Sr. Content Developer at Microsoft, working remotely in PA, TechBash conference organizer, former Microsoft MVP, Husband, Dad and Geek.
163108 stories
·
33 followers

Security Update for SQL Server 2025 RTM CU9

1 Share

The Security Update for SQL Server 2025 RTM CU9 is now available for download at the Microsoft Download Center and Microsoft Update Catalog sites. This package cumulatively includes all previous security fixes for SQL Server 2025 RTM CUs, plus it includes the new security fixes detailed in the KB Article.

Read the whole story
alvinashcraft
8 minutes ago
reply
Pennsylvania, USA
Share this story
Delete

Security Update for SQL Server 2025 RTM

1 Share

The Security Update for SQL Server 2025 RTM GDR is now available for download at the Microsoft Download Center and Microsoft Update Catalog sites. This package cumulatively includes all previous security fixes for SQL Server 2025 RTM, plus it includes the new security fixes detailed in the KB Article.

Read the whole story
alvinashcraft
8 minutes ago
reply
Pennsylvania, USA
Share this story
Delete

Build a Tenant-Wide Microsoft Foundry Model Operations Dashboard

1 Share

As AI projects move from pilots to production, model deployments often spread across subscriptions, regions, and application teams. A practical operations dashboard gives platform, engineering, governance, and finance teams one shared view of what is deployed, how it is used, what it costs, and where quota or capacity risk is forming. 

The operating problem 

Microsoft Foundry brings agents, models, tools, monitoring, tracing, evaluations, RBAC, networking, and policy controls under a unified platform surface. That is a strong foundation, but enterprise operations still need a view that cuts across the way Azure is usually organized: multiple subscriptions, resource groups, regions, business units, and application teams. 

The goal is not to replace product dashboards. The goal is to add a management-group or tenant-wide operating layer that answers simple but urgent questions: Which models are deployed? Which teams own them? Which deployments use Standard, priority, batch, or provisioned throughput? Where are token usage and PTU utilization rising? Which subscriptions are approaching quota pressure? Which costs should be allocated to which product or environment? 

Design principle: keep the dashboard about deployment and operations metadata. Do not collect prompts, completions, customer data, or other application payloads unless a separate privacy, security, and compliance design explicitly requires it. 

A practical reference architecture 

Use three data planes.  

  1. First, build the inventory plane with Azure Resource Graph, because Resource Graph is designed for efficient, large-scale querying across subscriptions and tenants. Combine account-level resource data with deployment-level data from the Azure AI Services deployments API, which lists deployments associated with a Cognitive Services account. Normalize subscription, resource group, region, account, deployment name, model name, model version, deployment type, SKU or capacity, tags, owner, environment, and business unit. 
  2. Build the telemetry plane with Azure Monitor. Public Azure OpenAI monitoring guidance describes dashboards for HTTP requests, token-based usage, PTU utilization, and fine-tuning. Azure Monitor supported metrics and resource log references provide the authoritative place to check what is available for each resource type and how exportability works. For exploratory operations views, Azure Workbooks are a good fit because they combine logs, metrics, parameters, Azure Resource Graph, Azure Resource Manager, JSON, and other data sources in one interactive report. 
  3. Build the finance plane with Cost Management APIs or exports. Cost Management automation guidance supports retrieving and managing cost data, including for larger datasets that need offline analysis. The Cost Management Query API supports usage queries at scopes such as subscription, resource group, billing account, management group, billing profile, and invoice section, subject to permissions. Power BI can be useful for leadership reporting, but the Cost Management connector has agreement constraints; direct connector support is documented for Enterprise Agreement and direct Microsoft Customer Agreement customers, while unsupported agreements should use exports. 

Separate inventory, usage, cost, and quota 

Do not treat every signal as the same kind of data. Inventory is mostly current-state metadata. Usage is time-series behavior. Cost is billing-scope data that may lag and must be joined carefully by resource identity, meter, and tags. Quota is a capacity-control signal, not a spend signal. Azure OpenAI quota is not enforced at tenant level; Microsoft Learn states that the highest quota restriction is scoped at the Azure subscription level. That means an executive dashboard can summarize quota posture broadly, but engineering views should still preserve subscription, model, region, and deployment-type detail. 

