April 2026 Summaries

The integration of Integrated Development Environments (IDEs) in AI-assisted software development is becoming crucial for optimizing AI tool performance, as highlighted in the discussion on AI quality variables. IDEs provide a structural understanding of the codebase, which can significantly enhance the context for AI tools, influencing output quality and efficiency. With the rise of AI gateways used in managing AI development processes, IDE choices are increasingly relevant, as they impact AI tool metrics like context payload size and model call volume. JetBrains IDEs, in particular, offer an architectural advantage by maintaining a continuous structural representation of codebases, facilitating more efficient context building for AI tools. This allows for more streamlined AI development processes by making comprehensive codebase information readily available to AI assistants, a factor that is becoming more critical as coding work grows in complexity and autonomy.

PHPverse 2026, a community-inspired professional event for PHP developers, is set to be streamed live on June 9, 2026, on the PHP Annotated YouTube channel, hosted by Brent Roose and Nuno Maduro. This one-day event will feature curated talks, live Q&A sessions, and special announcements, marking PHP's 31st anniversary. Attendees can expect insights from influential voices in the PHP ecosystem, including a keynote by Elizabeth Barron of the PHP Foundation, discussions on AI's impact on PHP programming, and detailed explorations of major tools like WordPress and Symfony. Building on the success of PHPverse 2025, which celebrated PHP's 30th anniversary with over 15,000 viewers, the event aims to connect developers worldwide to share knowledge and foster community growth. Interested participants are encouraged to register, join the event’s Discord server, and engage with the community using the #PHPverse hashtag on social media.

The Ruby on Rails Developer Community Survey, organized by Planet Argon and supported by RubyMine, seeks to gather real-world insights from Rails developers to benefit the entire community by examining current usage trends, tools, workflows, app development and deployment, and the integration of AI into daily work. The survey results are openly shared, providing a data-driven perspective on the state of the Rails ecosystem and fostering community understanding and growth. This initiative highlights the importance of collective efforts in keeping the Ruby community connected and advancing, with RubyMine encouraging participation to ensure continuous development and innovation within the Rails community.

The tutorial by Kevin Kimani emphasizes enhancing observability in backend systems using OpenTelemetry, specifically for a Kotlin and Spring Boot application. Initially, it outlines the limitations of basic logging when dealing with complex systems that involve concurrent processes, where logs from different executions can interleave, complicating error tracing. The guide then demonstrates how OpenTelemetry can overcome these issues by providing a unified model for tracing, metrics, and logs, thus enabling developers to associate log entries with specific execution contexts via unique trace identifiers. By employing the OpenTelemetry Java Agent, developers can automatically instrument applications, adding trace contexts to logs without altering the source code. This approach not only simplifies debugging by allowing developers to isolate and examine individual execution traces but also enhances the system's overall observability, thereby improving performance monitoring and system design. The tutorial includes practical steps for setting up OpenTelemetry, highlighting the benefits of transforming unstructured logs into structured, queryable data that can be integrated into observability platforms for deeper insights.

The blog post by Jodie Burchell provides a comprehensive exploration of the Bag-of-Words (BoW) model in natural language processing (NLP), detailing its function of converting text into numerical vectors by counting word occurrences, thus allowing for effective text classification despite ignoring grammar and word order. The text delves into how BoW compares with modern NLP approaches, and it showcases a project using PyCharm to implement BoW for classifying news articles, emphasizing PyCharm's features like Jupyter Notebook integration and code intelligence for efficient model building. Additionally, the post discusses advanced techniques to enhance BoW models, such as stop word removal, lemmatization, and TF-IDF vectorization, and contrasts BoW's limitations, like loss of word order and large sparse vectors, with alternatives like word embeddings and transformer-based models. Overall, the text underscores BoW's enduring relevance as a simple yet powerful tool in NLP, especially for tasks where word presence is more critical than sequence, while also highlighting the advantages of using PyCharm for developing and refining NLP projects.

The inaugural JetBrains x Codex Hackathon in San Francisco gathered developers to create 39 IDE-native AI projects, with six finalists emerging over a single weekend. The event underscored the rapid evolution of AI within integrated development environments (IDEs), with participants leveraging the hackathon's intense format to transform ambitious concepts into working code. Notably, the top prize was awarded to Aditya Mangalampalli for his project "hyperreasoning," which developed a coding agent that evaluates reasoning paths before generating code. The hackathon demonstrated the potential of IDE-native tools and was supported by key industry partners like OpenAI and Cerebral Valley, who contributed judges and resources. The focus of the projects was not just on speed but on enhancing visibility, safety, and reliability in AI-driven development processes, reflecting a shift towards sustainable, long-term solutions in software engineering.

