Tag Archives: microsoft
Manage Dev Tunnels with Group Policies
Dev Tunnels is a tunneling service that can boost your productivity when testing and debugging web apps, webhooks, APIs, and more. You can also simply use Dev Tunnels to seamlessly share your work with colleagues, demo at conferences, or cross-device app testing. Whether you use Dev Tunnels through Visual Studio, VS Code, or the devtunnel CLI, it takes mere seconds to get started.
We’ve heard from organizations that IT Administrators want to control certain aspects of Dev Tunnels to achieve consistency or compliance across their organization. In response to this feedback, we’re thrilled to announce that Dev Tunnels now supports this level of control with Group Policy Administrative Templates (ADMX/ADML files)!
To configure and deploy these policies, you can use the Group Policy Editor. These policies apply to Dev Tunnels in Visual Studio, port forwarding built into Visual Studio Code, the Visual Studio Code Remote – Tunnels extension, and the devtunnel CLI. These policies will be available in Microsoft Intune in the future as well.
Prerequisites
Windows Server 2016, Windows Server 2019, Windows Server 2022, Windows 8.1, Windows 10, Windows 11
Active Directory
Access to Group Policy Editor
Policies supported
Disable anonymous tunnel access: Disallow anonymous tunnel access. Enabling this policy enforces users to select either private or organization for tunnel access. This means users cannot connect to an existing tunnel with anonymous access control, host an existing tunnel with anonymous access control, or add anonymous access to existing or new tunnels.
Disable Dev Tunnels: Disallow users from using the Dev Tunnels service. All commands, with few exceptions, should be denied access when this policy is enabled. Exceptions: unset, echo, ping, and user.
Allow only selected Microsoft Entra tenant IDs: Users must authenticate within the given tenant list to access Dev Tunnels. When enabling this policy, multiple tenant IDs can be added by using a semicolon or comma to separate each. All commands, with few exceptions, should be denied access when this policy is enabled, and the user’s tenant ID isn’t in the list of allowed tenant IDs. Exceptions: unset, echo, ping, and user. Follow the steps in this article to find your Microsoft Entra tenant ID.
Configure policies with Local Group Policy editor
For machines within a corporate network, the Group Policy editor can be used to deploy Dev Tunnel policies.
Download the Administrator Template files
Download the Administrator Template files (ADMX/ADML) for Dev Tunnels from the Microsoft Download Center.
Navigate to the C:WindowsPolicyDefinitions folder and add the TunnelsPolicies.admx file.
Navigate to the C:WindowsPolicyDefinitionsen-US folder and add the TunnelsPolicies.adml file.
Apply the policies using the Local Group Policy Editor
Open Command Prompt and run gpupdate /force to ensure the policy files are configured.
Open the Windows Local Group Policy Editor.
Navigate to Computer Configuration > Administrative Templates > Dev Tunnels.
Apply the desired policy changes.
Contact us
If you have any feedback, feature requests, questions, or you encounter an unexpected issue while working with the devtunnel CLI, reach out to us. We want to hear from you!
GitHub issues is a great way to connect with us. You can open a new issue or up-vote any existing issues using a :thumbs_up: reaction to:
Request a feature
Submit a bug
Provide feedback
If you’re an enterprise looking to adopt dev tunnels in your organization with specific questions on security, enterprise management or support, email us at tunnelsfeedback@microsoft.com.
Microsoft Tech Community – Latest Blogs –Read More
Exploring Generative AI: An Hands-on Course on Prompt Engineering for non-tech students – Part 1
Introduction
Generative Artificial Intelligence (AI) has transformed the digital landscape through “intent-based outcome specification,” a paradigm where users describe desired outcomes via detailed prompts instead of traditional commands. This course – targeting a non-developer audience – delved into the foundational principles of Generative AI and Large Language Models (LLMs), focusing on their core mechanisms and capabilities. Students learned and practiced effective prompting techniques, essential for navigating this powerful yet complex method. The course included the analysis and discussion of recent research on prompt engineering, keeping students abreast of the latest developments.
The structure of the course balanced theoretical understanding and practical application, with 30% dedicated to traditional lectures and 70% to hands-on workshops and collaborative group projects. Practical exercises using models like GPT allowed students to apply their theoretical knowledge in real-world scenarios. Group projects focused on specific application domains – including music, literature and cuisine – leading to presentations, peer reviews, and instructor feedback.
