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Comment desinstaller windows 10 pour installer windows 11 proprement sur mon PC

Pachole — Mon, 09 Mar 2026 08:26:40 GMT

Avec la fin du support de Windows 10 qui approche, je voudrais mettre à niveau mon ordinateur vers Windows 11. Le problème est que je préfère repartir de zéro et desinstaller windows 10 pour installer windows 11 proprement plutôt que de faire une simple mise à jour. Je ne suis cependant pas sûr de la meilleure méthode pour effectuer cette installation propre sans rencontrer d’erreurs.

J’ai vu qu’il est possible de créer une clé USB bootable avec l’ISO de Windows 11, puis de démarrer le PC dessus pour supprimer les anciennes partitions. Est-ce la bonne méthode pour desinstaller windows 10 pour installer windows 11 ? Est-ce que je dois formater le disque avant ou l’installateur de Windows 11 s’en charge automatiquement ?

Can Your M365 Copilot Offer Actually Win Customer Deals?

MaheshKshirsagar — Mon, 09 Mar 2026 08:00:00 GMT

Note: This tool is designed for Microsoft partners operating within the AI Business Solutions commercial solution area who are building or publishing Microsoft 365 Copilot consulting service offers on the Microsoft Marketplace.

Creating Offers That Win Deals

Microsoft 365 Copilot represents one of the most significant opportunities for Microsoft partners. Organizations worldwide are seeking trusted partners to guide their AI transformation journey. The Microsoft Marketplace is where these customers discover partner services—making your offer listing your most critical sales asset.

A great Copilot service deserves a great marketplace listing—and getting there means balancing multiple dimensions at once:

Distributed Guidance: Offer development best practices are distributed across multiple documentation sources, slide decks, and training materials

Manual Review Burden: Evaluating whether your offer content addresses all required elements—value proposition, business outcomes, change management methodology, co-sell alignment—requires hours of cross-referencing

Quality Consistency: Without a systematic approach, offer descriptions may miss critical customer-facing elements that drive engagement

Co-sell Alignment Complexity: Achieving proper alignment with the AI Business Solutions solution area requires understanding evolving co-sell requirements

Iteration Overhead: Each content revision requires repeating the manual evaluation process, extending time-to-market

Getting these elements right is what separates offers that convert from those that get scrolled past.

Introducing M365 Copilot Offer Validation Assessment

M365 Copilot Offer Validation Assessment eliminates guesswork from marketplace offer development. This tool analyzes your offer content against Microsoft's official M365 Copilot Offer Development Guide and generates actionable, prioritized recommendations—in minutes.

How It Works

Document Upload: Assess your offer content before publishing by uploading PDF, Word, or PowerPoint documents containing your draft listing content

35+ Evaluation Rules: Comprehensive rule engine evaluates your content across 13 service areas derived directly from Microsoft guidance

AI-Contextualized Feedback: Every observation is translated into Copilot-specific improvement recommendations with references to source guidance

Local Execution: Runs entirely on your machine. Your offer content never leaves your environment

What You Get

The assessment generates detailed Excel report as shown:

Report showing the outcome after assessment.

Report includes:

Column	Description
Area	Category of guidance (Value Proposition, Business Outcomes, etc.)
Configuration	Specific rule being evaluated
Status	Pass, Needs Review, or Gap identified
Priority	High, Medium, or Low importance
Observation	What was found (or not found) in your content
Recommendation	Specific improvement action with guidance reference
Reference	Link to Microsoft documentation

Evaluation Coverage

The tool evaluates your offer against 13 critical service areas:

1. Offer Summary — 100-200 character value-led summary

2. Offer Description — Comprehensive 500-3000 character description with target audience, deliverables, and scope

3. Search Keywords — Copilot, M365, AI, and industry-specific discoverability terms

4. Value Proposition — Clear articulation of how you maximize Copilot ROI

5. Business Outcomes — Measurable productivity gains, time savings, and efficiency metrics

6. Functional Scenarios — Target roles (Sales, HR, Finance, Legal, Customer Service) with relevant KPIs

7. Service Pillars — Coverage of Advisory, Deployment, Extensibility, and Adoption services

8. Adoption & Change Management — Methodology, stakeholder engagement, training, and success measurement

9. Call to Action — Clear engagement path for interested customers

10. Pricing — Transparent pricing structure and engagement terms

11. Media Assets — Collateral references, case studies, and promotional materials

12. Security & Compliance — Data governance, oversharing risks, and technical readiness

13. Co-sell Readiness — AI Business Solutions solution area and Microsoft 365 product alignment

Value for Partners

Tool helps partners to:

Reach Quality Faster

Reduce the time from draft to polished offer. Automated validation shows exactly where to improve—so each revision moves you closer to a listing that converts.

Maximize Co-sell Opportunity

The tool specifically validates alignment with Microsoft's AI Business Solutions solution area and co-sell requirements. Properly aligned offers receive prioritized attention from Microsoft sellers—driving qualified customer referrals.

Differentiate Your Services

Stand out in the marketplace with offers that clearly communicate:

- Measurable business outcomes customers can expect

- Specific functional scenarios and target roles

- Formal change management methodology

- Security and governance expertise
Standardize Quality

Whether you're a large partner with multiple practice leads creating offers or a boutique firm with a single marketplace presence, the tool ensures consistent evaluation against the same Microsoft guidance standards.

Iterate with Confidence

Make changes to your offer content and re-run the assessment to verify improvements. Timestamped reports track your progress across multiple evaluation cycles.

Value for Customers

When partners use this tool, customers benefit from:

Clearer Value Communication

Offers created with guidance-aligned content clearly articulate what customers will receive, enabling faster and more confident purchasing decisions.

Outcome-Focused Engagements

Partners prompted to include measurable business outcomes help customers set realistic expectations and track Copilot deployment success.

Reduced Risk

Offers that address security, compliance, and governance concerns upfront give customers confidence that data protection is a priority—not an afterthought.

Better Partner Matching

Well-structured offers with specific functional scenarios and industry focus help customers identify partners whose expertise matches their needs.

Getting Started

M365 Copilot Offer Validation Assessment is available now. Requirements are minimal:

Python 3.8 or later

PDF, Word, or PowerPoint document with your offer content

Start in three steps:

1. Install Python dependencies: `pip install -r requirements.txt`

2. Run the tool: `python main.py`

3. Upload your document when prompted

Within minutes, you'll have a comprehensive assessment report with prioritized recommendations tailored to your specific offer content.

Elevate Your Marketplace Presence

In a crowded marketplace, quality matters. Customers searching for M365 Copilot partners have dozens of options—your offer listing is your opportunity to stand out.

M365 Copilot Offer Validation Assessment provides the objective, guidance-driven analysis you need to create marketplace listings that communicate your expertise, align with Microsoft co-sell priorities, and convert customer interest into engagement.

Stop guessing whether your offer meets the bar. Start validating.

Resources

M365 Copilot Offer Validation Assessment: microsoft/m365-copilot-marketplace-offer-assessment

M365 Copilot Offer Development Guide: https://microsoftpartners.microsoft.com/Downloads/?filename=abs/unprotected/M365-Copilot-Offer-Development-Guide.pptx

Microsoft Marketplace Documentation: https://learn.microsoft.com/partner-center/marketplace/

Tokyo Stock Exchange data

OferP2023 — Mon, 09 Mar 2026 07:35:07 GMT

Does any one know, and can share, a sample excel file that can import updated information (ticker, name, last price, dividend yield, daily high and daily low and analysts average target price) for ~50 stocks traded on the Tokyo stock exchange, which is not covered by Microsoft?

The Nightmare of renewing NDES Enrollment Agent Certificates

DagmarHeidecker — Mon, 09 Mar 2026 07:28:55 GMT

NDES EA Certificates – Quick Recap

By default, three version 1 certificate templates are assigned to your Certification Authority by the configuration routine of the NDES service:

CEP Encryption - Used by the device to encrypt communication with NDES
Exchange Enrollment Agent (Offline Request) - Used to request certificates on behalf of another subject
IPSec (Offline request) - Default template to enroll certificates to devices

All certificate templates from the list above are version 1 certificate templates. Number 1 and 2 share the common characteristic of having the Extended Key Usage (EKU) extension set to include the OID 1.3.6.1.4.1.311.20.2.1, which corresponds to “Certificate Request Agent”. In this article template number 1 and 2 (from the list above) will be referred to as “NDES Enrollment Agent certificate(s) templates”.

Version 1 certificate templates originated with Windows 2000 and have functional and security limitations. Since the autoenrollment feature did not exist at that time, these templates do not support autoenrollment and instead rely on Automatic Certificate Request Settings, a legacy mechanism that is no longer recommended. Furthermore, the only property that can be modified on a version 1 template is the set of assigned permissions that controls access to the template. Find more details in Certificate Template Concepts.

Certificate Enrollment (or Request) Agents were designed to enable trusted principals to perform certificate enrollment on behalf of other users or devices (aka Enroll-on-behalf). NDES is a concrete implementation of this concept as it enrolls certificates for entities other than itself.