JetBrains has introduced two new features, the Skills Manager and Skill Repository, in their AI Assistant to enhance the usability and management of skills within their integrated development environment (IDE). The Skills Manager allows developers to install and manage skills once, making them available across multiple projects and supported agents, thus eliminating the need for repetitive setups. This feature helps developers by providing a centralized location to install, manage, and reuse skills, which can be categorized as IDE-level, project-level, or agent-specific based on their application. Meanwhile, the Skill Repository offers a curated, verified list of skills, ensuring safer adoption by screening for potential security threats and crediting original authors. These features aim to streamline the usage of skills for various development tasks, such as debugging CI failures or automating browser flows, while also expanding their availability over time.

Companies often face the dilemma of whether to build their own analytics solutions or purchase existing ones, particularly in the field of agentic analytics. While the initial impulse might be to create a custom solution, this approach can encounter significant challenges as it scales. Early-stage DIY setups may demonstrate impressive results, but they often overlook crucial aspects such as maintainability, consistency, and integration across diverse teams. Common pitfalls include ambiguous business logic, context-dependent answer quality, and the dual sources of noise from user queries and metric definitions which complicate the system's reliability. As the project grows, maintaining these systems becomes increasingly complex, transitioning from a lightweight prototype to a comprehensive internal product that demands significant ongoing investment. The decision to build or buy hinges on how easily business logic can be defined and reused, the system's adaptability to evolving data stacks, and the potential for continuous improvement or manual intervention. For most companies, buying a reliable platform like Databao, which offers a structured semantic layer and seamless integration into existing workflows, presents a more feasible and efficient alternative.

Go is a popular language for building web services, with 46% of developers using it for this purpose, sparking debates about the necessity of web frameworks. While some developers prefer the standard Go library, net/http, for its robustness and simplicity, frameworks like Gin, Echo, Chi, and Fiber offer additional functionalities and conveniences, albeit with trade-offs. Gin, favored by 48% of developers, is known for its speed and extensive community support. Echo, used by 16%, offers a minimalist approach with features like HTTP/2 support. Chi, preferred by 12%, is a lightweight router compatible with net/http, while Fiber, popular among JavaScript developers, uses the fasthttp engine for high performance but at the cost of compatibility. Despite the lack of a dominant framework akin to Ruby on Rails or Python's Django, the choice of framework in Go often depends on specific project needs, performance requirements, and team familiarity.

TeamCity 2026.1 introduces significant updates aimed at making continuous integration and continuous delivery (CI/CD) processes more intuitive, intelligent, and enterprise-ready, featuring support for both Kotlin DSL and YAML in pipelines. The new TeamCity CLI enhances developer experience by allowing them to manage commands directly from the terminal and integrates with popular AI coding agents such as Claude Code, Codex, and JetBrains Junie. It also includes MCP support for connecting with leading AI tools across various environments. The live session, led by Daniel Gallo and Ernst Haagsman from JetBrains, will provide insights into these updates, showcasing AI-assisted workflows, flexible pipeline experiences, and the future roadmap for TeamCity. This session is designed for both current TeamCity users and those exploring modern CI/CD methodologies, offering practical insights into optimizing CI/CD build pipelines.

JetBrains is focusing on integrating AI into their IDEs while preserving traditional coding workflows, ensuring both methods coexist without interference. Developers have the flexibility to either write code manually or utilize AI for code generation, with the IDE supporting a natural and powerful experience for both approaches. The company's AI strategy emphasizes avoiding vendor lock-in by supporting multiple connection methods, including JetBrains AI subscriptions, API keys, OAuth, and the Agent Client Protocol (ACP) for external agents. JetBrains stresses the importance of treating AI-generated code with the same scrutiny as human-written code, ensuring changes are visible, reversible, and maintain code integrity. The IDE remains the central platform for developers to review and own their code, whether generated by AI or manually, and JetBrains commits to building AI workflows that enhance productivity while respecting professional coding standards.

JetBrains has introduced two new features, the Skills Manager and Skill Repository, to enhance the functionality of its AI Assistant in integrated development environments (IDEs). These features allow developers to install and manage reusable skills, which are capabilities for specific tasks, across multiple projects and supported agents, eliminating the need to rebuild setups for each use case. The Skills Manager offers a centralized platform for installing, managing, and reusing skills across projects, agents, and workflows, while the Skill Repository provides a curated and verified list of skills to simplify adoption and ensure security against malicious code. These improvements aim to streamline the development process by making skills more accessible and safer to implement, with ongoing expansions to include more IDE-specific skills.

JetBrains has continued its evolution in 2025, adapting to the fast-paced changes of the software development industry, particularly with the rise of AI. The company remained committed to developing tools that align with developers' needs, resulting in significant milestones across its products, business, and the communities it supports. The year was marked by advancements in AI-powered development and increased enterprise adoption, reflecting a period of substantial momentum for JetBrains. For a comprehensive look at these achievements and notable events, readers are encouraged to explore the full JetBrains Annual Highlights.

