++ codebases thanks to its micro-kernel based architecture.

A micro-kernel, in Arm’s parlance, refers to the “near-minimum amount of software to accelerate a given ML operator with high performance”, such as for example, packing or matrix multiplication. A key detail to explain why a micro-kernel is not simply a function, is that each micro-kernel processes only a portion of the output tensor, enabling the full operation to be dispatched across multiple threads.

In addition, KleidiAI has other features that will be welcome to developers, including it not relying on external dependencies, not using dynamic memory or requiring memory management, and a highly modular design where each micro-kernel is a stand-alone library consisting only of .c and .h files.

To help developers take advantage of SME2, Arm has released additional resources showcasing real-world examples of LLM-based apps using LiteRT, MNN, PyTorch and other supported frameworks.

Key Points

• The Nasdaq Composite returned 12% annually during the last two decades, and investors can reasonably expect similar returns in the future.

• AppLovin is an adtech company that has differentiated itself from peers with superior targeting capabilities, driven by its artificial intelligence (AI) recommendation engine called Axon.

• MongoDB develops the leading document-oriented database, a technology that lends itself to AI applications, and the current valuation is cheap versus the three-year average.

• 10 stocks we like better than AppLovin ›

Anticipating what the stock market will do in any given year is impossible, but investors can lean into long-term trends. For instance, the Nasdaq Composite (NASDAQINDEX: ^IXIC) soared 875% in the last 20 years, compounding at 12% annually, due to strength in technology stocks. That period encompasses such a broad range of market and economic conditions that similar returns are quite plausible in the future.

Indeed, the rise of artificial intelligence (AI) should be a tailwind for the technology sector, and most Wall Street analysts anticipate substantial gains in these Nasdaq stocks:

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• Among 31 analysts who follow AppLovin (NASDAQ: APP), the median target price of $470 per share implies 40% upside from the current share price of $335.
• Among 39 analysts that follow MongoDB (NASDAQ: MDB), the median target price of $275 per share implies 34% upside from the current share price of $205.

Here’s what investors should know about AppLovin and MongoDB.

Image source: Getty Images.

AppLovin: 40% upside implied by the median target price

AppLovin builds adtech software that helps developers market and monetize applications across mobile and connected TV campaigns. The company is also piloting ad tech tools for e-commerce brands. Importantly, its platform leans on a sophisticated AI engine called Axon to optimize campaign results by matching advertiser demand with the best publisher inventory.

AppLovin has put a great deal of effort into building its Axon recommendation engine. The company started acquiring video game studios several years ago to train the underlying machine learning models that optimize targeting, and subsequent upgrades have encouraged media buyers to spend more on the platform over time.

Morgan Stanley analyst Brian Nowak recently called AppLovin the “best executor” in the adtech industry. In particular, he called attention to superior ad targeting capabilities driven by its “best-in-class” machine learning engine, which has led to outperformance versus the broader in-app advertising market since 2023.

AppLovin reported strong first-quarter financial results. Revenue increased 40% to $1.4 billion, as strong sales growth in the advertising segment offset a decline in sales in the mobile games segment. Generally accepted accounting principles (GAAP) net income increased 149% to $1.67 per diluted share. And management guided for 69% advertising sales growth in the second quarter.

Wall Street estimates AppLovin’s earnings will increase at 53% annually through 2026. That makes the current valuation of 61 times earnings look rather inexpensive. Investors should pounce on the opportunity to buy this stock today. Personally, I would start with a small position and add shares periodically.

MongoDB: 34% upside implied by the median target price

MongoDB is the most popular document database. Whereas traditional relational databases (also called SQL databases) store information in structured rows and columns, the document model is more scalable and flexible. It supports structured data, but also unstructured data like emails, social media posts, images, videos, and websites.

Every application requires a database. It is where information can be stored, modified, and retrieved when needed. But the document model is particularly well suited to analytics, content management, e-commerce, payments, and artificial intelligence applications due to its superior scalability and flexibility. MongoDB is leaning into demand for AI.

Last year, the company introduced MAAP (MongoDB AI Application Program), a collection of resources and reference architectures that help programmers build applications with AI capabilities. Additionally, MongoDB recently acquired Voyage AI, a company that develops embedding and reranking models that make AI applications more accurate and reliable.

CEO Dev Ittycheria told analysts: “MongoDB now brings together three things that modern AI-powered applications need: real-time data, powerful search, and smart retrieval. By combining these into one platform, we make it dramatically easier for developers to build intelligent, responsive apps without stitching together multiple systems.”

MongoDB reported encouraging first-quarter financial results, exceeding estimates on the top and bottom lines. Customers climbed 16% to 57,100, the highest net additions in six years. Revenue increased 22% to $549 million, a sequential acceleration, and non-GAAP earnings jumped 96% to $1.00 per diluted share.

Going forward, Grand View Research estimates the database management system market will increase at 13% annually through 2030. MongoDB should grow faster as it continues to gain market share. That makes the present valuation of 7.8 times sales look reasonable, especially when the three-year average is 13.2 times sales. Patient investors should feel comfortable buying a small position today.

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Trevor Jennewine has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends AppLovin and MongoDB. The Motley Fool has a disclosure policy.

Disclaimer: For information purposes only. Past performance is not indicative of future results.

