Browse All Posts (179)

DevClass reports on Vercel’s Ship event announcements, focusing on the new open source eve agent framework and enterprise controls like Passport for bringing AI-built apps and agents under centralized identity and policy, including OpenID Connect support for providers such as Okta and Microsoft Entra ID.

sunil_sabat explains how Microsoft Fabric Data Factory supports multi-cloud data integration and orchestration across platforms like Snowflake, Databricks, Google BigQuery, and Salesforce, and how OneLake features (Shortcuts, mirroring) and Purview lineage help teams build governed, production-ready analytics and AI data flows.

Allison announces an update that lets Dependabot read from private GitHub-hosted package registries using the built-in GITHUB_TOKEN, removing the need for personal access tokens when the package grants Actions access to the repository.

Allison announces the general availability of GitHub Copilot CLI’s redesigned terminal interface, including tabbed navigation for issues, pull requests, and gists, plus an in-terminal setup flow for MCP servers, skills, plugins, and settings, with improved accessibility features.

Kayla Cinnamon demonstrates using MAI-Code-1-Flash inside VS Code via Copilot Chat to ship a feature end to end—navigating an existing codebase, building and running the project, and validating changes with tests—while also calling out the model’s cost benefits.

Justin Garrett interviews Microsoft MVP Andrew Pruski about building “Burrito Bot,” a semantic search demo that uses SQL Server 2025 vector search to recommend restaurants based on meaning rather than keywords, covering embeddings, similarity scoring, and scaling with vector indexes and ANN techniques.

Allison announces that Dependabot has dropped support for Python 3.9 due to end-of-life status, and explains the practical impact: Dependabot may stop opening pull requests for dependency updates if your setup still relies on Python 3.9.

Rohan Malpani explains how AI is being designed into developer tools, comparing Visual Studio “skills” with VS Code agent-based workflows and showing how teams move from prototypes to production-ready, AI-assisted engineering with attention to iteration, quality, and security.

Tim D'haeyer explains how to replace BizTalk-style code-table mapping during migrations by using an Azure Function that enriches XML documents via XPath-driven rules and SQL lookups, keeping Azure Logic Apps focused on orchestration instead of complex transformation logic.

Andrew Lock explains the new StringBuilder.MoveChunks() API in .NET 11 preview 5, showing how it can avoid large ToString() allocations by transferring a StringBuilder’s internal buffers. He also digs into the implementation details and why this matters for Roslyn source generators and SourceText creation.

John Edward introduces the GitHub Copilot Desktop App and explains how it extends Copilot beyond the IDE into a standalone workspace for understanding repositories, planning work, and getting AI help across day-to-day development tasks.

Learn Microsoft AI explains AgentSession in Microsoft Agent Framework, focusing on how it acts as a conversation state container to preserve context across agent runs and enable reliable multi-turn, stateful agent workflows.

GitHub introduces a beginner-friendly walkthrough of the GitHub Copilot app as a central place to manage work from idea to pull request, including using AI agents to explore issues, build features, and review changes in parallel.

Justin Bettencourt rounds up the May 2026 Azure SDK releases, including GA for the Azure SDK for Rust and the .NET Azure Batch client library, plus new Azure AI Search knowledge-base retrieval features and preview Azure AI Agent Server hosting libraries across .NET, Python, and JavaScript.

Santhosh_Ravin1 introduces Efficient Scaledown (Preview) for Microsoft Fabric Spark, explaining how remote shuffle storage and shuffle migration reduce recomputation during scale-down, improve resiliency, and cut compute costs, with concrete benchmark results and the Spark configuration needed to enable the feature.

Leonardo Micheloni explains how spec-driven development can reduce LLM “guesswork” by giving AI a clearer structure to work from, using GitHub Spec Kit as the concrete example discussed on On .NET Live.

Natalie Isak and Sarah Cooley explain how “AI memory” changes the threat model for assistants and agents, enabling delayed, cross-session attacks like adversarial memory poisoning. They outline Microsoft’s defense-in-depth approach for Microsoft 365 Copilot, including write-time sanitization, policy-governed storage, and auditability for SOC investigations.