Static code analysis is a crucial tool in software development that helps reduce the financial and operational costs associated with software bugs by detecting issues early in the development process. By shifting the identification of bugs to earlier stages, it minimizes the impact and cost of fixes, as bugs discovered later can lead to significant downtime, security breaches, and increased technical debt. The practice enhances code quality by automating checks for vulnerabilities and ensuring compliance with security standards, effectively preventing problematic code from being integrated into the main codebase. Tools like Qodana, built on JetBrains IDEs, exemplify this approach by providing real-time feedback, consistent code standards, and suggested fixes within familiar development environments, thereby streamlining integration and reducing the need for costly refactoring and downtime. Static code analysis, particularly when integrated into CI/CD pipelines, ensures that bugs are addressed promptly, enhancing overall software quality and preventing costly post-release issues.

AI advancements are rapidly enhancing coding capabilities, but they also pose risks of introducing inconsistencies and security vulnerabilities without proper oversight in the codebase. The livestream hosted by JetBrains experts, including Kai Schmithuesen, Alex Costa, and Artem Rokhin, aims to demonstrate how tools like Qodana and TeamCity can integrate into a CI pipeline to enforce quality checks on AI-generated code. By applying deterministic and repeatable inspections, these tools help maintain code quality, reduce review bottlenecks, and boost developer confidence, ensuring issues are caught before reaching production. The session is designed to provide insights into creating efficient and reliable workflows for teams utilizing AI-assisted development, whether in the experimental phase or full production use.

CLion's upcoming 2026.2 release is set to focus on enhancing build tools, project formats, and debugging capabilities, with a particular emphasis on simplifying debugger configuration and supporting multiple Zephyr West profiles. The new release aims to streamline the debugging process by introducing a unified configuration workflow and enabling easier inspection of fields and global variables, while also allowing configuration-specific breakpoints. Improvements are planned for the Project tool window UI to better handle external sources, and CMake target renaming will be simplified with a new refactoring operation. Additionally, support for the Bazel plugin will be expanded, and updates to bundled toolchains, including CMake, GDB, and GCC, are anticipated. Embedded developers will benefit from improved management of Zephyr West build configurations and extended live watch capabilities for global variables. The Early Access Program will soon allow users to explore these features before the official release.

AI is increasingly prevalent in software development, with widespread adoption among developers for tasks such as coding, debugging, and documentation. However, its integration into CI/CD pipelines remains limited due to concerns about reliability and the high cost of errors in these environments. CI/CD systems are designed to ensure stability and control, providing confidence in code releases through consistent validation signals. AI's role in these pipelines is currently focused on enhancing efficiency, such as speeding up failure analysis, aiding in security workflows, and optimizing tests, rather than replacing human decision-making. As AI begins to participate more actively in DevOps workflows, it introduces variability that requires robust validation processes and governance mechanisms to maintain trust. The evolution of AI in CI/CD will depend on the ability of these systems to accommodate increased change volumes while ensuring release readiness, highlighting the need for a balance between automation and human oversight.

IntelliJ IDEA 2026.1.1 has been released, introducing several important fixes and improvements to enhance user experience and functionality. Users can update to this version through the IDE, Toolbox App, snaps for Ubuntu, or by downloading it from the website. Key updates include the restored ability to set up a WSL Python SDK, improved functionality of Emmet in remote development, and successful Gradle sync without class cast errors. The IDE now connects properly to the WildFly admin process, supports WSL 2 JDK location, and allows double-clicking Ant targets to display build output. Additionally, search and code completion in large Spring projects have been optimized for better responsiveness, and the IDE supports creating run configurations for local WebLogic servers. The Find and Replace action has also been improved to work as expected. Users are encouraged to refer to the release notes for more detailed information and report any bugs to the issue tracker.

AI can significantly enhance the productivity of developers, but it also amplifies existing strengths and weaknesses within teams, according to various reports and studies. High-performing organizations and experienced developers typically benefit the most from AI, producing more durable code and increasing output, while those with less robust foundations may find their challenges exacerbated. Key areas of focus for technical leaders include improving code review processes to manage increased code volume, maintaining discipline in managing technical debt to prevent accumulation, and fostering developer judgment to ensure the effective use of AI tools. Studies indicate that AI can help reduce technical debt and improve documentation, but it can also lead to more bugs and code complexity if not managed properly. Furthermore, AI may reduce opportunities for junior developers to build critical skills, emphasizing the need for structured mentoring and training. Ultimately, the successful integration of AI requires a balance between standardizing core processes and allowing flexibility in tool usage to accommodate evolving technologies and individual developer needs.

