Why it matters: This article details advanced techniques in training AI for developer tools, showcasing how custom data collection, SFT, and RL overcome challenges in real-time code prediction. It's crucial for engineers building AI-powered developer experiences and understanding practical LLM deployment.
- •GitHub Copilot's Next Edit Suggestions (NES) uses a custom, low-latency model designed to predict developers' next code edits in real time.
- •Initial attempts with general LLMs and pull request data failed; a custom, high-quality dataset derived from real-time editing sessions was crucial for training.
- •The foundational NES model was developed using Supervised Fine-Tuning (SFT) on this specialized dataset.
- •Reinforcement Learning (RL), incorporating a custom 'grader' model, further refined the NES model, addressing SFT limitations by leveraging unlabeled data and explicitly defining criteria for 'bad' suggestions.
- •This 'AI-native' approach emphasizes end-to-end co-design of model training, prompt engineering, and user experience for seamless IDE integration.
- •Recent improvements focus on prompt optimization to reduce latency and enhance the relevance and quality of suggestions.
Why it matters: This article details how Meta scaled a critical security feature, Key Transparency, to Messenger's massive user base. Engineers can learn about distributed system challenges, cryptographic key management, and infrastructure resilience for high-volume, security-sensitive applications.
- •Messenger launched Key Transparency for end-to-end encrypted chats, providing verifiable and auditable public key records to prevent tampering.
- •This feature automates the verification of encryption keys, addressing the complexity of manual checks for users with multiple devices and frequent key changes.
- •The implementation leverages the Auditable Key Directory (AKD) library and integrates Cloudflare's key transparency auditor for enhanced security.
- •Scaling challenges included managing billions of key entries and hundreds of thousands of updates per 2-minute epoch due to Messenger's multi-device user base.
- •Engineering advancements involved optimizing AKD algorithmic efficiency for smaller proof sizes and improving infrastructure resilience and recovery processes.
Why it matters: Optimizing tool selection for LLM agents significantly boosts performance and reliability. This approach reduces latency and improves success rates for AI assistants like GitHub Copilot, making them faster and more effective for developers.
- •GitHub Copilot Chat's performance was hindered by reasoning across hundreds of tools via the Model Context Protocol (MCP).
- •New systems, embedding-guided tool routing and adaptive tool clustering, were developed to optimize tool selection for LLM agents.
- •The default toolset was reduced from 40 to 13 core tools, and 'virtual tools' were introduced to functionally group similar tools.
- •Adaptive tool clustering uses Copilot's internal embedding model and cosine similarity to efficiently group tools, replacing slower LLM-based categorization.
- •Embedding-guided tool routing pre-selects the most semantically relevant tools based on query embeddings, reducing unnecessary exploratory calls.
- •These optimizations improved success rates by 2-5 percentage points on benchmarks and reduced response latency by an average of 400 milliseconds.
Why it matters: Ensuring mobile accessibility is critical for legal compliance and inclusive user experiences. This post provides practical implementation details for common Android a11y hurdles, like custom actions and semantic announcements, helping engineers build more robust, accessible apps.
- •Slack conducted a VPAT audit in 2024 to identify and fix accessibility (a11y) gaps in their Android app following a major UI redesign.
- •Improved error communication by updating OutlinedTextField and SKBanner to trigger TalkBack announcements when validation fails.
- •Enhanced navigation by explicitly marking headings using semantics { heading() } in Jetpack Compose and accessibilityHeading in XML.
- •Resolved list count inaccuracies for screen readers by implementing CollectionInfo and CollectionItemInfo semantics.
- •Implemented CustomAccessibilityAction to make drag-and-drop functionality in the workspace switcher accessible to non-visual users.
- •Utilized TtsSpan to ensure screen readers announce text formatting like strikethrough, which is otherwise ignored by default.