The course – comprising 20 hours of direct instruction – was conducted at the Fondazione Bruno Kessler (FBK) campuses in Povo. Instructors Antonio Bucchiarone and Nadia Mana guided the learning journey. Additionally, Carlotta Castelluccio from Microsoft conducted a seminar on Responsible AI, emphasizing ethical considerations in AI applications.
In this first part of the blog series, we are going to present the methodological framework and the tools used throughout the course. In the second part, we are going to cover the student projects’ main outcomes and key takeaways.
The Card Model Template and the Flow of Cards
One of the primary goals of the course was to provide a clear and comprehensive understanding of prompt engineering. This was achieved by introducing a structured framework known as the “Card Model” to define and organize generative AI tasks. In the context of this course, a card refers to a structured format or template used to define a specific task or objective for generating content or output using generative AI techniques.
The Card Model serves as a conceptual framework that outlines the structure, components, and relationships involved in generating content or output using generative AI techniques. It provides a high-level abstraction of the task, capturing its essential elements and defining their interactions. Here’s a simplified model of a generative AI task:
Objective: This is the overarching goal or purpose of the generative AI task, defining what needs to be achieved through the content generation process.
Input: Information provided to the generative AI technique to guide the content generation process. This includes:
Prompt: A starting point or stimulus to generate content, such as a partial sentence, a question, an instruction, or other forms of input.
Context: Additional information or constraints that provide context for the generation task, such as background knowledge, relevant data sources, or specific requirements.
Generative Model: The AI model responsible for generating content based on the input provided. Examples include pre-trained language models like OpenAI GPT-3.5Turbo, neural network architectures for text generation, or other generative AI systems.
Output: The generated content produced by the generative model in response to the input, including:
Generated Text: The actual output, which could be in the form of text, images, or other media.
Evaluation Metrics: Criteria used to assess the quality and relevance of the generated content, including measures of coherence, relevance, fluency, and other factors depending on the specific task requirements.
Feedback Loop: A mechanism for iteratively improving the generative AI model based on feedback from users or evaluators. This may involve refining the input prompts, adjusting model parameters, or incorporating additional training data to enhance performance.
The Card Model helps define the key components involved in the task and their relationships, facilitating the design, execution, and evaluation of generative AI tasks in various applications.
Cards Flow Model
The concept of flow was also introduced in the course to provide a formal representation of the relationships between different cards composing a generative AI task. This flow model helps in visualizing and understanding the sequential and conditional transitions between different stages of a generative AI task, ensuring a structured and systematic approach to designing, executing, and evaluating generative AI processes.
In more details, cards can be combined together to create complex workflows by defining specific transitions and dependencies between them. By linking these cards through directed edges, students can create intricate flows that mirror real-world applications. This pattern also helps students to break down complex tasks into smaller subtasks, described through detailed prompts and potentially addressed by different specialized models, generally leading to a more accurate final outcome.
For example, a card flow might begin with a card that generates an initial story prompt. The output of this card could then flow into a card that adds contextual details, which in turn flows into another card responsible for generating the story based on the enhanced prompt. Subsequent cards could be used to evaluate the generated content, refine the prompt based on evaluation metrics, and iterate the process.
To ensure a thorough understanding of these flows, students were asked to evaluate different paths within a flow. This involved analyzing how changes in one card could affect the overall output and exploring alternative pathways to achieve the desired outcome. Students were tasked with:
Mapping Out Flows: Students mapped out various flows, identifying all possible paths and transitions between cards.
Evaluating Paths: They evaluated each path to understand how different sequences and combinations of tasks impacted the final output.
Comparing Outcomes: Students compared outcomes from different paths to determine which flow produced the most coherent, relevant, and high-quality results.
Feedback and Iteration: They incorporated feedback into their flows, refining cards and transitions to optimize the generative process.
By engaging in these activities, students gained hands-on experience in managing complex generative AI tasks, learning to anticipate and handle the dependencies and contingencies that arise in practical applications. This exercise not only reinforced their understanding of prompt engineering but also highlighted the importance of structured planning and iterative improvement in generative AI projects.
The Azure AI Proxy Playground
Students learned to interact with OpenAI models through the GUI offered by the Azure AI Proxy Playground. The service is an open-source solution which provides a Playground-like experience to explore the Azure OpenAI chat completions using a time-bound event code with different models and parameters. It’s designed for educational scenarios (e.g., a course, a hackathon, or a workshop) where students might not have access to an Azure subscription enabled with Azure OpenAI service and/or are not familiar with the Azure ecosystem and how to provision and consume Azure AI resources.