PyCharm's fundraising initiative for Django highlights the crucial role that Django, a robust web framework, plays in the era of AI-driven development, where rapid code generation often overshadows the need for dependable foundations. Despite AI's ability to quickly scaffold web apps, the stability and longevity offered by frameworks like Django remain indispensable. PyCharm's campaign aims to support the Django Software Foundation (DSF) by offering discounts on PyCharm Pro, with proceeds going to DSF. This initiative emphasizes Django's enduring value and its 'batteries included' philosophy, which simplifies development for both humans and AI agents. PyCharm enhances Django development by providing comprehensive support for navigating, refactoring, and debugging projects, and users are encouraged to support Django through this fundraiser to ensure the framework's continued innovation and community support.

Incremental highlighting for Scala in IntelliJ IDEA offers a novel approach to code analysis by focusing on the visible area of code rather than the entire file, which enhances responsiveness and reduces resource consumption. This approach diverges from traditional compilers that analyze entire files, as IDEs like IntelliJ can re-highlight only parts of a file, optimizing CPU usage and extending battery life. By enabling incremental highlighting in Scala's editor settings, users experience smoother scrolling and efficient memory usage, especially beneficial for the complex nature of Scala code. Although some inspections remain inactive, this method allows for quicker code navigation and reduced distraction from unrelated errors. The feature, which has been available since version 2024.3, is still under development, and users are encouraged to provide feedback to help refine its capabilities further.

JetBrains has collaborated with LinkedIn Learning to introduce the Kotlin Professional Certificate, a comprehensive program designed for developers with basic programming skills who want to learn Kotlin and explore multiplatform development. The certification path includes four courses that cover essential aspects of Kotlin, from functions and object-oriented programming to multiplatform development and building full-stack applications using JetBrains' Ktor framework and Compose Multiplatform. Participants will gain practical experience using IntelliJ IDEA, and upon completion, can earn a certificate to showcase their skills on LinkedIn. This course is accessible through a LinkedIn Premium subscription, with a one-month free trial available, and may also be offered through organizations, universities, or public libraries.

Static code analysis tools are essential for improving code quality, reducing bugs, enhancing security, and ensuring consistency in code reviews, but selecting the right tool for a team can be challenging due to varying needs and workflows. Qodana, developed by JetBrains, integrates seamlessly with JetBrains IDEs and emphasizes making code quality checks a natural part of the development process, appealing to teams that value maintainability and consistency. SonarQube offers broad language coverage and a governance-oriented approach, while Snyk integrates static analysis into a comprehensive security strategy. Semgrep provides flexibility with customizable rules, making it suitable for teams wanting control over detection logic, whereas Checkmarx caters to large enterprises with a focus on security and compliance. Aikido is designed for smaller teams seeking broad security coverage, and Codacy combines AI-driven code quality and security in one platform. Ultimately, the best tool aligns with a team’s specific priorities, whether that be centralized control, security, or developer-friendly workflows.

Kotlin Multiplatform (KMP) is presented as a transformative strategy for mobile development, shifting away from traditional siloed approaches that require separate codebases for iOS and Android, thereby reducing feature lag and technical debt. By enabling a shared business logic layer while maintaining native performance and UI, KMP significantly lowers the total cost of ownership and accelerates development timelines, making mobile engineering more agile and cost-effective. The adoption of KMP aligns engineering resources with business priorities, enhancing team flexibility and retention by allowing developers to operate as mobile engineers rather than platform-specific specialists. Companies like Google, Duolingo, and Forbes have reported substantial improvements in development speed, code reuse, and reduction in engineering time by integrating KMP into their workflows. Through empirical data and case studies, the article suggests that organizations can achieve a measurable return on investment within three to six months of adopting KMP, especially when guided by experts such as Touchlab, who provide strategic support to streamline the transition.

The JetBrains Marketplace News Newsletter for Q1 2026 highlights significant updates and resources for plugin developers, including new approval guidelines that prohibit plugins from altering the core functionality of JetBrains products, particularly in areas related to licensing and subscriptions. The newsletter showcases a redesign of the Build Plugins page to simplify the plugin creation process, updates to development tooling such as the IntelliJ Platform Plugin Template and IntelliJ Plugin Verifier, and enhancements to the IntelliJ Platform Gradle Plugin for improved defaults and compatibility. It also introduces tips like enabling Internal Mode for advanced development tools and discusses new features such as native Wayland support in upcoming IntelliJ-based IDEs, editor improvements, and insights into developers' changing priorities. The Community Spotlight section includes a livestream discussion on avoiding UI freezes in plugins and a video explaining the new Plugin Model v2 architecture for better modularity and dependency management.