The enrollment of the NDES EA certificates based on certificate templates number 1 and 2 (see above) during NDES configuration is hard‑coded in the configuration routine. This design choice from many years ago introduces several challenges:

Security: in case you misconfigure the default NDES certificate templates security settings, they are vulnerable to CVE-2024-49019. A detailed explanation of this vulnerability is out of scope here; however, as a general best practice, certificate templates version 1 should not be used.
The default “Exchange Enrollment Agent (Offline request)” certificate template (default template number 2. as per above) is a user template and the installation routine “somehow magically” imports this certificate into the machine store. This makes automatic renewal challenging...
Version 1 certificate templates have significant functional limitations, as they cannot be modified (except for security settings):
- The validity period (2 years) cannot be changed. For NDES EA certificate templates the validity period is 2 years.
- The template’s CSP[1] cannot be modified. As a result, NDES Enrollment Agent certificates cannot be enrolled in a Hardware Security Module (HSM).
- Version 1 templates do not support Autoenrollment. Consequently, NDES service certificates therefore must be renewed manually. When the Enrollment Agent certificates expire, NDES stops working.
- Version 1 templates lack template-level access control and modern enrollment safeguards (e.g. Certificate Manager Approval).

[1] NDES does not support KSP for EA certificates.

As you can see, there are several reasonable arguments to replace the default NDES service certificate templates.

Configuring custom NDES Service Certificate Templates

Generally, there are two ways of creating some kind of “fire and forget” certificate templates for NDES:

Common Windows Active Directory Autoenrollment can be used if there is no need for a custom name/subject in the request agent certificates.
We can use key-based renewal (KBR) which allows us to create custom subjects in the certificates even together with automatic renewals.

The NDES service will not verify the certificates’ subject information. It will just verify that the certificates have “request Agent” EKU (1.3.6.1.4.1.311.20.2.1).

In a nutshell, you will need to duplicate two version 1 certificate templates and modify those to fit your needs. See table below for detailed description of settings.

In addition to that, there are a few more things to consider:

NDES service account

There are different options for creating the SCEP IIS App Pool identity. As Microsoft recommends using a hardened Tier 0 domain user account, this article will focus on this configuration. By default, domain user accounts do not have any permissions on private keys in the computer certificate store. Therefore, you must grant READ permissions to the NDES service certificate private keys either manually or in the certificate template configuration. This can be configured on the Request Handling tab as we will see later in this article.

(Source) Certificate Templates to duplicate

Certificate templates include a flag that is hidden from the GUI and determines whether a template is treated as a user or a computer certificate template. If you are curious, the command certutil -ds -v “CEPEncryption” will make it visible. Look out for CT_FLAG_MACHINE_TYPE in the output. This distinction is important in our scenario because the Exchange Enrollment Agent (Offline Request) template does not include this flag. As a result, certificates based on this template can only be enrolled into the user certificate store. To ensure the new template replacing the Exchange Enrollment Agent (Offline Request) template supports enrollment into the computer certificate store, we use the Enrollment Agent (Computer) default template as the source template.

Subject and SAN for NDES Service Certificates

Subject/SAN can either be built from Active Directory or provided in the request.

a) Build (subject) from this Active Directory information (option 1 from above – using common autoenrollment)

Using Common Name and DNS name is common practice. Subject and/or SAN will simply include the NDES computer account name.

As this may not be appropriate in all scenarios, we also have option...

b) Supply (subject) in the request (option 2 from above – using key-based renewal)

While this option gives you the freedom of choosing a proper Enrollment Agent subject information and SAN, it comes at the price of some additional configuration requirements to allow secure and automatic renewal of NDES service certificates. Using the key based renewal feature, we will have to initially enroll the NDES service certificates manually. Renewal will happen automatically. To implement this, both NDES service certificate templates must be configured as described below:

Subject Name tab

Issuance Requirements tab

Extensions tab

“Certificate Request Agent” is the only Application Policy required.

Please note that “Client Authentication” is required as an additional Application Policy in case you use CEP and CES for key based renewal.

Request Handling and Security tab

NDES EA Certificate Template Configuration Summary

Default NDES service certificate template	CEP Encryption	Exchange Enrollment Agent (Offline Request)
Template to duplicate	CEP Encryption	Enrollment Agent (Computer)
Compatibility settings	Certification Authority: Windows Server 2016 Certificate recipient: Windows 10/Windows Server 2016
General	Provide a name for the new certificate template.
Request Handling	In case the SCEP AppPool is configured to run in the security context of a domain account, you must grant READ access to the private key to the NDES service account. Otherwise, no changes are required on this tab.
Cryptography	If available, configure an HSM backed CSP or adjust the key length as required. Note that Key Storage Providers (KSPs) are not supported for NDES service certificates.
Subject Name	Either choose Build from this Active Directory information or choose Supply in the request + key-based renewal.
Issuance Requirements	Because of NDES’ Enroll-on-behalf capability described above, the NDES service certificates are very powerful. We therefore recommend enforcing CA certificate manager approval for enrollment. Please keep in mind that this will interrupt the automatic renewal process of the certificate if not using KBR.
Extensions	The default Application Policy is Certificate Request Agent. Do not change it. In case key based renewal is enabled, Client Authentication must be added as an Application Policy.
Security	Grant ENROLL and AUTOENROLL* permissions to the NDES Computer account only. * Autoenrollment only makes sense for option a - Autoenrollment

Housekeeping

After new EA certificates have been enrolled…

Unassign version 1 certificate templates from all CAs
Revoke all previously issued NDES EA certificates and remove them from NDES server.
Restart NDES service (execute to reload the web service and certificates)

Final Thoughts

NDES Enrollment Agent certificates are highly privileged and should never rely on legacy version 1 templates. Replacing them with custom templates that support HSMs and secure automatic renewal significantly reduces outage risk and closes known security gaps.

"Method 'Calculation' of object '_Application' failed" error occurs on unpredictable attempts

Kendethar — Mon, 09 Mar 2026 07:23:45 GMT

At random times, this save code decides to spit the "Method 'Calculation' of object '_Application' failed" error. It doesn't happen on any other userforms. Any idea why?

