Browse Artificial Intelligence Videos (288)

dotnet demonstrates how to build an agentic AI companion using the GitHub Copilot SDK, a .NET and Aspire backend, and an M5Stack StackChan robot. The session explores agent behaviors and tool use, then connects the agent to the robot’s display and movements.

dotnet demonstrates how to build a StackChan robot's AI brain using a .NET backend orchestrated with Aspire. The session connects the backend to the physical desk robot, adds services and local observability, and brings the pieces together into an end-to-end interaction.

dotnet demonstrates how to bring an M5Stack StackChan desk robot to life by programming its expressions and movements and connecting it to a .NET backend and dashboard. This hands-on session explores how hardware and .NET services work together to create a first end-to-end interaction with a physical AI companion.

GitHub takes viewers inside Pimoroni’s factory to explore how the hackable GitHub Universe 2026 badge is manufactured. The video follows stencil printing, component placement, and solder reflow, and covers the handheld console’s capacitive touch controls, auto-flipping interface sensors, and custom firmware developed with GitHub Copilot.

Christoph Berlin explains how manufacturers can reuse one AI agent across cloud, edge, and fully disconnected environments. Building on a root cause example, he describes shared data contracts, guardrails, and repeatable blueprints that support deployment across many sites, with agent instances managed through a single interface.

Louis Erbkamm shares how Arnis converts real-world geographic coordinates into playable Minecraft worlds using OpenStreetMap data. The video explores the project's international contributor community and how its sole maintainer uses GitHub Copilot to triage bug reports, review pull requests, and keep the project moving.

Visual Studio Code demonstrates how the PostgreSQL extension and GitHub Copilot help you explore an unfamiliar database. The episode covers inspecting schemas, tables, views, and functions, visualizing relationships, asking context-aware questions in Copilot Chat, and checking answers against the database to understand its data model.

Visual Studio Code demonstrates a PostgreSQL workflow combining the PostgreSQL extension’s IntelliSense and database context with GitHub Copilot. The video covers drafting, explaining, analyzing, and improving SQL queries, then running them, inspecting results, and exporting data without leaving the editor, while keeping developers in control of the final queries.

John Savill examines why AI agents fail beyond the model itself, covering unclear goals, bad context, tool misfires, reasoning errors and lost state. He also discusses hijacking, excessive privileges, limited visibility and an improvement loop for understanding and addressing failures.

Microsoft Developer highlights three updates: .NET 11 is ready for serious production validation, Python 3.15 defaults to a native Arm64 runtime on Windows on ARM, and browser automation in Microsoft Foundry is generally available. The video outlines what these changes mean for developers.

Microsoft Developer demonstrates how to protect an AI agent from malicious user input and instructions hidden in tool responses. The walkthrough configures Microsoft Foundry guardrails and Prompt Shields, replays both attacks, and uses traces to show where protections intervene before the agent acts.

John Savill reviews this week's Azure and AI updates, including networking, backup, monitoring, storage, and identity changes. Topics include Traffic Manager integration with Azure DNS, keyless TLS, Cosmos DB key rotation, Azure Databricks private networking, and Copilot Studio agent identity.

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.

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.

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.

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.

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.

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.

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.

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.

GitHub shares a September platform update roundup covering Project HydraFusion’s expansion to VS Code and the GitHub Copilot app, plus new workflow features like local sandboxing GA, automated PR approvals that can satisfy merge gates, and bulk autofix for CodeQL findings.

Anna Hoffman explains how Microsoft SQL spans on-premises, Azure, and Microsoft Fabric, highlighting what’s new in the engine, developer tooling, and migration options. She also demonstrates AI-assisted performance troubleshooting and scalable vector search, and shows how Azure Arc/Azure Local and Foundry-based experiences can support modern data-driven apps.

Sukhwant Kaur demonstrates how SQL database in Microsoft Fabric can support app development, analytics, and AI in one place—covering database creation, workload migration, and automatic mirroring into OneLake, plus Git-based workflows with VS Code and a T-SQL RAG example for product search.

Patrick LeBlanc demonstrates Fabric Database Hub’s database agent for observing SQL Server databases, surfacing performance signals, and guiding investigation from detection through hands-on troubleshooting, including a blocking scenario in SSMS and follow-up analysis workflows in VS Code with GitHub Copilot support.

Bob Ward explains what “AI-ready” means in SQL Server 2025, focusing on native vector capabilities, embedding generation, and RAG patterns you can implement directly with T-SQL across local and cloud AI model deployments.

Amit Khandelwal demonstrates an end-to-end migration of a working app from SQL Server to Azure SQL Database Hyperscale, using SSMS assessments and VS Code tooling to evaluate readiness, address blockers, and execute a BACPAC-based move. He also shows GitHub Copilot helping resolve a Service Broker issue and validating the cutover.

Visual Studio Code introduces Canvases (Preview), a shared workspace concept in VS Code designed to help teams plan releases, prioritize backlog work, and collaborate with a coding agent using contextual cards that can be turned into actionable plans.

Microsoft Developer presents SQL Modernization Day, a set of sessions from the Microsoft SQL team focused on moving SQL Server workloads to Azure without rewriting applications, and then preparing those workloads for building AI-ready apps using modern Azure and Fabric SQL options.

GitHub shows how to fix repository issues from inside JetBrains IntelliJ by installing the GitHub Copilot plugin, browsing open GitHub issues, and using Copilot to generate an implementation plan and apply the changes with its coding agent.

Julia Muiruri demonstrates an end-to-end workflow for building and shipping an AI agent directly from Visual Studio Code, using GitHub Copilot and the Foundry Toolkit extension to pick a model, test the agent against a real product catalog, deploy to Microsoft Foundry, and connect it to a website via a secure proxy.

Takuto Higuchi interviews Sindhura Bandhakavi on why AI agents get expensive at scale, and how to think about “tokenomics” beyond simply cutting tokens. The episode shows how Azure AI Foundry features like Model Router and Agent Optimizer can help balance quality, performance, and cost while keeping agent usage measurable and controlled.

GitHub demonstrates an end-to-end workflow for taking an open GitHub issue to a pull request using GitHub Copilot inside JetBrains IntelliJ IDEA, including installing the plugin, planning the work, implementing a fix, reviewing changes, and validating accessibility.

John Savill explains what an agent control plane is and why it matters when you start running AI agents at scale, focusing on the need for centralized oversight, evaluation, and cost governance.