By leveraging this solution, we were able to provide students with a simplified lab environment, where all the complexity related to the Cloud resources provisioning and model deployments was hidden to the final user and managed through a single Azure subscription, connected to the Proxy Playground. This was particularly helpful in the context of a course whose audience was non-technical and whose focus was learning to interact with large language models through prompt engineering techniques. For the sake of the course, we provisioned a gpt-3.5 turbo instance, so all the students’ interactions via the playground happened with that specific model.
Tool GUI and Card Model mapping
The Playground GUI is composed of several elements. Most of them can be directly mapped with the Card Model components, ensuring consistency between the theoretical concepts and the actual experimentations.
User prompt: free-form text field used to enter the user request to the model. It’s the prompt component of the input in the card model.
System message: free-form text field used to enter additional information to use in responses, data sources and/or tone and style specifications. It maps with the context component of the input in the card model.
Configuration: parameters to tune the degree of randomness of the responses. It also includes a dropdown menu to select the model to use as chat engine, what we call generative model in the card template.
Assistant response: in the chat session the user can read the model’s response, aka the generated text component of the output in the card model.
Summary
In this article, we covered the methodological framework and tools used in the Prompt Engineering course at Fondazione Bruno Kessler, to teach non-tech students to effectively interact with generative AI models. We explored the “Card Model” – a structured approach to define and organize generative AI tasks – and the concept of the “flow”, which further structures the relationships between tasks, aiding in the creation of complex workflows.
Students utilized the Azure AI Proxy Playground, an open-source GUI, to interact with OpenAI models like GPT-3.5 Turbo, applying their theoretical knowledge in practical scenarios without needing extensive technical skills.
In the second part of the blog series, we will delve into the main outcomes of the students’ projects and the key takeaways from their practical applications.
Microsoft Tech Community – Latest Blogs –Read More
Using Azure’s AI Language Service to Summarize and Extract Themes from Interview Transcripts
Imagine you’re a program evaluator or a qualitative researcher tasked with analyzing hundreds of interview transcripts. Each transcript is filled with valuable information, but the sheer volume and time-consuming nature of the task can be overwhelming. You find yourself buried in a sea of words, desperately seeking a way to extract meaningful insights efficiently. This blog is meant to walk you into a solution by using Azure’s AI Language Service.
Introduction
In the realm of program evaluations and qualitative research, interview transcripts hold the key to understanding participants’ perspectives and experiences. However, manually sifting through these transcripts can be an arduous and time-intensive process. This is where Azure AI/ML steps in, offering a game-changing solution that automates the production of summaries and extraction of themes from interview transcripts. We will explore how to leverage Azure AI/ML to support program evaluations and qualitative research.
The Challenge of Summarization
Traditionally, summarizing interview transcripts is a time-consuming task, requiring meticulous reading and the ability to identify core themes and statements. This manual process is not only labor-intensive but also prone to inconsistencies.
Azure AI/ML to the Rescue
Azure AI/ML services provide a suite of tools designed to tackle the challenge of summarization head-on. With services like Azure AI Language service and Azure OpenAI GPT-3, researchers can automate the summarization process. Additionally, Azure’s extractive summarization API offers a way to pinpoint key sentences that represent the most important information within a transcript.
How It Works
Summarization: Azure AI Language service can generate summaries of these transcripts. It uses advanced algorithms to identify and condense the most salient points into a coherent summary.
Theme Extraction: Alongside summarization, Azure AI services can extract key phrases and themes from the text. This is particularly useful for identifying recurring topics or important concepts within a series of interviews.
Refinement: Researchers can then review and refine these automated summaries and themes, ensuring they align with the research objectives and provide the necessary depth of insight.
What you need to get started.
Azure account with a subscription: To create one use the following link: Azure portal Want to know what azure subscription is? azure subscription
Azure blob storage: A storage account to store documents which need to be extracted. Learn more about azure blob storage: Azure blob storage docs
Understanding Azure’s AI Language Service
Azure AI Language Service is a managed cloud service that simplifies the development of natural language processing (NLP) applications. With minimal machine-learning expertise required, it allows users to:
Identify Key Terms and Phrases: Quickly extract significant words and expressions from text.
Analyze Sentiment: Determine the emotional tone behind words to understand the context better.
Summarize Text: Condense long documents into shorter, digestible summaries using both extractive and abstractive techniques.