Private Sub CommandButton2_Click() 'Save 'Initial: On Error Resume Next Dim rng As Range, cell As Range Dim first_DB_avail_row As Range Dim Highest_Version_Row As Long Dim existingVersions() As String Dim ver_find As Variant Dim ver_list As Object: Set ver_list = CreateObject("System.Collections.ArrayList") 'Use an ArrayList for version sorting Dim padded_list As Object: Set padded_list = CreateObject("System.Collections.ArrayList") 'Create a temporary list for padded versions to ensure order (e.g., 5.1.28 > 5.1.2) Dim v As Variant, parts As Variant Dim padded_v As String, leadChar As String, all_vers As String Dim i As Integer 'Validate entries: If Me.Caption = "First Version - Business Manager" Then 'Adding product - first version If Me.ComboBox1.Value = "" Or Me.TextBox1.Value = "" Or _ Me.TextBox2.Value = "" Or Me.TextBox3.Value = "" Then 'Check if version is not inputted MsgBox "You must complete all fields.", vbExclamation, "Business Manager" GoTo MEM_CLEAN End If Insert_Product.ver_val = stage_entry & Major & Minor & Patch Unload Me Insert_Product.new_product_ver_cancel = False 'Set back to false from the terminate sub setting GoTo MEM_CLEAN End If Call Find_Latest_Ver 'Get the current latest version If stage_entry & Major & Minor & Patch = Highest_Version Then 'Check if version already exists MsgBox "This version already exists (as the newest version).", vbExclamation, "Business Manager" GoTo MEM_CLEAN End If existingVersions = Split(Replace(Me.TextBox4.Value, vbCrLf, ""), "• ") For Each ver_find In existingVersions If Trim(ver_find) = Trim(stage_entry & Major & Minor & Patch) Then MsgBox "This version already exists.", vbExclamation, "Business Manager" GoTo MEM_CLEAN End If Next ver_find If Me.ComboBox1.Value = "" Or Me.TextBox1.Value = "" Or _ Me.TextBox2.Value = "" Or Me.TextBox3.Value = "" Then 'Check if version is not inputted MsgBox "You must complete all fields.", vbExclamation, "Business Manager" GoTo MEM_CLEAN End If Me.Hide 'This will preserve public variables, keeping the form loaded, while still allowing the PLZ_WAIT userForm to display (no modal error) 'Macro Enhancement - Start: Application.Calculation = xlCalculationManual ActiveWorkbook.UpdateRemoteReferences = False Application.EnableEvents = False 'This must be false Application.ScreenUpdating = False Application.Interactive = False Application.DisplayAlerts = False Application.DisplayStatusBar = False PLZ_WAIT.Show PLZ_WAIT.Label2.Caption = "Setting new version" DoEvents 'Allows the PLZ_WAIT userForm to display If Err.Number <> 0 Then 'For some unknown reason, the Excel error "Method 'Calculation' of object '_Application' failed" occurs on unpredictable/unrepeatable attempts to save (sub runs) - cause unknown MsgBox "An Excel error occured (""" & Err.Description & """: " & Err.Number & "). Please try again (until it works).", vbExclamation, "Business Manager" GoTo MEM_CLEAN End If 'Pull data from the latest version: ThisWorkbook.Sheets("Products").Unprotect Password:=ThisWorkbook.Sheets("Background Data").Range("CY39").Value For Each cell In ThisWorkbook.Sheets("Background Data").Range("E4:E7503") If cell.Value = ThisWorkbook.Sheets("Products").Range("E" & Selection.Row).Value Then If cell.Offset(0, -2).Value = Highest_Version Then ThisWorkbook.Sheets("Products").Range("B" & Selection.Row).Value = cell.Offset(0, -3).Value 'Name ThisWorkbook.Sheets("Products").Range("C" & Selection.Row).Value = stage_entry & Major & Minor & Patch 'Product Version ThisWorkbook.Sheets("Products").Range("D" & Selection.Row).Value = cell.Offset(0, -1).Value 'File ThisWorkbook.Sheets("Products").Range("E" & Selection.Row).Value = cell.Value 'ID Number ThisWorkbook.Sheets("Products").Range("F" & Selection.Row).Value = cell.Offset(0, 1).Value 'Category ThisWorkbook.Sheets("Products").Range("G" & Selection.Row).Value = cell.Offset(0, 2).Value 'Details (Description) ThisWorkbook.Sheets("Products").Range("K" & Selection.Row).Value = cell.Offset(0, 6).Value 'Release Date ThisWorkbook.Sheets("Products").Range("L" & Selection.Row).Value = cell.Offset(0, 7).Value 'Copyright Y/N button ThisWorkbook.Sheets("Products").Range("M" & Selection.Row).Value = cell.Offset(0, 8).Value 'Copyright Status ThisWorkbook.Sheets("Products").Range("N" & Selection.Row).Value = cell.Offset(0, 9).Value 'Year ThisWorkbook.Sheets("Products").Range("O" & Selection.Row).Value = cell.Offset(0, 10).Value 'Copyright Statement ThisWorkbook.Sheets("Products").Range("P" & Selection.Row).Value = cell.Offset(0, 11).Value 'Published Y/N button ThisWorkbook.Sheets("Products").Range("Q" & Selection.Row).Value = cell.Offset(0, 12).Value 'Publish Status (Date) ThisWorkbook.Sheets("Products").Range("R" & Selection.Row).Value = cell.Offset(0, 13).Value 'Web Link ThisWorkbook.Sheets("Products").Range("S" & Selection.Row).Value = cell.Offset(0, 14).Value 'Withdraw Date Highest_Version_Row = cell.Row Exit For End If End If Next cell 'Save new version to version database: Set first_DB_avail_row = ThisWorkbook.Sheets("Background Data").Range(ThisWorkbook.Sheets("Background Data").Range("C7506").End(xlUp).Offset(1, 0).Address) first_DB_avail_row.Offset(0, -1).Value = ThisWorkbook.Sheets("Products").Range("B" & Selection.Row).Value 'Name first_DB_avail_row.Value = ThisWorkbook.Sheets("Products").Range("C" & Selection.Row).Value 'Product Version first_DB_avail_row.Offset(0, 1).Value = ThisWorkbook.Sheets("Products").Range("D" & Selection.Row).Value 'File first_DB_avail_row.Offset(0, 2).Value = ThisWorkbook.Sheets("Products").Range("E" & Selection.Row).Value 'ID Number first_DB_avail_row.Offset(0, 3).Value = ThisWorkbook.Sheets("Products").Range("F" & Selection.Row).Value 'Category first_DB_avail_row.Offset(0, 4).Value = ThisWorkbook.Sheets("Products").Range("G" & Selection.Row).Value 'Details (Description) first_DB_avail_row.Offset(0, 8).Value = ThisWorkbook.Sheets("Products").Range("K" & Selection.Row).Value 'Release Date first_DB_avail_row.Offset(0, 9).Value = ThisWorkbook.Sheets("Products").Range("L" & Selection.Row).Value 'Copyright Y/N button first_DB_avail_row.Offset(0, 10).Value = ThisWorkbook.Sheets("Products").Range("M" & Selection.Row).Value 'Copyright Status first_DB_avail_row.Offset(0, 11).Value = ThisWorkbook.Sheets("Products").Range("N" & Selection.Row).Value 'Year first_DB_avail_row.Offset(0, 12).Value = ThisWorkbook.Sheets("Products").Range("O" & Selection.Row).Value 'Copyright Statement first_DB_avail_row.Offset(0, 13).Value = ThisWorkbook.Sheets("Products").Range("P" & Selection.Row).Value 'Published Y/N button first_DB_avail_row.Offset(0, 14).Value = ThisWorkbook.Sheets("Products").Range("Q" & Selection.Row).Value 'Publish Status (Date) first_DB_avail_row.Offset(0, 15).Value = ThisWorkbook.Sheets("Products").Range("R" & Selection.Row).Value 'Web Link first_DB_avail_row.Offset(0, 16).Value = ThisWorkbook.Sheets("Products").Range("S" & Selection.Row).Value 'Withdraw Date 'Save Development Status Data to new version from latest version (copy over): first_DB_avail_row.Offset(0, 17).Value = ThisWorkbook.Sheets("Background Data").Range("T" & Highest_Version_Row).Value 'Title first_DB_avail_row.Offset(0, 18).Value = ThisWorkbook.Sheets("Background Data").Range("U" & Highest_Version_Row).Value 'Tags first_DB_avail_row.Offset(0, 19).Value = ThisWorkbook.Sheets("Background Data").Range("V" & Highest_Version_Row).Value 'Content first_DB_avail_row.Offset(0, 20).Value = ThisWorkbook.Sheets("Background Data").Range("W" & Highest_Version_Row).Value 'Total Tasks first_DB_avail_row.Offset(0, 21).Value = ThisWorkbook.Sheets("Background Data").Range("X" & Highest_Version_Row).Value 'Complete Tasks first_DB_avail_row.Offset(0, 22).Value = ThisWorkbook.Sheets("Background Data").Range("Y" & Highest_Version_Row).Value 'Platform first_DB_avail_row.Offset(0, 23).Value = ThisWorkbook.Sheets("Background Data").Range("Z" & Highest_Version_Row).Value 'Medium first_DB_avail_row.Offset(0, 24).Value = ThisWorkbook.Sheets("Background Data").Range("AA" & Highest_Version_Row).Value 'Framework first_DB_avail_row.Offset(0, 25).Value = ThisWorkbook.Sheets("Background Data").Range("AB" & Highest_Version_Row).Value 'Stage first_DB_avail_row.Offset(0, 26).Value = ThisWorkbook.Sheets("Background Data").Range("AC" & Highest_Version_Row).Value 'Dev Log (1) first_DB_avail_row.Offset(0, 102).Value = ThisWorkbook.Sheets("Background Data").Range("DA" & Highest_Version_Row).Value 'Dev Log (2) first_DB_avail_row.Offset(0, 103).Value = ThisWorkbook.Sheets("Background Data").Range("DB" & Highest_Version_Row).Value 'Dev Log (3) first_DB_avail_row.Offset(0, 104).Value = ThisWorkbook.Sheets("Background Data").Range("DC" & Highest_Version_Row).Value 'Dev Log (4) first_DB_avail_row.Offset(0, 105).Value = ThisWorkbook.Sheets("Background Data").Range("DD" & Highest_Version_Row).Value 'Dev Log (5) first_DB_avail_row.Offset(0, 106).Value = ThisWorkbook.Sheets("Background Data").Range("DE" & Highest_Version_Row).Value 'Dev Log (6) first_DB_avail_row.Offset(0, 107).Value = ThisWorkbook.Sheets("Background Data").Range("DF" & Highest_Version_Row).Value 'Dev Log (7) first_DB_avail_row.Offset(0, 27).Value = ThisWorkbook.Sheets("Background Data").Range("AD" & Highest_Version_Row).Value 'Total Bugs first_DB_avail_row.Offset(0, 28).Value = ThisWorkbook.Sheets("Background Data").Range("AE" & Highest_Version_Row).Value 'Resolved Bugs first_DB_avail_row.Offset(0, 29).Value = ThisWorkbook.Sheets("Background Data").Range("AF" & Highest_Version_Row).Value 'Total Requests first_DB_avail_row.Offset(0, 30).Value = ThisWorkbook.Sheets("Background Data").Range("AG" & Highest_Version_Row).Value 'Complete Requests first_DB_avail_row.Offset(0, 31).Value = ThisWorkbook.Sheets("Background Data").Range("AH" & Highest_Version_Row).Value 'Start Date first_DB_avail_row.Offset(0, 32).Value = ThisWorkbook.Sheets("Background Data").Range("AI" & Highest_Version_Row).Value 'End Date first_DB_avail_row.Offset(0, 33).Value = ThisWorkbook.Sheets("Background Data").Range("AJ" & Highest_Version_Row).Value 'Total Work Days first_DB_avail_row.Offset(0, 34).Value = ThisWorkbook.Sheets("Background Data").Range("AK" & Highest_Version_Row).Value 'Lines of Code first_DB_avail_row.Offset(0, 35).Value = ThisWorkbook.Sheets("Background Data").Range("AL" & Highest_Version_Row).Value 'Number of Features/Amenities first_DB_avail_row.Offset(0, 36).Value = ThisWorkbook.Sheets("Background Data").Range("AM" & Highest_Version_Row).Value 'Ease of Use first_DB_avail_row.Offset(0, 37).Value = ThisWorkbook.Sheets("Background Data").Range("AN" & Highest_Version_Row).Value 'Innovation/Uniqueness first_DB_avail_row.Offset(0, 38).Value = ThisWorkbook.Sheets("Background Data").Range("AO" & Highest_Version_Row).Value 'Complexity first_DB_avail_row.Offset(0, 39).Value = ThisWorkbook.Sheets("Background Data").Range("AP" & Highest_Version_Row).Value 'Optimization first_DB_avail_row.Offset(0, 40).Value = ThisWorkbook.Sheets("Background Data").Range("AQ" & Highest_Version_Row).Value 'Customer Request/Cater 'Set version list: Set rng = ThisWorkbook.Sheets("Background Data").Range("E4:E7503") ver_list.Add stage_entry & Major & Minor & Patch 'Add initial version For Each cell In rng 'Loop to add matches - Collect all versions If cell.Value = ThisWorkbook.Sheets("Products").Range("E" & Selection.Row).Value Then ver_list.Add cell.Offset(0, -2).Value End If Next cell 'Temporarily convert each version into sortable key (000.000.000) For i = 0 To ver_list.Count - 1 v = ver_list(i) leadChar = Left(v, 1) parts = Split(Mid(v, 2), ".") padded_v = leadChar padded_v = padded_v & Right("000" & parts(0), 3) padded_v = padded_v & Right("000" & parts(1), 3) padded_v = padded_v & Right("000" & parts(2), 3) ver_list(i) = padded_v & "|" & v 'Store padded key + original version 'Note: This converts, for example, "V54.17.44" to "V054017044" in order to sort, for each version (i) Next i 'Sort (descending) then strip padded key: ver_list.Sort: ver_list.Reverse For i = 0 To ver_list.Count - 1 ver_list(i) = Split(ver_list(i), "|")(1) Next i 'Note: This sorts then reverses the sort for highest version to be on top. Since sorting is left-to-right, major number will sort first, then minor, _ then patch, in that order. For the release, order will be A then B then V, since that's the alphabet's order, then it's reversed causing the order to be V then B then A. _ Basically, it is sorted lexicographically (V > B > A) then numerically (000000000), then reversed for descending order, then converted back to versioning format. 'Set validation: all_vers = " ," & Join(ver_list.ToArray, ",") 'Join all in array into one string and add initial blank option (for adding new when selected), for setting validation With Selection.Validation .Delete .Add Type:=xlValidateList, AlertStyle:=xlValidAlertStop, Operator:= _ xlBetween, Formula1:=all_vers .IgnoreBlank = True .InCellDropdown = True .InputTitle = "" .ErrorTitle = "" .InputMessage = "" .ErrorMessage = "" .ShowInput = False .ShowError = False End With ThisWorkbook.Sheets("Products").Protect Password:=ThisWorkbook.Sheets("Background Data").Range("CY39").Value 'Macro Enhancement - End: Application.Calculation = xlCalculationAutomatic ActiveWorkbook.UpdateRemoteReferences = True Application.EnableEvents = True Application.ScreenUpdating = True Application.Interactive = True Application.DisplayAlerts = True Application.DisplayStatusBar = True 'Final: Unload Me 'This clears "Highest_Version" and all public variables ?....... Sheet2.UPDATE_DB_FORCE = True Application.Run "Sheet2.Worksheet_Change", Selection 'Necessary in order to update Pricing and CUS_PRO_RATINGS sheets with new version Sheet2.UPDATE_DB_FORCE = False 'Release variables/objects from memory: MEM_CLEAN: Unload PLZ_WAIT: Set PLZ_WAIT = Nothing Set rng = Nothing Set cell = Nothing Set first_DB_avail_row = Nothing ver_list.Clear: Set ver_list = Nothing Set padded_list = Nothing End Sub