Provisioned throughput deserves a dedicated view. Microsoft documents PTU as dedicated model processing capacity for predictable throughput and latency, billed per PTU per hour or through reservations. That makes PTU utilization, idle capacity, traffic predictability, and latency requirement visible design inputs rather than after-the-fact finance questions. 

Dashboard views that matter 

Start with a few durable views rather than a large reporting program. The most useful first version usually includes: 

  • Executive summary: deployment count, model families, regions, monthly cost trend, and quota risk. 
  • Inventory: deployments by subscription, resource group, region, model, version, deployment type, owner, and environment. 
  • Operations: request volume, token trends, errors, latency indicators, and PTU utilization where available. 
  • Cost allocation: spend by owning team, application, environment, and deployment type. 
  • Governance hygiene: missing tags, ownerless deployments, old model versions, and regional concentration. 

Decision guidance 

Choose the reporting surface by audience. Platform engineers usually need Workbooks because they support operational drill-down and can combine Azure Monitor and Resource Graph. Finance teams often need Power BI because it supports broader cost modeling and distribution. Application owners need a small scorecard: their deployments, their usage, their costs, their quota posture, and their next required action. 

Production considerations and limitations 

  • Require tags before reporting on ownership. Dashboards cannot create accountability if resources do not carry owner, application, environment, and cost-center metadata. 
  • Use least-privilege access. Cost, inventory, and telemetry sources may require different permissions and scopes. 
  • Check portal-version language. Some public monitoring guidance is explicitly marked for the Foundry classic portal, so avoid assuming identical behavior in every new portal experience. 
  • Verify regional availability. Model availability varies by region, and deployment lifecycle settings such as automatic model updates should be reviewed before making standardization claims. 
  • Respect metric export limits. Azure Monitor notes that some metrics have export constraints, especially around multidimensional metrics and diagnostic settings. 

Implementation checklist 

  • Define the dashboard audience and the decisions it must support. 
  • Inventory Foundry and Azure OpenAI resources across subscriptions with Azure Resource Graph. 
  • List deployments per account and normalize deployment metadata. 
  • Route or query Azure Monitor metrics and logs needed for usage and PTU views. 
  • Add Cost Management queries or exports at the correct billing scope. 
  • Join by resource ID and tags; flag missing ownership metadata. 
  • Publish an Azure Workbook for operations and a Power BI model only where agreement support and permissions fit. 
  • Review model versions, quota posture, and PTU utilization on a fixed cadence. 

Conclusion 

A tenant-wide model operations dashboard is a practical bridge between AI experimentation and AI platform management. It does not need to be complex on day one. Start with inventory, usage, cost, quota, and ownership. Keep application content out of scope. Preserve subscription-level quota detail. Then evolve the dashboard into a repeatable operating rhythm for model lifecycle, cost control, capacity planning, and governance.

Read the whole story
alvinashcraft
8 minutes ago
reply
Pennsylvania, USA
Share this story
Delete

Tray-tober Day 17: Mini Sweeper

1 Share

When it comes to classic Windows games, for me Minesweeper. Now you can pop into some fun mine sweeping in your system tray with Mini Sweeper! This is a faithful recreation of the original minesweeper but with some extra updated visuals and animations build with Windows Composition APIs. If you are really aching for the battleship gray look, you can swap over to the classic style in settings.

As I’ve mentioned on the previous 2 Saturdays, little games in the tray are meant to keep you on the same task when you face a delay. Instead of multi-tasking or grabbing your phone, both of which can lead to complete distraction, Mini Sweeper does not have depth to further distract you. Play a quick round or two, then get back to your task.

Happy Tray-tober!

Joe

Read the whole story
alvinashcraft
8 minutes ago
reply
Pennsylvania, USA
Share this story
Delete

Syntax Recall Was Never The Goal

1 Share

The last 2 years have been some of the most exciting in the industry.

I’ve been thinking a lot this year about the impact AI is having on software development as a craft.

I’ve read a few posts recently from developers describing a sense of lost identity. AI is writing more of the code, sometimes entire features, and for some people the fun has gone with it.

I understand the feeling. But I think it comes from mistaking the tools of the job for the job itself.

~

How We Got Here

When I started out as a .NET developer, a good chunk of the job was remembering things. Namespaces, method signatures, the right overload. If you didn’t know it, you looked it up in MSDN or a book on your desk, and that took time.