In March 2026, a security vulnerability identified as CVE-2026-33392 was discovered in YouTrack, affecting all versions prior to 2025.3.132953. This flaw, a sandbox bypass allowing potential code execution, required administrator-level permissions to exploit and was particularly significant for YouTrack Cloud, where it could breach cross-tenant isolation on shared hardware. YouTrack Server, being single-tenant, faced less risk as it lacked tenant boundaries, though permission escalation within administrative roles was possible. Mitigation measures were swiftly implemented, with YouTrack Cloud patched within 48 hours and YouTrack Server fixes included from version 2025.3.132953 onwards. Administrators of YouTrack Server running older versions are advised to upgrade promptly, though there is no evidence of the vulnerability being exploited. Users can find detailed security updates and subscribe to notifications on JetBrains' website.

Efforts to improve the UI responsiveness of IntelliJ-based IDEs have been ongoing for several years, aiming to address performance issues tied to the platform's architectural constraints. The initiative focuses on moving performance-sensitive tasks away from the UI thread to minimize the time it holds the write lock, which has been a significant source of UI freezes due to its entanglement with read actions. This transition, which began in 2019, involves implementing background write actions without disrupting existing plugin compatibility or the modal dialog functionality within the IDE. Despite challenges such as the need for a new lock mechanism and maintaining compatibility with legacy code, the project has seen progress, notably in reducing the time the event dispatch thread (EDT) spends executing write actions. The effort continues with further plans to remove lock acquisition from the EDT to enhance the responsiveness of common user interactions while maintaining consistency and compatibility across the platform.

Sky’s the Limit Hackathon, co-hosted by JetBrains and Cloud9, successfully merged the developer and esports communities, attracting 2,293 global participants and resulting in 180 innovative project submissions. The event aimed to leverage the strategic and data-rich nature of esports alongside developers' problem-solving skills to create new tools and experiences, with participants receiving access to JetBrains’ AI tools to assist in their projects. Among the standout submissions were C9 StratOS, an assistant coaching platform; Spector, an automated scouting report generator; Cloud9 Draft Assistant, an AI-driven draft predictor; and Junie’s Arcade, a fan engagement game. The winners were celebrated at the Game Developers Conference (GDC) in San Francisco, where they showcased their projects and engaged with the broader game development community. The hackathon highlighted the potential for collaboration between software development and esports, emphasizing creativity and innovation as key drivers of success.

Koog Integration for Spring AI enhances agent orchestration by providing a robust runtime layer that complements the existing Spring AI functionalities, which include integration with multiple LLM providers, vector databases, and chat memory backends. Koog introduces advanced features such as multi-step workflows, persistent execution, sophisticated history management for cost optimization, and automated deterministic planning, allowing for better control and reliability as agent complexity grows. It seamlessly integrates with Spring AI, enabling users to maintain their current setup while benefiting from Koog's powerful orchestration capabilities, such as controllable type-safe workflows, persistence for durable execution, and history compression to manage LLM costs and context window limits. This integration allows users to expand their existing Spring AI applications without significant configuration changes, offering a streamlined process for enhancing agent functionality and fault tolerance.

TeamCity 2025.11.4, the final bug-fix update for the 2025.11 On-Premises servers, addresses several issues, including Bitbucket Cloud connection problems due to Atlassian's API deprecation, unresolved output parameters, excessive Git operations, and VCS-related performance issues. This update, which includes performance and security enhancements, is particularly crucial for teams using Bitbucket Cloud connections in TeamCity, as it ensures better compatibility, faster and more stable builds, and enhanced security. The data format remains consistent with other 2025.11.x releases, allowing upgrades or downgrades within this series without backup and restoration. Users can upgrade through the automatic update feature, download from the JetBrains website, or pull the updated TeamCity Docker image.

AI coding assistants are increasingly integrated into developers' daily workflows, offering perceived benefits in productivity and efficiency, yet their long-term impact remains complex and nuanced. A study by the Human-AI Experience team analyzed two years of telemetry data from 800 developers using JetBrains IDEs, complemented by surveys and interviews, to explore how AI tools reshape workflows over time. The findings reveal a discrepancy between developers' perceptions and their actual behaviors, as telemetry data showed sustained increases in code production, editing, and context switching among AI users, despite developers often perceiving minimal change. While AI tools appear to enhance code reuse and productivity, their impact on code quality is less clear, with developers continuing to double-check AI-generated code. The study underscores the importance of mixed-method approaches to capture the subtle shifts in workflows that AI introduces, offering a deeper understanding of how these tools influence everyday development practices beyond initial impressions.

Qodana, a code quality platform by JetBrains, is now featured on Gartner Peer Insights, providing an opportunity for users to review and influence its development. This inclusion is significant for the platform's growth and allows users to contribute feedback on Qodana's performance in real-world workflows, aiding other developers and teams in evaluating code quality tools. Users are encouraged to share their experiences with Qodana, highlighting its integration into development workflows, successful features, areas for improvement, and comparisons with other tools. Reviews will guide product improvements and future roadmap decisions, ultimately aiding in creating a better product and helping other teams enhance their code quality.

