A recent announcement by IBM and HashiCorp means future Terraform releases will end support for defining infrastructure using third-party programming languages such as Python or Ruby, leaving HashiCorp Configuration Language as the only supported option. This change shifts Terraform towards a narrower focus on its core language and reduces the burden of maintaining multiple language ecosystems, but it also forces teams that adopted Terraform for its language flexibility to plan migrations, retrain staff and manage the operational risk of changing established workflows. The change particularly affects the Cloud Development Kit for Terraform, with vendor support ending even if the project remains available under an open licence and may be sustained by the community. In response, organisations must weigh the effort of moving to HCL against considering other infrastructure as code tools that retain multi-language support, while recognising that any transition will take time and careful planning.
After the latest update to Visual Studio Code, daily users are likely to notice a stronger emphasis on AI agents, a preview of TypeScript 7 and the withdrawal of IntelliCode. The update introduces the loosely defined Agent HQ initiative and an Agent Sessions view for monitoring multiple background agents, although this view has been disabled by default and folded into Chat, creating confusion about what is new now and what is merely a longer-term direction. Alongside practical additions such as keeping agents running when Chat is closed, moving sessions between local and cloud environments and supporting custom subagents, the changes raise sharper security questions, including the risks of prompt injection and the presence of a YOLO setting that removes approval safeguards. TypeScript 7 is presented as a serious performance step forward through a Go rewrite of the compiler and language service, while the deprecation of the free, local IntelliCode completion feature pushes users towards GitHub Copilot with monthly free limits and paid subscriptions thereafter, leaving teams to weigh costs, workflow impact and security before enabling new capabilities.
The October 2025 release of Visual Studio Code introduces major enhancements across agent management, security, and the overall editor experience. The new Agent Sessions view centralises local and cloud agent activities, with improved options for organising, searching, and delegating tasks to coding agents such as Copilot and OpenAI Codex. Developers benefit from a new planning agent, refined cloud and CLI agent integrations, as well as easier session tracking and custom agent configuration. Code editing sees improvements like selectable deleted code in diffs, the open-sourcing of inline suggestions, expanded navigation features, and enhanced iconography. Additional updates include advanced settings options, better command palette search, the introduction of Terminal IntelliSense for command completion, richer diagnostic copying, and easier management of authentication and language models. The update brings increased accessibility features, notebook search, new source control capabilities such as folding in commit messages and improved branch/tag management, and more robust testing navigation tools. Python development is refined with improved environment management, AI-powered documentation insertion, and new code actions. Extensions receive more authoring capabilities, including new authentication options and richer UI elements. Numerous issues and bugs have been addressed by community contributors, and support for macOS 11.0 ends with this release.
Posit has announced new tools and integrations designed to streamline data science workflows for enterprise teams. The company is introducing Positron, a new integrated development environment built on VS Code that supports both R and Python whilst enabling data exploration, model building and application deployment within a unified interface. Posit AI Agents provide automated assistance through Positron Assistant for coding tasks and Databot for natural language data queries, maintaining transparency and reproducibility. The platform now integrates directly with Snowflake and Databricks, allowing teams to develop and deploy applications without leaving these environments whilst leveraging their native governance structures and AI models. Chronicle Usage Metrics helps organisations track how data science resources are being utilised, monitor licence consumption and plan for future capacity needs. Enhanced auditing capabilities provide detailed logging of user activities, flag packages with known security vulnerabilities, and enable better tracking of potential exposure risks. These updates aim to address common challenges in enterprise data science by providing connected tools, consistent governance and seamless integration with existing data infrastructure.
SAS and R are both statistical programming languages, though SAS has long been part of many organisations' legacy software systems, whilst R offers innovative reporting and automation capabilities that are increasingly valued in sectors such as pharmaceuticals and finance. Many recent graduates arrive in the workplace already familiar with R from their university studies. Although translation guides exist to help programmers move between the two languages, learning R properly from foundational principles enables users to fully exploit its potential rather than simply replicating SAS workflows. The transition involves becoming comfortable with RStudio as a development environment, understanding how to import data through packages like readr and haven, adopting the tidyverse collection of packages for data manipulation and visualisation, and learning to create reusable functions and packages instead of relying on macro libraries. R Markdown and Quarto serve similar purposes to SAS ODS for producing shareable outputs, whilst the tidymodels collection provides statistical modelling capabilities and Shiny enables the creation of interactive applications for sharing analytical results with collaborators who may not themselves programme in R.
