Posts tagged with culture
Why it matters: This article provides actionable insights for developers to leverage GitHub Copilot's custom agents effectively. By following these best practices, engineers can create highly specialized AI assistants that improve productivity and code quality, streamlining development workflows.
- •GitHub Copilot's new `agents.md` feature enables custom, specialized AI assistants for tasks like documentation or testing.
- •Successful `agents.md` files are highly specific, defining clear personas, executable commands, explicit boundaries, and concrete code examples.
- •Analysis of over 2,500 repositories reveals best practices: place commands early, use code snippets for style, set strict "never do" rules, and detail the exact tech stack.
- •Effective agent definitions should cover six core areas: commands, testing, project structure, code style, Git workflow, and boundaries.
- •Begin by creating agents for simple, specific tasks (e.g., linting, doc generation) with a clear name, description, and persona.
- •Copilot can help generate initial `agent.md` files, which users then customize with project-specific details and YAML frontmatter.
Why it matters: This toolkit empowers engineers by providing clear design intent and accessibility documentation directly in Figma, drastically reducing guesswork and preventing common accessibility bugs. It streamlines the design-to-code handoff, leading to more efficient development and higher quality products.
- •GitHub's open-source Annotation Toolkit is a Figma library designed to streamline design-to-code collaboration and improve accessibility documentation.
- •It allows designers to embed design intent and accessibility behaviors (e.g., responsive reflow, table handling) directly into design files using numbered annotations.
- •The toolkit was developed by GitHub's accessibility team after realizing nearly half of audit issues could be prevented with better upfront design intent documentation.
- •It integrates WCAG guidelines into the design workflow, ensuring accessibility is considered from the start, not as an afterthought.
- •This approach fosters clarity, consistency across teams, and enables preventative QA, reducing bugs and knowledge loss.
- •The toolkit is available via Figma Community or its GitHub repository, offering tutorials and guidance for implementation.
Why it matters: This article provides essential guidance for engineers to master Copilot Code Review instruction files, enabling more effective and consistent automated code reviews tailored to project standards. It helps optimize AI-assisted development workflows.
- •Copilot Code Review (CCR) leverages copilot-instructions.md and path-specific *.instructions.md files for customizable automated code reviews.
- •Instructions should be concise, structured, direct, and include code examples to effectively guide Copilot's review process.
- •Use repo-wide copilot-instructions.md for general standards and path-specific *.instructions.md with applyTo for language or topic-specific rules.
- •Avoid instructions that attempt to alter Copilot's UX, modify PR overviews, request non-review tasks, include external links, or make vague improvement demands.
- •A structured approach, including clear titles, purpose, naming, style, and code examples, is recommended for effective instruction files.
Why it matters: AI is reshaping software development by influencing language choices and developer roles. Typed languages gain traction due to AI compatibility, while "duct tape" languages become more usable. This impacts enterprise adoption and redefines developer skill sets.
- •AI is fundamentally changing language adoption, not just developer productivity, by influencing what tools developers choose to build with.
- •TypeScript's surge in popularity is attributed to its static typing, which provides guardrails for AI-generated code, reducing errors and improving model performance.
- •Language selection now includes "AI-compatibility" as a critical factor, favoring languages where AI models perform best due to extensive training data.
- •AI makes "duct tape" languages like Bash more tolerable, enabling developers to use the right tool for the job without manual drudgery.
- •Enterprises are seeing AI shift developer roles, with juniors shipping faster and seniors focusing on architecture and validation rather than writing boilerplate.
Why it matters: This report details Microsoft's extensive security advancements, showcasing industry-leading practices, new tools, and a security-first culture. Engineers can learn from these strategies to enhance their own systems and development processes.
- •Microsoft's Secure Future Initiative (SFI) is a massive cybersecurity effort, improving platforms, services, and threat response across its ecosystem.
- •Engineering sentiment for security has risen, supported by extensive training on AI-powered cyberattacks and expanded governance.
- •Azure, Microsoft 365, Windows, and Surface introduced innovations like secure defaults, AI Administrator roles, and Zero Trust principles.
- •Significant engineering progress includes 99.6% phishing-resistant MFA, secure virtual desktop migrations, and 99.5% live secret detection in code.