Connecting an AI agent to business data can be approached in three main ways, each with its own trade-offs between speed and reliability. Direct access allows fast setup but requires constant verification due to its reliance on the agent's ability to interpret data correctly. Building a formal semantic layer offers reliability by using predefined metrics for queries, but it requires significant time and effort to establish and maintain. An automated semantic layer seeks a balance by generating metrics from existing data projects and incorporating human review, allowing for scalable usage without sacrificing consistency. The choice of method depends on a business's current needs, with speed being more critical during exploration and consistency becoming paramount as usage scales. Databao offers a solution by integrating automated semantic layer generation with human validation, aiming to provide trusted self-service analytics for business users.

JetBrains has focused on optimizing the interactive rebase process in its IDEs by implementing in-memory operations to improve speed, particularly for large repositories where traditional methods became slow. This optimization was developed into an internship project and involves a technical deep dive into Git's internal mechanisms, like blobs, trees, and commits, enabling faster commit-history editing without impacting the working tree or index unnecessarily. The new approach uses low-level Git commands to rebuild commit sequences in memory, significantly reducing execution times from tens of seconds to just a few seconds on average, despite some remaining challenges on different operating systems. The improvements, which have been tested internally and in early access program builds, are set to be enabled by default in the 2026.1 release, making operations like rewording, dropping, or squashing commits more efficient. The project drew inspiration from community contributions and other Git performance projects and is expected to enhance the user experience by making commit editing faster and less disruptive.

In Cheuk Ting Ho's exploration of learning Python using PyCharm, the discussion centers around customizing the use of AI-powered features within the IDE to better suit both professional developers and beginners. While these features, such as local and cloud-based code completion, can significantly speed up coding by predicting and suggesting lines of code, they may hinder learning for newcomers by encouraging reliance on automated suggestions rather than understanding code functionality. The text advises beginners to consider disabling these AI features to foster a deeper comprehension of Python, emphasizing the importance of engaging with the code actively rather than passively accepting suggestions. It highlights the balance between utilizing advanced tools to streamline workflows and ensuring that foundational programming skills are developed through practice and problem-solving.

Valerii Tepliakov shares his journey of transitioning to an agent-first development approach using Air, a JetBrains’ agentic development environment. Initially hesitant to use AI tools for coding in work environments due to concerns over code quality and cumbersome interfaces, Tepliakov found Air's seamless integration into familiar workflows refreshing. Unlike previous experiences with AI tools like Claude Code, Air allowed him to switch easily between tasks, leveraging agents for small-scope tasks without the need to learn new interaction patterns. This shift enabled him to maintain productivity while delegating routine coding tasks to AI agents, reserving more complex tasks for traditional tools like IntelliJ IDEA. His experience culminated in successfully using Air to implement a Gemini CLI, demonstrating the efficiency of working within a single, integrated environment. Tepliakov anticipates further enhancements, such as mobile compatibility, to fully realize the potential of AI-driven development.

Maxim Kartashev's exploration of rendering a pixel in Java on a Wayland compositor system delves into the complex journey from Java code to screen display. The process begins at the high-level frameworks of Java's AWT or Swing, navigating through the Java 2D graphics pipeline, which involves handling color models, transformations, and rendering hints. A pixel's journey continues as it is embedded within shared memory buffers, moving through various layers until it reaches the Wayland compositor and becomes visible on the screen. The text highlights the intricacies of Java's rendering pipeline, including the use of SurfaceData for bridging Java and native windowing systems, the importance of color models and gamma correction for accurate color representation, and the role of double-buffering in Swing for efficient rendering. Additionally, it discusses the challenges of coordinate transformations and damage tracking in the Wayland protocol, emphasizing the need for optimized buffer management to maintain responsiveness and visual fidelity in Java applications on Linux systems.

The article explores the integration of Spring Data JDBC with Kotlin for building efficient and predictable persistence layers, highlighting Kotlin's advantages such as immutability, null safety, and concise syntax. It explains how Spring Data JDBC focuses on aggregate roots and constructor-based mapping, which aligns well with Kotlin's data class structure, reducing boilerplate code and enhancing reliability. The text provides examples of defining aggregates, custom queries, and handling one-to-many relationships using Kotlin's value and data classes, demonstrating how these features streamline database interactions by allowing explicit data flows and full SQL control. It also discusses the benefits of using Kotlin for maintaining clear and maintainable code, especially in transactional contexts, and concludes by emphasizing the simplicity and scalability of combining Spring Data JDBC with Kotlin for growing applications.