プレミアムでSpotifyをMP3に変換する方法について質問がありますか？

Natrauss — Mon, 09 Mar 2026 06:57:05 GMT

最近、Spotifyでよく音楽を聴いているのですが、オフラインで自由に再生するために曲やプレイリストをMP3として保存したいと思っています。ただ、Spotifyの公式アプリではMP3形式で保存できないと聞きました。そこで、Spotify MP3 変換ができる方法やツールがあるのか知りたいです。

ネットで調べると、いくつかSpotify MP3 変換ツールやコマンドラインツールがあるようですが、どれが安全で使いやすいのか分かりません。Windows PCで使えるおすすめの方法やソフトがあれば教えてもらえないでしょうか？プレイリストをまとめてMP3に変換できる方法があれば特に知りたいです。

もし実際に使っているツールや、簡単な手順などがあればぜひ共有していただけると助かります。よろしくお願いします！

How can I screen mirror iPhone to Mac wirelessly without usb cable

Obaileyy — Mon, 09 Mar 2026 06:24:32 GMT

My Mac has a built-in iPhone screen mirroring app but it does not work. This app requires iOS 18 and sign in with the same apple id. Unfortunately, my iPhone is still running an older iOS version. Because of that, the native option doesn't work for me.

What I want is a simple way to screen mirror my iPhone to mac wirelessly, mainly for viewing photos, demoing apps, and occasionally recording the screen. I would prefer something that works over Wi-Fi instead of using a Lightning/USB cable. I heard that AirPlay receivers might work, but not sure which option is the easiest and most stable in 2026.

macOS Catalina dmg file download for old macbook pro

Keithyes — Mon, 09 Mar 2026 05:53:48 GMT

Got a refurbished macbook pro late 2015. The best maos version for this old macbook pro is Catalina. I need to download the dmg file to make a bootable drive for reinstalling Catalina on my mac.

I tried two One Drive links but not working anymore. I guess it is due to network limit as the file is quite big. Where can I safely download macos catalina dmg file on a PC?

Stop Burning Money in Azure Storage

Sabyasachi-Samaddar — Mon, 09 Mar 2026 03:19:14 GMT

Audience: Engineers, Architects, FinOps teams (and anyone whose finance team sends "friendly" cost emails)

Your blobs called. They want to talk about your spending habits.

Look, we've all been there. You spin up a storage account, dump everything into Hot tier, and walk away feeling productive. Six months later, your finance team sends you a cost report that looks like a phone number.

Let's fix that — without a 47-page whitepaper.

────────────────────────────────────────────────────────────

1. Not Everything Deserves the Hot Tier

Hot tier is like first-class on a flight. Great for things that actually fly often. But that compliance PDF from 2019? It doesn't need a window seat and champagne.

Azure offers five access tiers — each with a different storage vs. access cost trade-off:

Tier	Optimized For	Storage Cost	Access Cost	Min Retention
Hot	Frequently accessed/modified data	Highest	Lowest	None
Cool	Infrequently accessed data	Lower	Higher	30 days
Cold	Rarely accessed, fast retrieval needed	Even lower	Even higher	90 days
Archive	Rarely accessed, flexible latency (hours)	Lowest	Highest	180 days
Smart	Unknown/variable patterns	Auto-optimized	Auto-optimized	None

Rule of thumb: If you have to search for it, it probably shouldn't live in Hot.

2. Upload to the Right Tier from Day One

Uploading to Hot and then moving to Cool is like buying a first-class ticket and then asking to switch to economy after takeoff. You still paid for first class.

When you change the tier of a blob after upload, you pay:

Write cost to the initial tier (when you upload)
Write cost to the new tier (when you re-tier)
Interim storage cost while the blob sits in Hot waiting for the move

Upload directly to the tier that matches the data's actual use. For bulk offline data movement, use Azure Data Box. Your wallet will thank you.

3. Smart Tier: For Those Who Don't Want to Think About It

Not sure where your data belongs? Don't want to build rules? Meet Smart Tier — Azure's "I'll handle it" option.

Data starts in Hot
Idle for 30 days? → Auto-moves to Cool
Idle for 90 days? → Cold
Someone reads it? → Boom, back to Hot. No penalties.

No early-delete fees. No transition charges. Just a tiny monitoring fee ($0.04 per 10K objects). It's like hiring a very cheap, very efficient intern to organize your storage closet.

4. Smart Tier vs. Lifecycle Management — The Showdown

"Should I use Smart Tier or Lifecycle Management?" — Every storage planning meeting, ever.

