Browse All Posts (130)

Allison announces an update to the GitHub Copilot usage metrics API that adds a new totals_by_client breakdown, letting enterprises and organizations see request, session, and token usage coming from tools outside the IDE (like OpenCode, Copilot CLI, VS Code, and JetBrains).

Visual Studio Code introduces a four-part video series focused on doing more PostgreSQL work directly inside VS Code, including connecting to databases, writing and optimizing SQL, using GitHub Copilot to understand database context, and troubleshooting performance, with Azure mentioned as the underlying platform for modern PostgreSQL workloads.

Allison announces an updated GitHub app for Microsoft Teams that combines GitHub notifications with GitHub Copilot-powered actions, letting teams respond to issue and pull request updates by mentioning @GitHub to investigate, fix bugs, run tests, and open pull requests from within Teams.

Microsoft Cloud outlines a “Physical AI” vision for industrial scenarios, describing how AI systems can perceive, reason, and act in the physical world across manufacturing, quality, and supply chains, and how organizations can move from simulation to real operations spanning edge and cloud.

Allison explains a GitHub Enterprise Managed Users (EMU) incident where SCIM deprovisioning incorrectly deleted accounts instead of suspending and obfuscating them, how to confirm impact via audit logs, and what GitHub changed to restore records and prevent recurrence.

Microsoft Security Research, Mahesh Mandava, Srinivasan Govindarajan, Sagar Patil and Suriyaraj Natarajan break down the LiteLLM 1.107.0 supply-chain compromise, showing how Python startup hooks enabled credential theft without importing LiteLLM, what data was targeted, and how to investigate and recover using Microsoft Defender detections and hunting queries.

GitHub demonstrates a hands-on workflow for going from a GitHub issue prompt to a deployed AI agent inside the GitHub Copilot app, using Azure AI Foundry tooling to provision resources, deploy a model, validate grounding to reduce hallucinations, and ship a secure proxy plus a website chat widget.

Allison announces new GitHub public monitoring capabilities for handling leaked secrets at enterprise scale, including alert triage workflows, continuous token validity checks, and integrations via a REST API and enterprise webhooks for incident response pipelines.

sbaynes introduces Microsoft research on evaluating step-by-step clinical diagnostic reasoning with SDBench and the Microsoft AI Diagnostic Orchestrator (MAI-DxO), including accuracy and cost results across NEJM and MIMIC-IV-ED cases and plans to open-source parts of the benchmark for reproducible evaluation.

Microsoft Developer introduces the idea of “adaptive” software that can reason and take actions, positioning it as a shift beyond apps that only respond to user input.

Allison announces general availability of enterprise-level installations for GitHub Apps, expanding API and permission coverage so enterprises can migrate more automation away from classic PATs and manage governance, security, and admin settings through GitHub Apps.

Allison summarizes the September 2026 update for GitHub Copilot in Visual Studio 2026, including PR exploration via the Git agent, preview BYOM support (with a breaking change to the new Agent harness), and a new flow to fix NuGet Audit vulnerabilities directly from the Error List.

Daniel Roth and Shriram Sankaran join the Blazor Community Standup to demo the free, open-source Syncfusion Toolkit for Blazor, showing the components in action and how to use them when building Blazor apps.

Allison announces an update to the Copilot usage metrics API that reports configured names for custom skills, custom agents, MCP servers, slash commands, and plugins (instead of grouping them as “other/custom”), making it easier for admins to track adoption and usage trends across an organization.

sbaynes introduces Flint-interactive, a language that helps AI agents generate interactive data visualizations (not just static charts) using high-level semantic events and update operators, so users can click, filter, zoom, and link interactions across charts, text, tables, and app state.

Allison announces general availability of a global default policy for GitHub Copilot features on Copilot Business and Copilot Enterprise, explaining how unconfigured settings will be enforced during the rollout and where admins can review and override defaults.

arindamc introduces Schema-aware Fabric Eventstreams (Preview), which lets Microsoft Fabric Real-Time Intelligence streams preserve event “shape” so teams can explore, classify, and process multiple event types in a single live stream without forcing everything into one wide schema.

Christoph Berlin explains how an AI agent can speed up root cause analysis by correlating data and reducing the dependency on tracking down subject-matter experts, helping engineers start investigations with better initial context.

Allison announces new GitHub Actions blob storage hosts that are now included in the GitHub meta API, with guidance for organizations that use network allowlists to avoid interruptions to job summaries and logs.

Allison announces a public preview feature for GitHub Enterprise Cloud with data residency that expands secret scanning to public commits on github.com, while keeping alert delivery and data handling within the customer’s residency region via verified domain matching.

Allison announces two GitHub pull request review improvements: a saved whitespace visibility preference that persists across pull requests, and a split-view change where new and deleted files use the full width to reduce wrapping and improve readability.