The upcoming webinar, "OSS Power-Ups: XenoAtom.Terminal.UI," presented by Alexandre Mutel, will explore the development of a modern terminal UI within the .NET ecosystem, focusing on design choices that foster a reactive, maintainable, and scalable framework. Scheduled for April 16, 2026, this session is part of a series spotlighting open-source .NET projects. Mutel will discuss the use of AI coding agents to enhance the development process, including aspects like API exploration, implementation, and testing, while sharing insights on what worked and what didn't. The webinar will also provide a glimpse into Mutel's broader open-source endeavors and how AI is influencing his project selection and development strategies. Alexandre Mutel, VP of Engineering at DataGalaxy, is renowned for his contributions to several popular open-source projects and is recognized for his innovative approach to .NET technologies.

dotInsights by JetBrains offers a comprehensive overview of recent developments and insights in the .NET and software development communities, including articles and discussions on topics such as AI's impact on development workflows, testing myths, UX tips for .NET MAUI developers, and the intricacies of C# programming. The newsletter highlights various contributions from developers like Emily Bache, Laurent Kempé, and Kevin Gosse, covering a range of technical topics from LINQ to XML, containerizing apps with Docker, and the challenges of software craftsmanship in the AI era. Additionally, it features updates on JetBrains products like the dotUltimate 2026.1 release, ReSharper's expansion to Visual Studio Code, and new features in Rider 2026.1, emphasizing enhancements in AI integration and performance monitoring.

Jacob Lee, a founding software engineer at LangChain, shares his experience of developing a custom coding agent using Deep Agents and the Agent Client Protocol (ACP) to better align with his workflow preferences. While initially a small project during his paternity leave, the endeavor evolved into a significant enhancement of his daily software development processes, effectively replacing his previous tool, Claude Code. By integrating the agent with JetBrains IDEs, Lee achieved seamless code editing and agent interaction, leveraging features like human-in-the-loop support, observability through LangSmith tracing, and the ability to switch between different AI models such as OpenAI's GPT-5.4 and open-source alternatives like GLM-5. This project not only improved his productivity but also provided valuable insights into the inner workings of AI agents, demonstrating that developers can customize and control their workflows for better efficiency and understanding.

Junie CLI, an AI coding assistant by JetBrains, now offers integration with JetBrains IDEs, leveraging the full code intelligence of the IDE without requiring manual setup. This connection allows Junie CLI to utilize the IDE's indexing, semantic analysis, and pre-configured build and test settings, enhancing its ability to handle complex projects and monorepos with precision. Unlike standalone AI coding agents that operate in isolation and often misinterpret project contexts, Junie CLI comprehends and navigates code by mirroring the user's current work environment, ensuring accurate refactoring and test execution. The integration is seamless for JetBrains AI subscribers and supports both automatic authentication and Bring Your Own Key options for other AI platforms. Currently in Beta, Junie CLI's capabilities continue to expand, inviting user feedback to shape its development.

Evgenia Verbina's guest post by Iulia Feroli provides a beginner-friendly guide to training a TensorFlow model using PyCharm, showcasing the framework's capabilities in transforming raw data into deployable machine learning models. The tutorial focuses on loading and visualizing the Fashion MNIST dataset, building and training two simple Keras models, and evaluating their performance, with PyCharm's features aiding the process through code completion and visualization tools. It highlights TensorFlow's versatility in handling data pipelines, from preprocessing to deployment on edge devices, and encourages experimentation with additional techniques like CNNs, dropout, and data augmentation. The post aims at demystifying TensorFlow for newcomers by offering a practical, hands-on approach with an emphasis on understanding the trade-offs in model accuracy and complexity, and it also advocates for utilizing PyCharm's functionalities to streamline the development workflow.

Artificial Intelligence has significantly transformed product development and business operations, yet it simultaneously introduces new cybersecurity challenges, particularly in the realm of adversarial AI. Adversarial AI encompasses malicious techniques designed to exploit and manipulate AI systems, posing significant threats to their integrity, reliability, and security. These threats are categorized into two main areas: AI used as a weapon, which includes deepfake generation and AI-generated malware, and direct attacks on AI systems, such as data poisoning, evasion attacks, and model theft. These attacks can lead to severe business repercussions, including financial losses, reputational damage, and loss of customer trust, highlighting the necessity for robust security measures. Organizations must focus on securing AI algorithms, generative AI filters, and the AI supply chain to mitigate these risks, employing strategies that go beyond traditional application security to protect AI-driven decisions and maintain competitive advantage.

In the April 2026 edition of Java Annotated Monthly, the spotlight is on the release of Java 26, with contributions from Java Champion Marit van Dijk. This edition provides a comprehensive overview of the new features and updates in Java 26, as well as insights from the JavaOne conference, including discussions about 25 years of IntelliJ IDEA and the introduction of JetBrains' Koog AI agent to Java. The newsletter also covers developments in Kotlin, AI advancements, Spring updates, and highlights from various tech conferences, offering readers an extensive roundup of industry trends and community engagements. Additionally, it explores broader topics such as mindful leadership in the AI era, cultural alignment in tech hiring, and the environmental impact of AI, encouraging readers to reflect on the evolving landscape of software development.