Both help you save money. Both move data to cooler tiers. But they're fundamentally different tools for different mindsets. Here's the cage match:

Aspect	Smart Tier	Lifecycle Management
How it works	Automatic, per-object, based on actual access	Rule-based — you define conditions
Setup effort	Enable once at account level. Zero rules.	Author, test, maintain JSON policies
Tier transitions	Hot→Cool (30d) →Cold (90d) — fixed	You choose any thresholds + Archive
Auto-rehydrate to Hot?	✅ Yes — on access, restarts cycle	Only with specific rule config
Archive tier?	❌ No — Hot/Cool/Cold only	✅ Yes
Early deletion penalties	❌ None	✅ Cool 30d, Cold 90d, Archive 180d
Tier transition charges	❌ None within Smart Tier	✅ Set Blob Tier API cost per move
Data retrieval charges	❌ None	✅ Standard Cool/Cold/Archive rates
Monitoring fee	$0.04 per 10K objects/month (>128 KiB)	Free — no policy cost
Control granularity	None — fixed thresholds	Full — custom thresholds, prefixes, tags
Auto-delete expired data?	❌ No	✅ Yes
Versions & snapshots	Not separately managed	Can tier/delete independently

When to Choose Smart Tier

Access patterns are unpredictable or unknown
You want zero management overhead — no rules to write or maintain
You don't need Archive tier
Data frequently bounces between active and inactive states
You prefer a flat monitoring fee over per-transition charges

When to Choose Lifecycle Management

You need Archive tier for long-term cold data
You want custom thresholds (e.g., tier to Cool after 7 days, not 30)
You need to auto-delete old blobs, versions, or snapshots
You want fine-grained scoping with blob index tags or prefix filters
Access patterns are well-understood and predictable

Can You Use Both Together?

Yes — but lifecycle management policies don't affect Smart Tier objects. They operate on different blob populations: Smart Tier manages blobs on the default account tier (no explicit tier set), while lifecycle policies target blobs with explicitly set tiers or specific filters.

Cost Example: 1 Million Objects (> 128 KiB)

	Smart Tier	Lifecycle Management
Monthly management cost	~$4 (monitoring fee)	$0 (policies are free)
Tier transition charges	$0	Per-transaction Set Blob Tier costs
Early deletion risk	None	Prorated penalty if moved before min retention
Retrieval charges	None	Standard Cool/Cold/Archive rates

Bottom line: For unpredictable workloads, Smart Tier's flat fee often wins. For well-understood patterns needing Archive or auto-delete, lifecycle policies give more control.

5. Lifecycle Management — Your Cost Autopilot

If Smart Tier is "set and forget," lifecycle management is "I have a spreadsheet and I'm not afraid to use it."

You write rules like:

"Move to Cool after 15 days"
"Move to Cold after 60 days"
"Archive after 180 days"
"Delete after 365 days" (Marie Kondo would approve)
"Delete previous blob versions after 90 days"

It's free to set up. You pay only for the tier transitions. And it supports Archive tier — something Smart Tier doesn't touch.

What Lifecycle Management Can Do

Capability	Description
Auto-tier current versions	Move blobs to cooler tiers if not accessed/modified for N days
Auto-tier previous versions & snapshots	Same rule-based tiering for versions and snapshots
Auto-rehydrate on access	Move blobs back from Cool to Hot when accessed
Auto-delete	Delete blobs, versions, or snapshots at end of lifecycle
Scoped rules	Apply to entire account, containers, or subsets via prefixes / blob index tags

Limitations to Know

Tiering is only for block blobs (convert append/page blobs first)
Cannot rehydrate blobs via lifecycle (rehydration is separate)
Cannot tier blobs with encryption scopes to Archive
Delete actions don't work on blobs in immutable containers
Max 10 prefixes and 10 tag conditions per rule
Changes take up to 24 hours to go into effect

6. Pack Small Files Before Moving to Cooler Tiers

Every blob operation has a per-transaction cost. One million tiny files = one million tiny charges that add up to one large headache.

ZIP or TAR small files before uploading to cooler tiers
Fewer files = fewer transactions = fewer sad finance emails
Keep an index file in Hot tier so you can find things without unpacking the whole archive like it's grandma's attic

Impact is especially significant for Archive tier, where per-operation costs are highest.

7. Turn On the Lights (a.k.a. Monitoring)

You can't optimize what you can't see.

Enable blob inventory reports — Know what you have, where it lives
Enable last access time tracking — Know what's actually being used — required for access-time lifecycle rules
Analyze with Azure Synapse or Databricks — Find idle data hiding in expensive tiers

This is the "check your bank statement" step. Boring? Yes. Effective? Absolutely.

8. Don't Forget About Append and Page Blobs

Append blobs (log files) and page blobs (disk backups/snapshots) that are no longer actively used can benefit from cooler tiers too. But there's a catch:

You must convert them to block blobs first before tiering.

Without conversion, they stay in Hot regardless of usage. It's like paying rent on an apartment you moved out of three years ago.

9. Early Deletion: The Penalty Box

Moving or deleting blobs before the minimum retention period incurs prorated charges. Know the rules before you move:

Tier	Minimum Retention	Penalty Example
Cool	30 days	Delete after 21 days → charged for remaining 9 days
Cold	90 days	Move after 60 days → charged for remaining 30 days
Archive	180 days	Delete after 45 days → charged for remaining 135 days

Smart Tier eliminates these penalties entirely. Lifecycle management does not. Choose wisely.

The Cost Optimization Checklist

If you skipped straight here — welcome. Here's the whole blog in one table:

#	Do This	Save This
1	Upload to the right tier from the start	Double-write costs
2	Enable Smart Tier for unpredictable data	Management time + penalty fees
3	Set up lifecycle policies for known patterns	30–70% on idle data
4	Pack small files before archiving	Transaction cost explosion
5	Enable blob inventory + access-time tracking	Future you will be grateful
6	Convert append/page blobs to block blobs	Unlock tiering for all blob types
7	Review default account access tier	Match default to dominant workload
8	Monitor early deletion penalties	Avoid unnecessary charges
9	Use Azure Storage Actions for multi-account	Scale optimization across accounts
10	Periodically re-analyze and adjust	Adapt to changing usage patterns

Final Thought

Azure Storage is incredibly powerful and flexible. But "flexible" also means "will happily let you store 10 TB in Hot tier that nobody's looked at since the last World Cup."

Don't be that person. Tier wisely. Automate ruthlessly. And maybe buy your finance team a coffee — they've been through a lot.

────────────────────────────────────────────────────────────

References

Best practices for using blob access tiers
https://learn.microsoft.com/en-us/azure/storage/blobs/access-tiers-best-practices
Access tiers for blob data
https://learn.microsoft.com/en-us/azure/storage/blobs/access-tiers-overview
Optimize costs with smart tier
https://learn.microsoft.com/en-us/azure/storage/blobs/access-tiers-smart
Lifecycle management overview
https://learn.microsoft.com/en-us/azure/storage/blobs/lifecycle-management-overview
Manage and find data with blob index tags
https://learn.microsoft.com/en-us/azure/storage/blobs/storage-manage-find-blobs
Block Blob pricing
https://azure.microsoft.com/pricing/details/storage/blobs/

Microsoft 365 Copilot can replace Power BI optimization experts today

Greg_Beaumont — Mon, 09 Mar 2026 03:14:18 GMT

Slow and inefficient Power BI reports are a widespread cause of low customer satisfaction, bloated costs, wasted compute capacity, and increased total cost of ownership. Fixing these issues often requires a significant time investment and a high level of domain expertise in business intelligence data modeling, Power Query ETL design, and DAX runtime expression optimization.

But what if you could save thousands of dollars in person-hours (often spent on external consultants) by using Microsoft 365 Copilot to analyze your Power BI Desktop files and generate a step-by-step mitigation plan for slow and inefficient reports? What if optimization tasks that used to take days could now be completed in minutes or hours?

In this video, I’ll walk through my methodology for enabling M365 Copilot to read your Power BI Desktop files, produce a consultant-grade 20‑page assessment, and deliver a clear, actionable mitigation plan in less than 20 minutes. With this approach, a resource with intermediate Power BI skills can resolve issues in a few hours that previously required days or more of expert-level effort.

All you need to follow along today is M365 Copilot and Power BI Desktop. Everything described in this article is generally available now, and no additional licensing is required beyond M365 Copilot.

For the example in my video (embedded below), I created a very poorly architected Power BI solution and experienced the following improvements:

Semantic Model and Power Query refresh time reduced by 45%
Semantic Model (Import Mode) size reduced by 79%
Report visualization rendering times reduced by 25%
Assessment and mitigation guide that would’ve taken 16-40 hours reduced to 15 minutes
All changes made in two hours (may have taken an additional 16-40 hours without M365 Copilot guide)

Some of you might ask why I’m not using the new Power BI MCP server capabilities. While powerful, setting up an MCP server requires a high level of technical aptitude and dedicated infrastructure. These resources are simply not available to many Power BI developers.

In contrast, fixing slow Power BI reports using M365 Copilot requires no additional licensing beyond M365 Copilot itself. All sensitive metadata remains within the customer’s tenant, and no advanced coding skills are required beyond standard Power BI development. An organization with hundreds of Power BI developers can realistically ask each developer to apply this method to optimize their own reports.

For my end-to-end demo and tutorial on using M365 Copilot to fix a slow and inefficient Power BI report, you can view my video below:

Step-by-step instructions and copies of the prompts can be found at my blog: Use M365 Copilot to improve Power BI semantic models, power query, and report speed – Greg Beaumont's Data & Analytics Blog

Multiple Tabs Added to Incorrect Workspace

John_Samuels — Mon, 09 Mar 2026 02:42:28 GMT

I have 4 workspaces, reserved for different projects. Several have pinned tabs that I frequently use. When I open the Edge icon on my taskbar, only the first workspace opens, and, without me requesting it, it has added about 20 tabs that I don't want, probably from another workspace. Also, Edge is not my default browser. How can I prevent these tabs from opening?