Build Conversational Interfaces: Create intelligent chatbots and virtual assistants that can engage with users naturally.
What is document and conversation summarization?
Summarization is one of the features offered by Azure AI Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language.
Document summarization only accepts plain text blocks, and conversation summarization accepts various speech artifacts for the model to learn more.
Key features
There are the aspects of document summarization this API provides:
Extractive summarization: Produces a summary by extracting salient sentences within the document.
Multiple extracted sentences: These sentences collectively convey the main idea of the document. They’re original sentences extracted from the input document’s content.
Rank score: The rank score indicates how relevant a sentence is to a document’s main topic. Document summarization ranks extracted sentences, and you can determine whether they’re returned in the order they appear, or according to their rank.
Multiple returned sentences: Determine the maximum number of sentences to be returned. For example, if you request a three-sentence summary extractive summarization returns the three highest scored sentences.
Positional information: The start position and length of extracted sentences.
Abstractive summarization: Generates a summary that doesn’t use the same words as in the document but captures the main idea.
Summary texts: Abstractive summarization returns a summary for each contextual input range within the document. A long document can be segmented so multiple groups of summary texts can be returned with their contextual input range.
Contextual input range: The range within the input document used to generate the summary text.
Get started with summarization
To use summarization, you submit for analysis and handle the API output in your application. Analysis is performed as-is, with no added customization to the model used on your data.
Input requirements and service limits
Summarization takes text for analysis. For more information, see Data and service limits in the how-to guide.
Summarization works with various written languages. For more information, see language support.
Prerequisites
An Azure subscription – Create one for free.
Go to the Azure Portal
Navigate to Azure Portal and sign-in with credentials that have access or subscription to your resource.
From the Azure Portal landing page navigate to search button and search language service.
Let’s Provision Azure AI language Service
Search for Azure language
Create Azure AI language service
Select option 2 to feature to custom summarization and text analytics then click continue to create the resource
Under create Azure AI language page
Choose your subscription.
Choose or create a resource group.
Choose the region to deploy (use that is near to your geolocation).
Create the resource name (make it unique).
Choose your pricing tier
Create a storage account or select one if you have.
Check the box to acknowledge terms in Responsible AI notice
Then click next.
As for me I will leave network setting as default and click Review and Create.
Once deployed navigate to the resource and open it.
Let’s Navigate to Azure AI Language studio
Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up language studio quickstart.
Click the language studio on the service you have deployed successfully.
Select your preferred language, azure resource that we deployed, number of sentences you prefer in summary and specify your summary interest.
Upload your .txt file that contains your interview transcript.
Check the box to acknowledge and Run the model.
If everything goes well, and all your services are okay, and documents are collected you should get a response like this.
Congratulations, you have Summarized an interview transcript with Azure AI Language Model.
The document summarization API request is processed upon receipt of the request by creating a job for the API backend. If the job succeeded, the output of the API is returned. The output is available for retrieval in 24 hours. After this time, the output is purged. Due to multilingual and emoji support, the response might contain text offsets. See how to process offsets for more information.
For those interested in leveraging these powerful tools to enhance their research process, the web page outlines the necessary steps to get started, including creating an Azure AI language service, setting up Azure blob storage, and navigating the Azure AI Language studio.
If you’re ready to streamline your research and analysis of interview transcripts, get started with Azure AI/ML today and unlock the potential of automated summarization and theme extraction.
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Microsoft Tech Community – Latest Blogs –Read More
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I’m experiencing QuickBooks Error 193 on my Windows 10/11 system. This error prevents me from accessing my company file in multi-user mode. I’ve tried restarting my computer and reinstalling QB, but the issue persists. What steps can I take to resolve this error and get QB running smoothly again? Read More
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One of our azure virtual desktop applications requires sv-SE as the date time settings. We have tried setting “UserOverrideKey” beneath the registry key HKEY_USERS.DEFAULTControl PanelInternational to no avail.
Is it not possible to have another regional setting than the default one of the session host installation?
One of our azure virtual desktop applications requires sv-SE as the date time settings. We have tried setting “UserOverrideKey” beneath the registry key HKEY_USERS.DEFAULTControl PanelInternational to no avail. Is it not possible to have another regional setting than the default one of the session host installation? Read More
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Hello Bubble.
We are small company with about 30 employess. We use OneDrive for our project.
We are active in the sewer inspection industry.