Claude Code is a command-line tool designed for developers that allows direct interaction with large codebases through a terminal interface. It can rapidly search through millions of lines of code, provide detailed explanations of project structures and architectures, and automate complex development workflows that would traditionally require switching between multiple tools. The system integrates with popular development platforms like GitHub and GitLab, enabling users to handle complete development cycles from reading issues through to submitting pull requests without leaving the terminal. It offers intelligent code analysis that understands dependencies and project organisation, allowing it to perform sophisticated multi-file edits whilst maintaining code integrity.
MCP servers extend agent mode functionality in VS Code by providing specialised tools for various tasks including connecting to databases, invoking APIs and performing automated operations. The available servers span multiple categories, with developer tools offering integration with platforms like GitHub for repository management, Figma for design extraction, Playwright for browser automation and Sentry for error analysis. Productivity-focused servers connect to project management platforms such as Notion, Linear, Asana and Atlassian services, whilst also providing workflow automation through Zapier and task breakdown capabilities. Data and analytics servers enable interaction with databases like DuckDB, Neon Postgres and MongoDB, alongside analytics platforms including PostHog and Microsoft Clarity for gathering user behaviour insights. Business service integrations cover payment processing through Stripe, PayPal and Square, customer support via Intercom and site building through Wix and Webflow. Cloud and infrastructure servers provide management capabilities for Azure resources, DevOps operations, Terraform infrastructure as code and Convex backend services, creating a comprehensive ecosystem for development and business operations within the VS Code environment.
The SAS Developer Portal serves as a comprehensive platform enabling developers to integrate SAS artificial intelligence and analytics capabilities with open source technologies. The portal provides extensive REST API documentation across multiple categories including automated machine learning, cloud analytic services, data management, fraud detection, healthcare applications, and visualisation tools. Developers can access resources for integrating SAS with various programming languages such as Python, R, Lua, and Java, whilst also utilising software development kits and frameworks to embed SAS insights into custom applications and dashboards. The platform supports SAS Viya programming and CAS actions for data processing and analytics operations, complemented by tools like the Visual Studio Code extension. Key solutions include Customer Intelligence 360 for marketing automation, SAS Viya Workbench for cloud-based analytics, and SAS 360 Match for advertising delivery, all underpinned by trustworthy AI principles emphasising transparency and accountability. The portal connects developers through community forums and GitHub repositories containing code examples and collaborative resources.
This is a public GitHub repository that serves as a collaborative platform for sharing SAS Studio Custom Steps, which are user interface tools that enable SAS Studio users on SAS Viya platforms to complete specific tasks through standardised workflows. The repository contains over 100 custom steps spanning various categories including data quality operations, natural language processing, computer vision, machine learning, cloud integration, file management, and synthetic data generation. Contributors have developed tools for tasks such as data anonymisation, sentiment analysis, image processing, database connections, API interactions, and automated report generation. The repository operates under an Apache 2.0 licence and welcomes community contributions through established guidelines, providing comprehensive documentation and tutorials to help users implement these custom steps within their SAS Studio environments. Recent activity shows ongoing maintenance with regular updates to documentation, bug fixes, and the addition of new functionality, particularly in areas like large language model integration and advanced analytics workflows.
The SAS Airflow Provider is an Apache 2.0 licensed open-source package that enables users to execute SAS Studio Flows and Jobs within Apache Airflow workflows. The provider offers comprehensive functionality including the ability to run SAS Studio Flows stored on file systems or in SAS Content, selection of compute contexts for execution, optional return of SAS logs to Airflow, parameter specification for code generation, proper handling of return codes with exception raising for failed flows, and flexible authentication through either OAuth tokens or username-password combinations. Installation can be accomplished through PyPI or by building from the repository sources, whilst connections to SAS Viya require configuration through the Airflow interface with host, login credentials, and optional OAuth token specification in JSON format. The provider supports TLS verification settings and custom certificate bundles for secure connections, and includes example DAG's for implementation guidance, though users must first create SAS Studio Flows or Job Definitions before referencing them in their workflows.