John

After clearing Edge cache, I lost multiple system settings...

WillBee — Mon, 09 Mar 2026 02:28:53 GMT

I cleared out the cache on my Edge browser and then restarted my computer. I do this occasionally, about monthly or so, previously without incident.

This time, after restarting the computer, the video settings are off so that part of the screen is cut-off all around the monitor, amounting to about half the Task bar at bottom and about half the Title bar in windows (such as Word, Excel, Edge, etc.) ALSO, my audio settings are lost so that I have no sound; it did not recognize the speakers on my TV, and I hear nothing. I went to the Sound System settings and had it look for new audio devices. It found the TV (which has speakers) but it says, "Windows doesn't have a network profile for this device." but it was ALL working fine before the cache clear and restart.

I then went and performed a Restore for earlier today (before the problem). That executed but there is no change.

I'm out of ideas and would appreciate anything you might suggest.

Modernizing On‑Prem File Servers: Azure Storage Mover and File Sync

SriniThumala — Sun, 08 Mar 2026 21:46:02 GMT

Azure File Sync is a hybrid cloud storage service that centralizes on-premises file shares into Azure Files while preserving the experience of a local Windows file server. It installs an agent on Windows Server(s) to cache and sync files to an Azure file share (the cloud backend). Key features include cloud tiering (keeping only hot files on-prem and tiering cold data to Azure) and multi-site synchronization, so changes propagate across servers via the cloud. In essence, Azure File Sync transforms your file server into a cache for Azure, providing continuous two-way sync between on-premises and the Azure file share.

Azure Storage Mover is a fully managed migration service used to transfer file data into Azure Storage (Azure Blob containers or Azure file shares) with minimal downtime. It works by deploying a migration agent near the source storage, which then copies data directly to Azure. The cloud-based Storage Mover resource orchestrates migrations (including initial bulk transfer and optional delta syncs for changes) across multiple shares from a central interface. Unlike File Sync, Storage Mover is not a continuous sync service but rather a one-directional data mover for scenarios like one-time migrations or periodic updates.

Side-by-Side Technical Comparison

Aspect	Azure File Sync	Azure Storage Mover
Primary Purpose	*Hybrid file service*: Ongoing two-way sync between on-prem file servers and Azure Files, enabling local caching and multi-site data sharing.	*Migration tool*: One-way transfers of file data from on-prem (or other storage) to Azure Storage, optimized for lift-and-shift migrations with minimal downtime.
Architecture	Agent on Windows Server connects local NTFS volumes to an Azure File Share (cloud endpoint). The Azure Storage Sync Service coordinates sync across servers in a sync group. Supports cloud tiering to offload cold files to cloud. Sync is continuous and multi-directional (all endpoints stay in sync).	Cloud service + on-prem agent: An Azure Storage Mover resource manages migration jobs. Lightweight migration agents (VMs or containers) run near your sources, sending data directly to the Azure target (Blob or File share). The service orchestrates project-based migrations but does not keep sources in sync after completion.
Supported Sources	Windows file servers (NTFS), accessed via SMB/NFS protocols (the agent needs Windows Server OS). Ideal for Windows-based file shares.	SMB shares, NFS exports, and similar file systems on any platform (Windows or Linux NAS). Also supports migrating from other clouds’ storage (e.g., S3 buckets) into Azure. Broad support for heterogeneous sources.
Supported Targets	Azure Files only. (Cloud endpoint is an Azure file share in a Storage Account). Supports SMB (and NFS 4.1 Azure file shares in preview) as target share types.	Azure Storage (Blob containers or Azure file shares). For example, can migrate into an Azure Blob (ADLS Gen2) container or an Azure File share depending on scenario.
Sync vs. Migration	Continuous Sync: Bi-directional; changes on-prem or in Azure propagate to all endpoints. Designed for long-term hybrid operation, not just a one-time move.	Batch Migration: One-time or repeated transfer; not a live sync. Typically used to move data entirely to Azure (cutover once done). Supports incremental (delta) syncs to capture changes between migration runs.
Performance & Scale	Scales to large datasets (tested up to 100 million files per sync group). Throughput can reach hundreds of files/sec for upload/download given sufficient resources. Performance depends on server hardware, network, and Azure Files limits.	Built to handle high-volume migrations (100M+ files). Can scale out by deploying multiple agents or using bigger VMs to increase throughput. Performance mainly limited by network bandwidth and source/target IOPS and can be optimized by parallel jobs and delta sync workflows.
Integration	Deep integration with Azure Files (the back-end store) and Windows Server. Works with Azure Backup for centralized backups or using file share snapshots. Leverages Azure’s redundancy (LRS/ZRS/GRS) for durability; supports Azure AD DS for identity integration to maintain ACLs in cloud.	Integrated with Azure Arc (for agent management) and can combine with Azure Data Box for hybrid migrations (offline + online phases). Managed using Azure Portal and CLI, provides logging/monitoring through Azure Monitor for migration jobs. No ongoing infrastructure after migration completes.

Advantages of Azure File Sync:

Hybrid Cloud Caching: Retains on-premises low-latency file access (via local Windows servers) while using Azure as central storage. End users and apps continue using a local file server interface.
Cloud Tiering & Storage Efficiency: Frees up local storage by tiering infrequently used files to Azure. This reduces on-prem disk usage without sacrificing access to full dataset (files are pulled from cloud on demand).
Multi-site Sync & Collaboration: Enables near-real-time sync across multiple servers/sites via Azure hub. Great for distributed teams sharing a common file set, replacing need for complex DFS-R setups or manual transfers.
Minimal Disruption Migration: Can be used as a no-downtime migration path to Azure Files – sync in background, then cut over clients to the Azure share with identical structure and ACLs.

Advantages of Azure Storage Mover:

Purpose-Built for Migration: Optimized for transferring data at scale into Azure. Can handle large one-time migrations or scheduled recurrent syncs without continuous agent overhead post-migration. Simplifies multi-terabyte or multi-site migration projects.
Heterogeneous Source Support: Works with a variety of source types (SMB, NFS shares on any OS) and can migrate into both Azure Files and Azure Blob, providing flexibility that Azure File Sync can’t (e.g., migrating Linux NFS servers or third-party storage to Azure).
Centralized Orchestration: Cloud architects can manage all migrations via a single Azure Storage Mover resource – with projects & jobs tracking progress per share. Logging, error handling, and coordination are unified, unlike scripting copy operations per server.
Incremental & Low Downtime: Supports delta synchronization to bring the target up-to-date after an initial bulk copy. This reduces cutover downtime since only last-minute changes need transferring. Also integrates with offline seeding (Data Box) plus online catch-up to minimize network strain and downtime.

Common Use Cases and When to Choose Each

Azure File Sync – Use Cases: Ideal when you need to maintain on-premises file server access while leveraging cloud storage. Some common scenarios:

Branch Office File Sharing: Multiple offices each have a local file server, all synced to a central Azure Files share. Users get fast local access, and the cloud ensures each site’s data stays consistent.
File Server Augmentation: You want to extend an existing Windows file server with virtually unlimited cloud capacity (via tiering) rather than fully moving to cloud. Azure File Sync offloads old data and provides cloud backup, but users and apps continue as normal with the local server.
Gradual Migration / Testing: You plan to eventually migrate to Azure Files but want a seamless, no-downtime transition. Deploy AFS on the server, let it sync all data to cloud, then optionally eliminate the on-prem server later. This way, users never stop accessing their files during the migration process.

Azure Storage Mover – Use Cases: Best when you have a defined migration project to move file shares to Azure, especially for heterogeneous environments or large volumes:

Data Center Exit / Large File Share Migration: Moving tens or hundreds of TBs of data from on-prem NAS or file servers to Azure Storage as a one-time project. The Storage Mover’s robust scalability and delta-sync capabilities help ensure a smooth transfer with minimal final cutover downtime.
Consolidating Cross-Platform Data to Azure: If you need to migrate non-Windows file systems (Linux NFS, etc.) or even data from other clouds into Azure, Storage Mover supports those sources out-of-the-box. For example, migrating a Linux file repository into Azure Blob for big data analytics.
Recurring Scheduled Migrations: In cases where you periodically copy data from on-prem to Azure (e.g., monthly exports from a local system to cloud for archiving), Storage Mover can be run as needed and centrally monitored, without maintaining a constant sync infrastructure in between.

Conclusion

When to choose which: Use Azure File Sync if your goal is to keep using on-prem servers and need a hybrid solution for the foreseeable future, or if you require distributed caching and continuous sync for collaboration. It’s essentially part of your production architecture for hybrid cloud file storage. Conversely, choose Azure Storage Mover when you want to permanently migrate data into Azure (and possibly decommission on-prem storage), or when dealing with a one-off bulk transfer task. In summary, Azure File Sync is an ongoing hybrid file service, and Azure Storage Mover is a one-time (or scheduled) migration service. Both can complement your cloud strategy, but they address distinct scenarios in a cloud architect’s toolkit.

