This week on Azure Friday, Scott Hanselman talks with Jonathon Frost about AI-enhanced migration from Oracle to PostgreSQL using the VS Code PostgreSQL extension. See how developers can automate schema conversion, transform application code, and validate results using an intelligent, agent-driven workflow.

🌮 Chapter Markers:

0:00 – Introduction
0:36 – Model Hyperparameter Tuning, Agent Orchestration, and Determinism
2:25 - Architectural Overview of AI-enhanced Schema Migration
4:28 – How it is Built on the VS Code Extension for PostgreSQL
4:56 – Self-correction of AI-enhanced Migration
5:59 – Migration Demo
6:33 - Connect to Oracle Database
7:23 - Connect to PostgreSQL Database
7:50 - Connect to Azure OpenAI Endpoint
8:45 - Run Migration
9:37 – Review Completed Migration Report
11:07 - Visualize Schema of PostgreSQL Database
12:17 – Side-by-side File Diff
13:30 - Where to get the Extension and Learn More

🌮 Resources:
Learn Docs: https://aka.ms/pg-migration-tooling
VS Code Extension Marketplace Page: https://aka.ms/pgsql-vscode
Azure Product Page: https://azure.microsoft.com/products/postgresql/
Blog: https://aka.ms/pg-blog-orcl

🌮 Follow us on Social:
Jonathon Frost | https://linkedin.com/in/jjfrost
Azure Database for PostgreSQL | https://www.linkedin.com/company/azure-database-for-postgresql/
Scott Hanselman | @SHanselman – https://x.com/SHanselman
Azure Friday | @AzureFriday – https://x.com/AzureFriday

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Twitter - https://aka.ms/azuredevelopers/twitter
LinkedIn - https://aka.ms/azuredevelopers/linkedin
Twitch - https://aka.ms/azuredevelopers/twitch

#azuredeveloper #azure

Overview of major model and agent launches: Meta's Muse/Spark multimodal models and personal-agent focus, Google's Gemini notebooks for shared task contexts, and open-source GLM 5.1 pushing coding benchmarks. Benchmark comparisons show GLM 5.1 and Muse leading on coding and visual reasoning while Anthropic's Claude/Mythos faced a restricted rollout over cybersecurity concerns. New managed-agent stacks and agent harnesses promise rapid prototype-to-production flows and persistent-memory assistants, with tooling, governance and safety challenges in the spotlight

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In this episode, we explore how AI enhances Operational Excellence. From observability and troubleshooting to automation, governance, and human-in-the-loop decision making.

In this conversation, Boris Scholl and Niels Buit discuss where AI delivers real value—and where architects must carefully manage risks and tradeoffs.

If you’re a cloud architect, platform engineer, or developer responsible for operating systems in production, this video will help you understand how to apply AI practically and assess risks and tradeoffs in real-world environments.

✅ What You’ll Learn

How AI fits into the Operational Excellence pillar of the Well-Architected Framework
How AI improves observability, dependency mapping, and proactive issue detection
What AI can do today in root cause analysis, summarization, and remediation
The risks of non-deterministic systems and how to mitigate them
When and why to keep a human in the loop
Security, privacy, and governance considerations for AI-driven operations
How AI can turn WAF from a static checklist into a living, evolving system

00:06 – AI and Operational Excellence in the Well-Architected Framework
00:48 – Running AI vs Using AI to Improve Operations
01:21 – Why Day Two Operations Matter More Than Day One
02:05 – Observability, Deployment, and Troubleshooting
02:40 – Why Observability Is the Best Starting Point for AI
03:38 – Non-Deterministic AI and Operational Risk
04:05 – Real-World AI Use in Engineering Operations
04:39 – AI Opportunities: Simulation and Auto-Remediation
05:00 – Getting Started with OpenTelemetry
05:48 – What AI Enables in Observability Today
06:24 – Dependency Graphs and Predictive Failure Detection
07:01 – Human-in-the-Loop for Critical Incidents
08:00 – Fail Fast, Guardrails, and Responsible AI
08:50 – Hallucinations, RCA Accuracy, and Summarization Risk
09:29 – AI-as-a-Judge and Evaluation Patterns
10:17 – Security and Privacy Considerations
11:30 – How WAF Helps Navigate AI Tradeoffs
12:19 – Making WAF a Living, AI-Driven Framework
13:15 – Key Takeaways and Final Thoughts

🔗 Resources & Links
🚀 Azure Well-Architected Framework
https://learn.microsoft.com/azure/well-architected/
Microsoft Learn
https://learn.microsoft.com/en-us/azure/well-architected/operational-excellence/
⚡ Try Azure for free
https://aka.ms/AzureFreeTrialYT

👥 Speakers
Boris Scholl
https://www.linkedin.com/in/bscholl/

Niels Buit
https://www.linkedin.com/in/nielsbuit/

#Azure
#AI
#OperationalExcellence
#WellArchitectedFramework
#CloudArchitecture
#Observability
#OpenTelemetry
#DevOps
#ResponsibleAI
#AzureArchitecture
#MicrosoftDeveloper
#CloudComputing

Welcome back to GitHub Checkout! In this episode, we sit down with Copilot CLI Product Manager Ryan Hecht to explore the latest experimental features. We dive into the new plugin marketplace, the powerful /chronicle slash command for self-healing prompts, and how to run Claude Opus 4.6, GPT-5.4, and Gemini 3 simultaneously. See how /fleet mode and autopilot can handle massive refactoring tasks while you step away.

Install Copilot CLI: https://github.com/features/copilot/cli?utm_source=social-youtube-checkout-cli-features-cta&utm_medium=social&utm_campaign=dev-pod-copilot-cli-2026

#GitHubCopilot #CopilotCLI #GitHub

— CHAPTERS —

00:00 Welcome to GitHub Checkout
00:26 Experimental mode and subagents
01:16 Managing Copilot CLI plugins
03:16 Using the chronicle command
07:39 Multi-model background agents
11:10 Fleet mode and autopilot
13:13 What is coming next?

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About GitHub
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