RustRover 2026.1 enhances the developer experience by integrating native support for cargo-nextest, a faster and more scalable test runner, directly into the IDE. This integration allows developers to run Nextest sessions with detailed progress reporting and structured results without leaving the IDE, thereby improving testing efficiency in large Rust workspaces. Rust's standard testing framework, which uses the #[test] attribute and the cargo test command, is complemented by cargo-nextest, which executes tests in parallel and identifies flaky tests, offering a more robust testing solution. RustRover adapts its test runner to work seamlessly with cargo-nextest, maintaining familiar UI elements like gutter icons and a hierarchical tree view for test results. This integration ensures that developers can leverage Nextest's speed and reliability while maintaining their existing workflow within the RustRover environment.

Lena Reinhard, a seasoned tech leader and keynote speaker at KotlinConf’26, addresses the evolving uncertainties in the tech industry, particularly in light of the rise of AI and its impact on productivity and career stability. With over 20 years of experience, including roles at CircleCI and Travis CI, Reinhard discusses the shifting landscape of the tech industry, the questioning of long-held promises of opportunity and growth, and the challenges leaders face in supporting their teams through these changes. She emphasizes the importance of understanding what productivity truly means in an era where AI could either aid or complicate workflows and highlights the need for open communication and realistic goal-setting amid the hype surrounding AI technologies. Reinhard also notes the pressures that tech professionals now face, such as the expectation to integrate AI into their work, leading to increased workloads. Her insights underscore the importance of leaders acknowledging uncertainties and fostering environments where open discussions about goals and productivity can occur, rather than succumbing to the pressure of rapid output without meaningful progress.

Recent large-scale surveys reveal that AI coding tools are increasingly being used by developers globally, with approximately 90% of them regularly employing at least one AI tool in their work by January 2026. GitHub Copilot remains the most recognized and widely adopted tool, although its growth has plateaued, while Claude Code has seen significant increases in both awareness and adoption, particularly in North America, driven by high satisfaction and loyalty metrics. Meanwhile, OpenAI's Codex has struggled to gain traction compared to its peers, and Google's recently launched Antigravity editor has quickly captured a 6% adoption rate. The surveys underscore a trend towards "best-of-breed" agents that prioritize performance over platform integration, with developers preferring standalone tools that excel in specific tasks. JetBrains is positioning itself for this agentic future by integrating various AI agents into its IDEs and creating an open ecosystem that allows developers to choose the best agents for their needs, while also launching new platforms like Air and Junie CLI that facilitate agentic development. The data collected highlights the rapid evolution of developers' toolkits and the challenges of adopting AI tools at an organizational level, with further insights expected from the upcoming Developer Ecosystem Survey 2026.

In a guest post by John Arundel, Go writer and teacher, readers are taken on a whimsical journey through using JetBrains' AI coding agent, Junie, within the GoLand development environment, using a pirate-themed approach called "Code like a PIRATE." The narrative uses playful pirate language to outline a six-step workflow designed to optimize productivity with AI development tools, focusing on planning, iterating, reviewing, assessing, testing, and evaluating. Arundel emphasizes the importance of guiding Junie with clear directives to avoid coding errors and inefficiencies, suggesting practical adjustments and improvements to the code throughout. The article concludes with a light-hearted reminder to enjoy the coding process responsibly, reinforcing the educational and entertaining nature of this AI-guided programming adventure.

JetBrains has announced that RSS feed support for their blog and product blogs is now generally available, following extensive development and testing across 47 RSS readers on six platforms. The RSS feeds provide full-text articles and plain-text summaries, allowing users to subscribe to individual product blogs or a combined feed. They have ensured compatibility with RSS 2.0 specifications, and readers can import all feeds using an OPML file. The feed updates in real-time with conditional GET support, and JetBrains has made the service freely available without requiring a JetBrains account. Additionally, JetBrains is considering future support for Atom and JSON Feed formats based on user feedback.

YouTrack 2026.1 introduces Whiteboards, a new feature designed to enhance project planning, brainstorming, and collaboration by allowing teams to visualize and manage their projects more effectively. This functionality extends YouTrack’s capabilities by enabling users to connect existing projects, import ongoing tasks, and transform ideas into actionable tasks or articles. The update also includes streamlined project access management through a new People tab, a full-page notification center for improved communication, and integrations with AI tools like n8n and remote Model Context Protocol (MCP) server actions to enhance workflow automation. Additionally, the YouTrack app ecosystem has grown, offering new apps for project managers, QA, DevOps, and support teams to further customize and optimize user experience. Enhancements in search accuracy for Knowledge Base articles and various design updates have been implemented to improve overall functionality, while users can explore these new features through YouTrack’s free trial or subscription options.