Understanding the New Task Manager in Windows 11 (Complete Guide)

JohnNaguib — Sun, 08 Mar 2026 13:37:12 GMT

If you’ve been using Windows for years, you probably rely on Task Manager whenever your computer slows down, an app freezes, or you simply want to see what is happening behind the scenes. With Windows 11, Microsoft has redesigned the Task Manager with a modern interface, better performance monitoring, and improved navigation.

While the core purpose remains the same monitoring system resources and managing running applications the new Task Manager in Windows 11 is more powerful and easier to use once you understand its layout and features.

https://dellenny.com/understanding-the-new-task-manager-in-windows-11-complete-guide/

Microservice Architecture Drawbacks and How to Solve Them: A Solution Architect’s Perspective

JohnNaguib — Sun, 08 Mar 2026 13:35:14 GMT

Microservice architecture has become one of the most popular approaches for building modern, scalable systems. Many organizations adopt it to improve scalability, enable independent deployments, and allow teams to move faster. However, after working on multiple large-scale systems, I can confidently say that microservices are not a silver bullet. They solve certain problems but introduce a new set of challenges that engineering teams must be prepared to handle.

https://dellenny.com/microservice-architecture-drawbacks-and-how-to-solve-them-a-solution-architects-perspective/

How to Speed Up Windows 11 Without Third-Party Tools (Complete Expert Guide)

JohnNaguib — Sun, 08 Mar 2026 13:31:34 GMT

Windows 11 is designed to be fast, modern, and efficient. However, over time many users notice their system becoming slower apps take longer to open, startup times increase, and overall performance drops. The good news is that you don’t need third-party optimization tools to fix this. Windows 11 already includes powerful built-in features that can dramatically improve performance if used correctly.

https://dellenny.com/how-to-speed-up-windows-11-without-third-party-tools-complete-expert-guide/

In Windows 11 25H2, File Explorer loses vertical maximum sizing.

kwikc — Sat, 07 Mar 2026 22:32:12 GMT

In Windows, when moving the cursor to the top or bottom edge of a window, the cursor becomes a bi-directional arrow shape. If double-clicking at the moment, the window changes the size at the vertical direction to the maximum screen height.

I installed Windows 11 25H2, the sizing function above was lost on File Explorer.

The bug report link: https://aka.ms/AA1058lx

Outlook contact groups

amazoit — Sat, 07 Mar 2026 20:20:55 GMT

Hi,

We use a specific m365 account for email blasts...and have 10 contact groups withing that account (and not a distro from m365), each contact group has a limit of 1000 contacts (limitation from microsoft), so we have 10 such groups as we send out to about 9000 people total (and microsoft has a limit of sending 10,000 emails per day per account).

Issue is a lot of times we get undeliverables because people in the groups either no longer work there or whatever...so its tough to clean up.

Is there a better way to do this?

1. For the contact groups itself? I thought a out creating a distro in m365 but then I have to add the 900p contacts into our tenant which would be annoying

2. Sending the blasts (without a crm or something like constant contact)

3.managing the lists from undeliverables

undeliverables

undeliverable

Thanks all.

The LLM Inference Optimization Stack: A Prioritized Playbook for Enterprise Teams

bobmital — Sun, 08 Mar 2026 15:43:12 GMT

The Solutions — An Optimization Stack for Enterprise Inference

The optimizations below are ordered by implementation priority — starting with the highest-leverage.

The Three-Layer Serving Stack

Most enterprise LLM deployments operate across three layers, each responsible for a different part of the inference pipeline. Understanding which layer a bottleneck belongs to is often the fastest path to improving inference performance.

Azure Kubernetes Service (AKS) orchestrates the infrastructure — GPU nodes, networking, and container lifecycle.
Ray Serve provides the distributed model serving layer — handling request routing, autoscaling, batching, replica placement, and multi-model serving.
Inference engines such as vLLM execute the model forward passes and implement token-generation optimizations such as continuous batching and KV-cache management.

In simple terms: AKS manages infrastructure. Ray Serve manages inference workloads. vLLM generates tokens

With that architecture in mind, we can examine the optimization stack.

1. GPU Utilization: Maximize What You Already Have

Before optimizing models or inference engines, start here: are you fully utilizing the GPUs you’re already paying for? For most enterprise deployments, the answer is no. GPU utilization below 50% means you’re effectively paying double for every token generated.

Autoscaling on inference-specific signals. Autoscaling should be driven by request queue depth, GPU utilization, and P95 latency — not generic CPU or memory metrics, which are poor proxies for LLM serving load. AKS supports GPU-enabled node pools with cluster autoscaler integration across NC-series (A100, H100) and ND-series VMs. Scale to zero during idle periods; scale up based on token-level demand, not container-level metrics.

Inference-aware orchestration. AKS orchestrates infrastructure resources such as GPU nodes, pods, containers. Ray Serve operates one layer above as the inference orchestration framework, managing model replicas, request routing, autoscaling, streaming responses, and backpressure handling, while inference engines like vLLM perform continuous batching and KV-cache management. The distinction matters because LLM serving load doesn't express well in CPU or memory metrics; Ray Serve operates at the level of tokens and requests, not containers.

AKS orchestrates infrastructure; Ray Serve orchestrates model serving. Anyscale Runtime reports faster performance and lower compute cost than self-managed Ray OSS on selected workloads, though gains depend on workload and configuration.

Right-sizing Azure GPU selection.

The default instinct when deploying GenAI in production is often to grab the biggest, fastest hardware available. For inference, that is often the wrong call. For structured output tasks, a well-optimized, quantized 7B model running on an NCads H100 v5 (H100 NVL 94GB) or an NC A100 v4 (A100 80GB) node can easily outperform a generalized 70B model on a full ND allocation—at a fraction of the cost. New deployments should target NCads H100 v5.

The secret to cost-effective inference is matching your VM SKU to your workload's specific bottleneck. For compute-heavy prefill phases or massive multi-GPU parallelism, the ND H100 v5's ultra-fast interconnects are unmatched. However, autoregressive token generation (decode) is primarily bound by memory bandwidth. For single-GPU, decode-heavy workloads, the NCads series is the better fit: the H100 NVL 94GB has higher published HBM bandwidth (3.9 TB/s) than the H100 80GB (3.35 TB/s).

ND H100 v5 remains the right choice when you need multi-GPU sharding, high aggregate throughput, or tightly coupled scale-out inference. You can extend utilization further with MIG partitioning to host multiple small models on a single NVL card, provided your application can tolerate the proportional drop in memory bandwidth per slice.

2. GPU Partitioning: MIG and Fractional GPU Allocation on AKS

For smaller models or moderate-concurrency workloads, dedicating an entire GPU to a single model replica wastes resources. Two techniques address this on AKS.

NVIDIA Multi-Instance GPU (MIG) partitions a single physical GPU into up to seven hardware-isolated instances, each with its own compute cores, memory, cache, and memory bandwidth. Each instance behaves as a standalone GPU with no code changes required. On AKS, MIG is supported on Standard_NC40ads_H100_v5, Standard_ND96isr_H100_v5, and A100 GPU VM sizes, configured at node pool creation using the --gpu-instance-profile parameter (e.g., MIG1g, MIG3g, MIG7g).

Fractional GPU allocation in Ray Serve is a scheduling and placement mechanism, not hardware partitioning. By assigning fractional GPU resources (say, 0.5 GPU per replica) through Ray placement groups, multiple model replicas can share a single physical GPU. Ray Serve propagates the configured fraction to the serving worker (i.e. vLLM), but, unlike MIG, replicas still share the same underlying GPU memory and memory bandwidth. There’s no hard isolation.

Because fractional allocation does not enforce hard VRAM limits, it requires careful memory management: conservative gpu_memory_utilization configuration, controlled concurrency and context length, and enough headroom for KV cache growth, CUDA overhead, and allocator fragmentation. It works best when model weights are relatively small, concurrency is predictable and moderate, and replica counts are stable.

For stronger isolation and guaranteed memory partitioning, use NVIDIA MIG. Fractional allocation is best treated as a GPU packing optimization, not an isolation mechanism.

3. Quantization: The Fastest Path to Cost Reduction

Quantization reduces the numerical precision of model weights, activations, and KV cache entries to shrink memory footprint and increase throughput. FP16 → INT8 roughly halves memory; 4-bit quantization cuts it by approximately 4×.

Post-Training Quantization (PTQ) is the fastest path to production gains. As one example, Llama-3.3-70B-Instruct reduces weight memory from ~140 GB in BF16 to ~70 GB in FP8, which can make single-GPU deployment feasible on an 80GB GPU for low-concurrency or short-context workloads. Production feasibility still depends on KV cache size, engine overhead, and concurrency, so careful capacity planning is required.

4. Inference Engine Optimizations in vLLM

Modern inference engines — particularly vLLM, which powers Anyscale’s Ray Serve on AKS — implement several optimizations that compound to deliver significant throughput improvements.