MichalBar introduces a preview feature in Microsoft Fabric Real-Time Dashboards that uses Copilot to generate purpose-built custom visuals. By describing an operational question in natural language, dashboard authors can have Copilot create both the KQL query and an HTML-based visualization directly as a new dashboard tile.

Allison announces a new “Industry comparison” view in the GitHub Copilot impact dashboard, letting opted-in enterprises benchmark agentic adoption and leverage against aggregated peer data, with controls for policy-based participation, daily refresh, and data residency boundaries.

Microsoft Developer walks through how developers (and AI coding agents) can build applications with Azure SQL, including a demo flow that starts from an Azure SQL DB container and moves into Azure SQL Database’s free tier, plus an overview of Microsoft SQL agent skills and plugin concepts.

Marko Hotti and Patty Chow show how to build an Azure Cosmos DB app using AI-assisted development, starting from a blank folder and a product spec to generate a working travel planner. They cover data modeling, container design, queries, and local testing using the Cosmos DB Linux emulator and GitHub Copilot in VS Code.

GitHub explains how GitHub Copilot approvals can help teams avoid routine pull request review bottlenecks by letting Copilot provide delegated review approvals that count toward merge protection rules, while still combining that automated assessment with a human approval to satisfy branch requirements.

Marko Hotti and Madeline Egan explain how to move an AI agent from a demo to a production-ready system using Azure Cosmos DB, focusing on observability and evaluation practices like tracing, regression testing, and before/after metrics to improve retrieval quality, latency, token usage, memory efficiency, and RU consumption.

Marko Hotti and Madeline Egan show how to build AI agents that keep durable, shared memory across sessions using Azure Cosmos DB, so a multi-agent travel planner can remember user preferences, share context between agents, and continue work after handoffs instead of restarting from scratch.

Marko Hotti and Patty Chow demonstrate how to build a smarter AI travel planner on Azure Cosmos DB by combining operational app data with vector embeddings and search features, so the app can answer nuanced queries while still respecting real inventory and booking workflows.

Sunip explains an enterprise Azure networking pattern that separates “where workloads live” from “what needs to be reachable,” using enterprise-routable prefixes for shared services while keeping workload VNets non-advertised and exposing only selected endpoints through a routable front door.

Microsoft Developer shows how to use Agent Optimizer in Azure AI Foundry to improve an agent when prompt tweaks aren’t reliably fixing behavior. The video walks through building an evaluation dataset, scoring a baseline, generating candidate instruction sets, and validating improvements (quality, latency, and safety) before shipping a new agent version.

Christoph Berlin explains how agentic AI can speed up root cause analysis by correlating signals across multiple systems, ranking likely causes by confidence, and presenting supporting evidence so engineers can start investigations faster after stoppages or slowdowns.

madhurinrao explains ABFS Vectored Read for Azure Data Lake Storage and how it reduces request overhead for selective Parquet reads by coordinating noncontiguous byte ranges through the ABFS driver, enabling compatible Spark workloads to adopt the optimization via configuration.

mukta introduces Enhanced Feature Flags (public preview) in Azure App Configuration, explaining how a dedicated resource model and API surface improve governance of feature rollouts. The post focuses on separating feature-flag permissions from general configuration, adding server-side validation, and keeping existing key-value feature flags supported.

poorvinarang explains how Azure Linux evolved from CBL-Mariner into Azure Linux 4.0, and what Microsoft changed to make it a supported, general-purpose Linux option for Azure VMs, VM Scale Sets, and container images, with a focus on upstream alignment, lifecycle predictability, and security servicing.

Sindhura Bandhakavi explains why “using fewer tokens” can backfire when you’re building AI agents: if the agent has too little context, it may compensate by making more calls to reach the same accuracy, which can increase total cost.

Bruno Capuano and Luis Quintanilla explain how .NET’s AI building blocks fit together for real-world apps, focusing on data ingestion, embeddings, and vector search to ground LLM responses in your own documents and business data.

Wiliam_Rosa breaks down Azure Databricks multi-table, multi-statement transactions (GA as of Oct 16, 2026), explaining the two transaction modes, how conflict detection works, and why external clients need explicit retry logic to be reliable under real concurrency.

sbaynes reports on Microsoft Research work proving the KLS conjecture, a long-standing result in high-dimensional geometry with implications for sampling, optimization, and randomized algorithms. The post also describes a human-directed, agentic AI workflow used to explore candidate proof paths and support derivations and verification.

Microsoft Security Research and Microsoft Threat Intelligence detail how Storm-0940 used the ARTEX agentic AI framework to accelerate a ConnectWise supply chain compromise, then abused OAuth application identities to access Microsoft 365 tenants via app-only authentication and Microsoft Graph, with concrete mitigation steps, detections, IOCs, and hunting queries.