Wes Steyn shows how to build an “agent harness” (a loop around a model with tools, planning, memory, and web search) using Microsoft Agent Framework. The post walks through creating a chat client with Microsoft AI Foundry, wrapping it into a harness agent, and running it in a console UI with plan/execute modes.

Wes Steyn introduces a hands-on series for building a CLI-style “claw” (a coding agent) using Microsoft Agent Framework, explaining the core harness loop—tools, planning, memory, approvals, and observability—and outlining how the sample evolves from a minimal agent to a production-ready service in .NET and Python.

Santhosh_Ravin1 explains how Microsoft Fabric’s Native Execution Engine (NEE) speeds up Spark workloads that use Python/Scala UDFs and nested data types. It covers why UDFs and complex types have historically forced costly serialization and row-based fallbacks, what NEE changes in the execution path, and the benchmarked performance gains.

Dona Sarkar shares what she thinks indie developers will be excited about at Microsoft Build 2026, focusing on AI-powered devices, new hardware form factors, and why these shifts matter beyond just writing code.

yexu announces general availability of invoking Microsoft Fabric Copy jobs directly from Fabric Activator, enabling event-driven data movement that runs only when a condition is met (like a file landing in OneLake or a table update) instead of relying on fixed schedules.

Microsoft Incident Response (DART) breaks down a ransomware investigation where two unrelated threat actors operated in parallel inside the same environment, blending tactics and obscuring attribution. The post highlights the intrusion chain, evasion and persistence techniques observed, and practical defensive priorities around patching, identity protection, and centralized telemetry.

GeertVanTeylingen explains how the NFS nconnect mount option (nconnect=4) improves Azure VMware Solution datastore performance when using Azure NetApp Files, by enabling multiple parallel TCP connections per ESXi host to increase throughput and reduce latency under concurrent I/O.

Allison summarizes new GitHub Copilot updates for JetBrains IDEs, including org/enterprise custom agents, better control of long-running Copilot CLI requests, an agent debug logs summary view, and a public preview of Claude as an agent provider, plus model picker and AI credits visibility improvements.

Payal Mahesh and Vicky Lin share large-scale test results showing how Azure Container Registry’s internal per-layer replication affects AKS image pull performance. They explain why there’s a “sweet spot” where extra copies eliminate storage throttling, why too many copies can regress tail latency, and what ACR is building next.

GitHub shows how a GitHub engineer uses the GitHub Copilot app with MCP server integrations to automate morning triage, including scheduled workflows, a daily brief, and surfacing prioritized issues to reduce noise before the day starts.

Waldek Mastykarz explains why “model preferences” (like “Claude prefers React”) are usually artifacts of missing or changing context, not stable traits of an LLM. He outlines how prompt format, workspace files, and evaluation-like prompting can shift outputs—and how to test models using realistic repo and task context.

Visual Studio Code highlights a quick path for moving a project from .NET Framework 4.8 to .NET 10 in VS Code, and points viewers to the AwesomeCopilot repository for related resources.

DevClass reports on upcoming npm 12 default changes that stop install-time scripts from running automatically, aiming to reduce a major supply-chain attack surface on developer machines and CI runners. The piece explains the new flags, breaking-change impact, and how teams can prepare using npm 11.x settings.

Eric van Wijk announces the deprecation and planned retirement of the Azure DevOps OIDC issuer used by Workload Identity Federation (WIF) service connections, and explains what Azure Pipelines users need to do to move existing connections to the Microsoft Entra issuer before the 2027 deadline.

DevClass reports on Checkmarx survey findings that many developers believe AI-generated code contains more vulnerabilities, yet some still ship it to production. The piece connects AI-assisted development, open source supply-chain risk, and security process gaps to higher breach frequency.

John Edward explains why AI apps are moving from single “copilot” assistants to multi-agent systems, and how Semantic Kernel can be used to orchestrate specialized agents that collaborate via tools, memory, and coordination patterns—along with the practical engineering challenges this introduces.

Thomas Maurer explains Azure Local Simplified Machine Provisioning, a new workflow for provisioning physical Azure Local nodes with minimal on-site work while keeping configuration and control centralized in Azure.