I can still remember being on a client site with not internet access but priding myself on the fact that I knew the ASP.NET events and commands from memory that would let me build the main features for a CRUD application with lists, details, nested repeaters and so on!

IntelliSense arrived and you no longer had to remember every class and method name.

Google and Stack Overflow meant almost any problem had been solved by someone else.

Then Copilots started completing lines, then whole methods. Now agents can scaffold entire features while you review the output.

Each of those steps removed some recall from the job. Nobody had an identity crisis over IntelliSense. A few people grumbled about it making developers lazy, but most of us just got more done.

What’s different now is the size of the step. AI doesn’t just remember the method name. It writes the method, the tests, and the pull request description. That’s a much bigger leap, but it’s in the same direction we’ve been travelling for years.

~

So What’s The Goal ?

Solve problems. Generate value. Delight users. Make a profit.

That’s it.

No business ever paid a developer to remember syntax. They paid for outcomes, a system that works, a process that runs faster, a customer who comes back. Code was the means of getting there. It was always the means.

That’s why I think the identity question matters. If your sense of yourself as a developer was built on the means, the typing and the remembering, then AI feels like a threat. If it was built on the outcome, AI is a lever. Same tool, very different relationship with it.

~

What We Lost

I don’t want to cast shadow on the people who say the fun has gone, because some of it has.

There’s a satisfaction in wrestling with a problem for hours and finally getting it working with your own hands.

There’s hitting flow states, where the code seems to write itself because you know the language so well, and you’re so locked in, that time seems to stop. Caffeine into code and all that.

There’s the craft of writing something clean and elegant that nobody asked you to make beautiful. Sometimes I miss those parts too. It’s a bit like sanding the rough edges off.

There’s also a harder question for people earlier in their careers. Most of us built our judgement by writing a lot of code, much of it bad, but over time, write enought bad code and you end up growing and writing great code.

If AI writes the code, where does the judgement come from?

You still need enough fluency to read what’s produced and know when it’s wrong. You can’t review what you don’t understand.

So, syntax hasn’t fully disappeared. It just stops being the point.

~

Where The Satisfaction Lives Now

For me, the satisfaction has moved up a level.

This year I’ve spent a lot of time building RAG pipelines and MCP servers. I typed far less code than I used to. But I spend more time thinking about whether retrieval is returning the right results, what happens at the edges, and what the user actually needs rather than what they asked for.

The interesting decisions are now about framing the problem, designing the system, deciding what not to build, and spotting that something is wrong before the tests tell you. That’s still engineering.

Arguably it’s the part that always mattered most. We just spent most of our time on the typing in between.

Shipping something useful in days rather than weeks has its own kind of fun too. The gap between “I have an idea” and “let’s see what this looks like” is much smaller now.

~

Closing Thought

Syntax recall was a tax we paid to get to the real work. AI is removing more of that tax than any tool before it.

If you loved the work for what it produced, the problems solved and the value delivered, there’s never been a better time to be building. If you loved it for the typing, that’s a real loss, and it’s worth admitting. But it was never the goal, and there was never any glory in remember obscure syntax.

What do you think?

Drop your comments below.

~

JOIN MY EXCLUSIVE EMAIL LIST
Get the latest content and code from the blog posts!
I respect your privacy. No spam. Ever.
Read the whole story
alvinashcraft
8 minutes ago
reply
Pennsylvania, USA
Share this story
Delete

The Most Important AI Model Announcements This Week

1 Share
From: AIDailyBrief
Duration: 25:22
Views: 3,257

The most consequential AI announcements this week might be the ones getting the least attention. NLW explores what Microsoft’s MAI Base 2 and Meta’s Muse Spark Open mean for how businesses use, customize, and pay for AI. In the headlines: Anthropic’s embedded safety evaluators, Cognition’s performance-based token budgets, and a community-built AI battle royale.

The AI Daily Brief helps you understand the most important news and discussions in AI.
Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614
Get it ad free at http://patreon.com/aidailybrief
Learn more about the show https://aidailybrief.ai/

Read the whole story
alvinashcraft
2 hours ago
reply
Pennsylvania, USA
Share this story
Delete
Next Page of Stories