- •Microsoft is evolving Sentinel into an AI-first platform and offering SFI-based guidance and Zero Trust Workshops for customers.
- •The initiative leverages 35,000 engineers, prioritizing risks, accelerating security innovations, and using AI for efficiency and rapid anomaly detection.
Why it matters: The developer workflow is rapidly evolving towards faster iteration and continuous delivery. Understanding these shifts in practices, tools like feature flags and CI/CD, and communication styles is crucial for engineers to remain effective and competitive.
- •Developer workflows are rapidly shifting towards continuous iteration, favoring smaller, more frequent commits over large, infrequent releases.
- •Modern software delivery heavily relies on feature flags for safely deploying incomplete work and automated CI/CD pipelines for testing, building, and deployment.
- •The industry is moving towards smaller, focused pull requests, which are easier and faster to review, thereby reducing mental overhead and risk.
- •Comprehensive automated testing, including unit, integration, and end-to-end tests, is becoming increasingly essential to maintain quality and momentum in accelerated development cycles.
- •Team communication and hiring practices are evolving to support faster shipping, emphasizing async updates, issue-based status, and clear communication skills.
Why it matters: This tool significantly boosts developer productivity by integrating AI directly into the terminal, reducing context switching and automating complex tasks. It empowers engineers to work faster and more efficiently within their preferred command-line environment.
- •GitHub Copilot CLI integrates AI capabilities directly into the command-line interface, enabling users to generate, explain, and execute commands without leaving the terminal.
- •It streamlines developer workflows by allowing natural language interaction to create scripts, refactor code, and manage environments, enhancing precision and control.
- •The CLI operates in both interactive and programmatic modes, consistently requiring user confirmation before modifying or executing files for enhanced safety.
- •Users can extend Copilot CLI's functionality by connecting to custom MCP servers, integrating domain-specific tools and contextual data for improved suggestions.
- •The tool aims to automate repetitive tasks, facilitate learning new tools, and significantly reduce context switching for developers who prefer working in the terminal.
Why it matters: Automating routine maintenance at scale reduces developer toil and technical debt. Spotify's success with 1,500+ merged PRs proves that AI agents can reliably handle complex code modifications, allowing engineers to focus on innovation rather than manual upkeep.
- •Spotify developed an AI-driven background coding agent to automate large-scale software maintenance tasks.
- •The agent has successfully merged over 1,500 pull requests, proving the scalability of AI-generated code changes.
- •It focuses on reducing developer toil by handling repetitive tasks like dependency updates and migrations.
- •The system operates autonomously to identify and resolve technical debt across a massive codebase.
- •This initiative shifts the engineering focus from routine upkeep to high-value feature development.
Why it matters: TypeScript's journey from a pragmatic fix to GitHub's most-used language underscores its value in building scalable, maintainable systems. Its type safety and tooling are now essential for modern frontend development and increasingly vital for reliable AI-assisted coding.
- •TypeScript, created by Anders Hejlsberg, addressed JavaScript's scalability challenges for large codebases by adding static typing and tooling.
- •It became GitHub's most-used language in 2025, demonstrating significant adoption and a 66% increase in contributors.
- •Its 'superpowers' of type safety, improved IDE support, and refactorability made it the default for major frontend frameworks.
- •The compiler was rewritten in Go, achieving a 10X performance boost while preserving functional compatibility.
- •TypeScript's open-source evolution on GitHub provides a transparent history of its development.
- •Its typed nature is crucial for the AI era, making AI-assisted coding more reliable and maintainable.
Why it matters: This article demonstrates how applying core software engineering principles like caching and parallelization to build systems can drastically improve developer experience and delivery speed, transforming slow pipelines into agile ones.
- •Slack reduced 60-minute build times for Quip and Slack Canvas backend by applying software engineering principles to their build pipeline.
- •They leveraged modern tooling like Bazel and modeled builds as directed acyclic graphs (DAGs) to identify optimization opportunities.
- •Key strategies included caching (doing less work) and parallelization (sharing the load) to improve build performance.
- •Effective caching relies on hermetic, idempotent units of work and granular cache keys for high hit rates.
- •Parallelization requires well-defined inputs/outputs and robust handling of work completion/failure across compute boundaries.