This week in DevOps, the common thread is making change safer: Azure platform migrations are getting clearer control points (from Logic Apps hosting redirects to large-scale networking cutovers), and GitHub Actions is tightening defaults and trigger policies to reduce workflow abuse. Security teams also got concrete lessons from an npm compromise, alongside steady improvements in secret scanning and more structured, production-focused AI scanning pipelines. On the automation and operations side, MCP servers are turning agent-driven work into repeatable, auditable tools, while Azure Monitor adds practical alerting options (dynamic thresholds and per-row alerts) and deeper guidance on evidence-backed investigations with the Copilot Observability Agent.

This week in Security, AI agents and MCP-based tooling ran into familiar trust-boundary problems, especially when browser-like agents can be pushed from untrusted web content into localhost services and privileged tools. Microsoft Defender Security Research unpacked AutoJack, showing how a single page can drive an agent into an MCP WebSocket path that ends in host-side code execution, reinforcing the need for explicit mediation, authentication, and monitoring even on loopback. On the control side, teams shared concrete governance patterns like placing Azure API Management in front of MCP servers to enforce tool visibility, logging, and rate limits, alongside deterministic agent workflows in the ARM MCP Server that make infrastructure changes reviewable and repeatable. Rounding it out, enterprise reinforcement learning guidance emphasized that training loops need production-grade isolation too, using sandboxed environments and clear evaluation gates to keep experimentation contained.

This week's ML roundup connects three threads teams keep running into in production: how to improve agent behavior with measurable learning loops, how to query governed data across tools without copying, and how to keep AI-assisted operations safe. Microsoft outlined an enterprise reinforcement learning workflow with OpenEnv and Foundry that centers on controlled environments, rubric-based scoring, and managed post-training, while OneLake interoperability expanded across Databricks and ServiceNow through catalog federation and Iceberg-compatible table APIs. We also saw practical agent patterns in analytics and operations (MCP-based query agents, Spark diagnostics skills, Postgres guardrails), plus a look at extreme-scale training engineering from Azure and NVIDIA and a new open dataset for multilingual research.

This week's Weekly AI Roundup is about AI moving from chat helpers to agent-driven workflows that ship real code and run inside everyday team processes. GitHub Copilot's new desktop app, stronger CLI and IDE agent modes, and GitHub-side changes (review shaping, PR attribution, issue triage) all point to agents becoming normal collaborators, with MCP as the connective tissue. At the same time, model routing, lifecycle changes, and per-user spend reporting are turning cost and policy into daily ops concerns. We also cover MCP's expanding tool ecosystem (from APIM gateways to MSBuild binlog analysis), the AutoJack security lesson on trust boundaries, and practical grounding patterns for RAG across Azure AI Search, file data via OneLake shortcuts, and Postgres-backed retrieval.

This week's Azure roundup focuses on platform migrations where waiting can turn a routine change into a risky cutover. Logic Apps Standard is preparing to move from in-proc hosting to the Azure Functions out-of-proc model as part of the path to .NET 10, so teams should validate early and avoid depending on temporary redirect behavior. On the networking side, Azure Firewall explicit proxy shifts PAC retrieval to Azure Storage with SAS and identity-based access, while large hub-and-spoke topologies get a practical playbook for moving ExpressRoute MSEE hairpin routing to AVNM mesh without weakening segmentation or inspection.

This week, GitHub Copilot moved further into an agent-first workflow with the Copilot desktop app reaching general availability, tightening the loop from issue to merge with canvases, parallel sessions, and Git worktrees under the hood. At the same time, Copilot is getting more explicit about model operations: Auto mode is now available to everyone in Copilot Chat, token efficiency work is reducing long-session overhead, and teams need to plan for the Opus 4.6 (fast) deprecation with policy-aware replacements. On the governance side, new enterprise controls, richer usage and AI credit reporting, and better attribution for agent-opened pull requests make it easier to roll out agents responsibly. Rounding it out, MCP and agent discovery expanded into build diagnostics, database tooling, and cross-editor workflows (CLI, JetBrains, and SSMS), showing where Copilot integrations are heading next.