Continuous batching replaces static batching, where the system waits for all requests in a batch to complete before accepting new ones. With continuous batching, new requests join at every decode iteration, keeping GPUs more fully utilized. Anyscale has demonstrated up to 23x throughput improvement using continuous batching versus static batching (measured on OPT-13B on A100 40GB with varying concurrency levels). In practice, this can push GPU utilization from 30–40% to 80%+ on AKS GPU node pools.

PagedAttention manages KV cache allocation the way an operating system manages RAM — breaking it into small, non-contiguous pages to eliminate fragmentation. Naive KV cache allocation wastes significant reserved memory through internal and external fragmentation. PagedAttention eliminates this, enabling more concurrent requests per GPU. Enabled by default in vLLM.

Prefix caching automatically stores the KV cache of completed requests in a global on-GPU cache. When new requests share common prefixes — system prompts, shared context in RAG — vLLM reuses cached state instead of recomputing it, reducing TTFT and compute load. Anyscale’s PrefixCacheAffinityRouter extends this by routing requests with similar prefixes to the same replica, maximizing cache hit rates across AKS pods.

Chunked prefill breaks large prefill operations into smaller chunks and interleaves them with decode steps. Without it, a long incoming prompt can stall all ongoing decode operations. Chunked prefill keeps streaming responses smooth even when new long prompts arrive, and improves GPU utilization by mixing compute-bound prefill chunks with memory-bound decode. Enabled by default in vLLM V1.

Speculative decoding addresses the sequential decode bottleneck directly. A smaller, faster “draft” model proposes multiple tokens ahead; the larger “target” model verifies them in parallel in a single forward pass. When the draft predicts correctly — which is frequent for routine language patterns — multiple tokens are generated in one step. Output quality is identical because every token is verified by the target model. Particularly effective for code completion, where token patterns are highly predictable.

5. Disaggregated Prefill and Decode

Since prefill is compute-bound and decode is memory-bandwidth-bound, running both on the same GPU forces a compromise — the hardware is optimized for neither. Disaggregated inference separates these phases across different hardware resources.

vLLM supports disaggregated prefill and decode and Ray Serve can orchestrate separate worker pools for each phase. In practice, this means Ray Serve routes each incoming request to a prefill worker first, then hands off the resulting KV cache to a dedicated decode worker — without the application layer needing to manage that handoff. This capability is evolving and should be validated against your Ray and vLLM versions before deploying to production. With MIG or separate node pools, prefill and decode resources can be isolated to better match each phase’s hardware requirements.

Azure ND GB200 v6 VMs include four NVIDIA Blackwell GPUs per VM, while the broader GB200 NVL72 system enables rack-scale NVLink connectivity — providing the high-bandwidth GPU-to-GPU communication that disaggregated prefill/decode architectures depend on for KV-cache movement.

6. Multi-LoRA Adapters: Serve Many Use Cases from One Deployment

Fine-tuned Low-Rank Adaptation (LoRA) adapters for different domains can share a single base model in GPU memory, with lightweight task-specific layers swapped at inference time. Legal, HR, finance, and engineering copilots are served from one AKS GPU deployment instead of four separate ones.

This is a direct cost multiplier: instead of provisioning N separate model deployments for N departments, you provision one base model and swap adapters per request. Ray Serve and vLLM both support multi-LoRA serving on AKS.

Open-Source Models for Enterprise Inference

The open-source model ecosystem has matured to the point where self-hosted inference on open-weight models - running on AKS with Ray Serve and vLLM - is a viable and often preferable alternative to proprietary API access. The strategic advantages are significant: full control over data residency and privacy (workloads run inside your Azure subscription), no per-token API fees (cost shifts to Azure GPU infrastructure), the ability to fine-tune and distill for domain-specific accuracy, no vendor lock-in, and predictable cost structures that don’t scale with usage volume.

Leading Open-Source Model Families

Meta Llama (Llama 3.1, Llama 4) is the most widely adopted open-weight model family. Llama 3.1 offers dense models from 8B to 405B parameters; Llama 4 introduces MoE variants. Strong general-purpose performance with native vLLM integration. The 70B variant hits a reasonable quality-to-serving cost for most enterprise use cases. Available under Meta’s community license - Validate the specific model architecture and license you plan to use.

Qwen (Alibaba) excels in multilingual and reasoning tasks. Qwen3-235B is a MoE model activating roughly 22B parameters per token — delivering frontier-class quality at a fraction of dense-model inference cost. Strong on code, math, and structured output. Apache 2.0 license on most variants.

Mistral models are optimized for efficiency and inference speed. Mistral 7B remains one of the highest-performing models at its size class, making it well-suited for cost-sensitive, high-throughput deployments on smaller Azure GPU SKUs. Mixtral 8x22B provides MoE-based quality scaling. Mistral Large (123B) competes with frontier proprietary models. Licensing varies: most smaller models are Apache 2.0, while some larger releases use research or commercial licensing terms. Verify the license for the specific model prior to production deployment.

DeepSeek (DeepSeek AI) introduced aggressive MoE architectures with cost-efficient training. DeepSeek-V3 (671B total, 37B active per token) delivers strong reasoning quality at significantly lower per-token inference cost than dense models of comparable capability. Strong on math, code, and multilingual tasks. DeepSeek models are developed by a Chinese AI research lab. Organizations in regulated industries should evaluate applicable data sovereignty, export control, and vendor risk policies before deploying DeepSeek weights in production.

The examples below are illustrative starting points rather than fixed recommendations. Actual model and infrastructure choices should be validated against workload-specific latency, accuracy, and cost requirements.

Model Selection Examples

Workload	Recommended Model Class	Azure Infrastructure	Rationale
Internal copilots, high-throughput APIs	7B–13B (Llama 8B, Mistral 7B, Qwen 7B)	NCads H100 v5 with MIG, or NC A100 v4 (existing deployments)	10–30x cheaper serving; recover accuracy via RAG and fine-tuning
Customer-facing assistants	30B–70B (Llama 70B, Qwen 72B, Mistral Large)	NC A100 v4 (80GB – existing deployments ) or ND H100 v5	Quality directly impacts revenue and trust
Frontier quality at sub-frontier cost	MoE (Qwen3-235B-A22B, DeepSeek-V3, and Mistral’s Mixtral-family models)	ND H100 v5 or ND GB200-v6	Active parameters determine inference cost, not total model size
Code completion and engineering copilots	Code-specialized (DeepSeek-Coder, Qwen-Coder)	NCads H100 v5 with MIG	Domain models outperform larger general models at lower cost
Multilingual	Qwen, DeepSeek	Matches workload size above	Strongest non-English performance in open-weight ecosystem
Edge / on-device	Small edge-capable models (for example, 2B–8B-class models, often quantized)	Azure IoT Edge / local hardware	Fits within edge memory and power envelopes

The rule of thumb: start with the smallest model that meets your quality threshold. Add RAG, caching, fine-tuning, and batching before scaling model size. Treat model choice as an ongoing decision —the open-source ecosystem evolves fast enough that what’s optimal today may not be in six months. Actual performance varies by workload, so these model and size recommendations should be validated through testing in your target environment.

All leading open-weight models are natively supported by vLLM and Ray Serve / Anyscale on AKS, with out-of-the-box quantization, multi-GPU parallelism, and Multi-LoRA support.

The optimizations above assume a platform that is already secure, governed, and production-hardened. Continuous batching on an exposed endpoint is not a production system. Part three covers the architecture decisions, security controls, and operational metrics that make enterprise inference deployable — and auditable. Continue to Part 3: Building an Enterprise Platform for Inference at Scale →

Part 1: Inference at Enterprise Scale: Why LLM Inference Is a Capital Allocation Problem | Microsoft Community Hub
Part 3: (coming soon)

Available today: GPT-5.4 Thinking in Microsoft 365 Copilot

NitinAgrawal — Fri, 06 Mar 2026 22:29:40 GMT

Today, we’re bringing OpenAI’s GPT‑5.4 Thinking to Microsoft 365 Copilot and Microsoft Copilot Studio—available in addition to the recent GPT-5.3 Instant update. With GPT‑5.4 Thinking, Copilot can think deeper on complex work by combining advances in reasoning, coding, and agentic workflows—helping it work through technical prompts and longer tasks with higher-quality outputs, and less back-and-forth. Work IQ brings relevant work context into Copilot so it can reason, personalize, and help you turn deeper thinking into context-aware drafts, slides, and spreadsheets.

We are committed to bringing you the latest cutting-edge AI innovation and model choice built for work and tailored to your business needs—with the security, compliance, and privacy that you expect from Microsoft.

Get started today

GPT-5.4 Thinking is now available in Copilot Studio early release cycle environments and begins rolling out today to Microsoft 365 Copilot users with priority access and Microsoft 365 Copilot Chat users with standard access. Learn more about standard versus priority access here.

In Copilot Chat, you can select GPT‑5.4 Think deeper from the model selector under More, and in Copilot Studio you can select GPT‑5.4 Reasoning.

Our team will continue to refine the experience based on your feedback. Learn more about Microsoft 365 Copilot and Microsoft Copilot Studio and start transforming work with Copilot today. For model details, learn more about GPT-5.4 Thinking here.

For the latest research insights on the future of work and generative AI, visit WorkLab.