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: Engineers can leverage Ax, an open-source ML-driven platform, to efficiently optimize complex systems like AI models and infrastructure. It streamlines experimentation, reduces resource costs, and provides deep insights into system behavior, accelerating development and deployment.
- •Ax 1.0 is an open-source adaptive experimentation platform leveraging machine learning for efficient optimization of complex systems.
- •It's widely used at Meta to improve AI models, tune production infrastructure, and accelerate advances in ML and hardware design.
- •The platform employs Bayesian optimization to guide resource-intensive experiments, identifying optimal configurations efficiently.
- •Ax provides advanced analytical tools, including Pareto frontiers and sensitivity analysis, for deeper system understanding beyond just finding optimal settings.
- •An accompanying paper details Ax's core architecture, methodology, and performance comparison against other black-box optimization libraries.
Why it matters: Azure's new AI-powered Copilot agents and enhanced infrastructure promise to automate complex cloud operations, significantly reducing manual effort and allowing engineers to focus on innovation and architecture rather than routine administration.
- •Azure introduces Copilot agents to automate complex cloud operations, including migration, deployment, optimization, observability, resiliency, and troubleshooting.
- •Azure Copilot provides an agentic interface for cloud management, integrating with existing governance, RBAC, and policy frameworks for secure and compliant operations.
- •Azure is significantly enhancing its global AI infrastructure with increased capacity, resilience, optimized datacenter design, and network topology for AI-scale workloads.
- •Key infrastructure modernizations include new systems like Azure Cobalt and Azure Boost, AKS Automatic, and Azure HorizonDB for PostgreSQL, supporting diverse workloads.
- •The initiative aims to free up engineering teams from repetitive tasks, allowing them to focus on architecture and innovation by embedding AI agents directly into the platform.
Why it matters: This article highlights Microsoft's push for a unified, AI-powered data estate. Engineers gain access to new, integrated database solutions like SQL Server 2025 and Azure DocumentDB, simplifying data management and accelerating AI development across hybrid and multi-cloud environments.
- •Microsoft announced the general availability of SQL Server 2025, Azure DocumentDB, and SQL/Cosmos DB in Fabric, alongside a preview of Azure HorizonDB (PostgreSQL).
- •Microsoft Fabric serves as a unified hub, integrating these new database offerings for a cohesive, AI-ready data estate.
- •SQL Server 2025 introduces developer-first AI capabilities like smarter search and AI model management, enhanced reliability, and security features.
- •SQL Server 2025 data is instantly accessible in Microsoft OneLake through mirroring for Fabric, supporting AI and analytics workloads.
- •Azure DocumentDB is a new MongoDB-compatible, AI-ready service designed for hybrid and multi-cloud environments.
Why it matters: This article highlights Azure's comprehensive AI-first platform, offering engineers new tools for building, securing, and scaling intelligent applications and data solutions, enhancing productivity and innovation across various domains.
- •Azure at Ignite 2025 unifies AI, data, apps, and infrastructure to deliver intelligence at scale, addressing key business questions on AI adoption and data readiness.
- •New AI agent capabilities include Microsoft Fabric IQ, Foundry IQ, and Microsoft Agent Factory, simplifying the creation and scaling of intelligent applications.
- •Significant data modernization updates feature SAP BDC Connect for Fabric, Azure HorizonDB (PostgreSQL), Azure DocumentDB, and SQL Server 2025 for enhanced data management.
- •Operations and security are boosted with AI-powered tools like Foundry Control Plane, Azure Copilot with built-in agents, and native DevSecOps integration for Defender for Cloud and GitHub Advanced Security.
- •AI-ready infrastructure is introduced with Azure Boost for speed and security, and Azure Cobalt 200, redefining performance for the agentic era.
- •Microsoft Foundry expands its model choice by adding Anthropic Claude (Sonnet 4.5, Opus 4.1, Haiku 4.5) and Cohere models, making Azure the only cloud offering both OpenAI and Anthropic models.