Since we sometimes exchange small projects, we have the problem that when we do it via the “Main Channel”, the files/folders are synchronized for all workers. This is rather suboptimal. Hence my question.
Would you create a separate channel for each employee or a “project handover” channel and the employees’ sub-folders in it, with approvals only for the correct colleague?
The aim should be that when employee 1 uploads the project, it should not be synchronized with everyone, but only with the colleagues who either have the approval for this folder or channel.
Thank you in advance.
Hello Bubble. We are small company with about 30 employess. We use OneDrive for our project.We are active in the sewer inspection industry.Since we sometimes exchange small projects, we have the problem that when we do it via the “Main Channel”, the files/folders are synchronized for all workers. This is rather suboptimal. Hence my question.Would you create a separate channel for each employee or a “project handover” channel and the employees’ sub-folders in it, with approvals only for the correct colleague?The aim should be that when employee 1 uploads the project, it should not be synchronized with everyone, but only with the colleagues who either have the approval for this folder or channel.Thank you in advance. Read More
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unresolved external symbol __imp__getaddrinfo@16
Hi,
I am reaching out to everyone in the community to check this error I am facing. I have included all the necessary headers and pre-proc directives to use getaddrinfo. But my compiler still shows error. I am using Windows Server 2022, with Visual Studio Enterprise 2022 and Windows SDK version 10.0.22621.0. Can you please help me understand why is still this error occurring.
#define WIN32_LEAN_AND_MEAN
#include <Ws2tcpip.h>
#include <stdio.h>
// Link with ws2_32.lib
#pragma comment(lib, “Ws2_32.lib”)
Hi, I am reaching out to everyone in the community to check this error I am facing. I have included all the necessary headers and pre-proc directives to use getaddrinfo. But my compiler still shows error. I am using Windows Server 2022, with Visual Studio Enterprise 2022 and Windows SDK version 10.0.22621.0. Can you please help me understand why is still this error occurring. #define WIN32_LEAN_AND_MEAN
#include <Ws2tcpip.h>
#include <stdio.h>
// Link with ws2_32.lib
#pragma comment(lib, “Ws2_32.lib”) Read More
SSRS Report Server View Hidden Shared Data Source Folder
I would like to know if I could view the data source or connection string for those reports. Specifically, I am looking for information such as which databases and tables these reports are generated from, or the connection string itself.
I can go to Report Manager and navigate to the report I want, then go to the Manage section. However, all I see is this:
Is there another place where I can view or amend this data source/connection string?
Could someone advise me on this, please?
Thanks
I would like to know if I could view the data source or connection string for those reports. Specifically, I am looking for information such as which databases and tables these reports are generated from, or the connection string itself.I can go to Report Manager and navigate to the report I want, then go to the Manage section. However, all I see is this: Is there another place where I can view or amend this data source/connection string?Could someone advise me on this, please? Thanks Read More
Add Todo Task to calendar
Now we can add task on web, but is there anyway can add task to calendar through Android Phone. Maybe add a button like “send to Outlook”, or use copilot to do it automatically.
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Solutions to Fix QuickBooks Desktop Error 503 windows 11?
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How much RAM does Windows 11 typically take?
Recently, I’ve noticed a huge amount of memory getting consumed by seemingly nothing or some background service. My PC have 32 GB of RAM here, and ~10 GB is not accounted for. The consumption got so out of hand that I had to restart my computer to keep it at bay…. Is there any software that can tell me what’s happening in the background?
Recently, I’ve noticed a huge amount of memory getting consumed by seemingly nothing or some background service. My PC have 32 GB of RAM here, and ~10 GB is not accounted for. The consumption got so out of hand that I had to restart my computer to keep it at bay…. Is there any software that can tell me what’s happening in the background? Read More
Desktop files only go to OneDrive
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Failed to disable spell check in several apps on Windows 11
Some apps like Asana, Todoist or others highlight English text with red lines. It concerns apps that can also be used via internet browser. Context menu reveals that this is a spell check. I have turned off ALL spell checking features of Windows 11 and of all my internet browsers. A second laptop with Windows 11 does not show this wrong behaviour. Does anyone meet the same problem? Do you know a good solution?
Some apps like Asana, Todoist or others highlight English text with red lines. It concerns apps that can also be used via internet browser. Context menu reveals that this is a spell check. I have turned off ALL spell checking features of Windows 11 and of all my internet browsers. A second laptop with Windows 11 does not show this wrong behaviour. Does anyone meet the same problem? Do you know a good solution? Read More