This course is a favorite for beginners. It covers everything needed to build websites from scratch, starting withHTML and CSS, then moving on to JavaScript, Node.js, React, MongoDB, and even some basic Web3 concepts. The content is packed with projects, such as a personal blog, a weather app, and a to-do list, that help build confidence step by step.
With more than 400,000 reviews and a 4.7-star rating, it’s clear that students really like this course. It’s also updated frequently, so the content remains current with the latest tech trends.

Best for: Those who are beginners and would like to learn both front-end and back-end skills and create real projects in the process.

Also Read:Top YouTube Creators to Follow for Web Development Tricks

Microsoft has released the Azure DevOps Model Context Provider (MCP) Server in public preview.

The release enables GitHub Copilot to access and interact with Azure DevOps project data using natural language prompts within developer environments such as VS Code.

The MCP Server acts as a local bridge between GitHub Copilot in Agent Mode and a developer’s Azure DevOps instance. It makes structured project data such as work items, pull requests, test plans, builds, and wiki entries available as context for the AI assistant. This allows developers to query, create and update project information conversationally without leaving their IDE. According to Microsoft product manager Dan Hellem, “This lets the assistant give better, more accurate, and more relevant answers tailored to your specific Azure DevOps project.”

The MCP Server runs locally, which ensures that project data does not leave the developer’s network. It is installed as a Node.js service and integrates with Copilot through a configuration file and Azure CLI authentication.

This initial version provides functionality for core components within Azure DevOps. Developers can list projects, repositories, builds, releases, test plans, teams and iterations. Work items can be created and edited through natural language, with Copilot able to suggest the content automatically. Test cases and pull requests can also be generated and linked to backlog items. In a LinkedIn post, Lyon Till, Senior Software Engineer at Microsoft, described the experience as “No more context switching – manage your entire DevOps workflow without leaving VS Code or Visual Studio.”

To get started, developers install the MCP Server from the official GitHub repository, sign in using the Azure CLI, configure their local mcp.json file and start the service. Once running, Copilot Agent Mode can handle commands such as “list work items assigned to me” or “create a user story for login failure handling”, translating these into authenticated REST API calls to Azure DevOps.

The preview release is currently limited to Azure DevOps Services. On-premises Azure DevOps Server is not supported. Microsoft is inviting early adopters to share feedback and feature requests via GitHub Issues.

A separate MCP Server for Azure resource queries was launched in May, allowing developers to retrieve information about services such as Key Vault, Cosmos DB and Storage Accounts using natural language. The Azure DevOps MCP Server builds on the same underlying approach.

Documentation, setup guidance, and source code are available on GitHub. The preview is open to developers using Azure DevOps Services and GitHub Copilot Business or Enterprise licences.

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W (©StockStory)

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Max Juang /

2025/07/11 4:00 pm EDT

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What Happened?

A number of stocks fell in the afternoon session after the Trump administration announced intentions to impose a 35% tariff on many goods imported from Canada. 

This move is far more than a typical trade dispute; it targets the United States’ largest and most deeply integrated trading partner. Canada is not merely a neighbor but a critical component of North American supply chains, particularly in sectors like automotive, energy, and critical minerals.

This move has sparked concerns about potential retaliatory actions and a wider impact on the North American economy, leading to a risk-off sentiment among investors. The S&P 500, Dow Jones Industrial Average, and Nasdaq all opened lower, pulling back from recent record highs and heading for their first weekly loss in three weeks. 

The stock market overreacts to news, and big price drops can present good opportunities to buy high-quality stocks.

Among others, the following stocks were impacted:

Zooming In On Sabre (SABR)

Sabre’s shares are extremely volatile and have had 35 moves greater than 5% over the last year. In that context, today’s move indicates the market considers this news meaningful but not something that would fundamentally change its perception of the business.

Sabre is down 11.5% since the beginning of the year, and at $3.16 per share, it is trading 30.1% below its 52-week high of $4.52 from February 2025. Investors who bought $1,000 worth of Sabre’s shares 5 years ago would now be looking at an investment worth $408.80.

Do you want to know what moves the business you care about? Add them to your StockStory watchlist and every time a stock significantly moves, we provide you with a timely explanation straight to your inbox.
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On July 11, 2025, MongoDB’s trading volume was $347 million, a 43.81% decrease from the previous day, ranking 266th in the day’s stock market. MongoDB (MDB) fell 3.23%, marking two consecutive days of decline, with a total drop of 6.73% over the past two days.

MongoDB, a leading provider of general-purpose database platforms, has been facing challenges in the market. The company’s recent financial performance has been under scrutiny, with investors expressing concerns over its revenue growth and profitability. Despite these challenges, MongoDB continues to invest in its technology and expand its product offerings, aiming to maintain its competitive edge in the database market.

In response to the market’s concerns, MongoDB’s management has been actively communicating with investors, emphasizing the company’s long-term growth prospects. They have highlighted the increasing demand for cloud-based database solutions and the company’s strong position in this growing market. Additionally, MongoDB has been focusing on strategic partnerships and acquisitions to enhance its product portfolio and expand its customer base.

Despite the recent setbacks, MongoDB remains optimistic about its future. The company is confident in its ability to navigate the current market challenges and continue to deliver value to its shareholders. With a strong focus on innovation and customer satisfaction, MongoDB is well-positioned to capitalize on the growing demand for database solutions in the digital age.