Study Buddy: Learning Data Science and Machine Learning with an AI Sidekick
If you've ever wished for a friendly companion to guide you through the world of data science and machine learning, you're not alone. As part of the "For Beginners" curriculum, I recently built a Study Buddy Agent, an AI-powered assistant designed to help learners explore data science interactively, intuitively, and joyfully. Why a Study Buddy? Learning something new can be overwhelming, especially when you're navigating complex topics like machine learning, statistics, or Python programming. The Study Buddy Agent is here to change that. It brings the curriculum to life by answering questions, offering explanations, and nudging learners toward deeper understanding, all in a conversational format. Think of it as your AI-powered lab partner: always available, never judgmental, and endlessly curious. Built with chatmodes, Powered by Purpose The agent lives inside a .chatmodes file in the https://github.com/microsoft/Data-Science-For-Beginners/blob/main/.github/chatmodes/study-mode.chatmode.md. This file defines how the agent behaves, what tone it uses, and how it interacts with learners. I designed it to be friendly, encouraging, and beginner-first—just like the curriculum itself. It’s not just about answering questions. The Study Buddy is trained to: Reinforce key concepts from the curriculum Offer hints and nudges when learners get stuck Encourage exploration and experimentation Celebrate progress and milestones What’s Under the Hood? The agent uses GitHub Copilot's chatmode, which allows developers to define custom behaviors for AI agents. By aligning the agent’s responses with the curriculum’s learning objectives, we ensure that learners stay on track while enjoying the flexibility of conversational learning. How You Can Use It YouTube Video here: Study Buddy - Data Science AI Sidekick Clone the repo: Head to the https://github.com/microsoft/Data-Science-For-Beginners and clone it locally or use Codespaces. Open the GitHub Copilot Chat, and select Study Buddy: This will activate the Study Buddy. Start chatting: Ask questions, explore topics, and let the agent guide you. What’s Next? This is just the beginning. I’m exploring ways to: Expand the agent to other beginner curriculums (Web Dev, AI, IoT) Integrate feedback loops so learners can shape the agent’s evolution Final Thoughts In my role, I believe learning should be inclusive, empowering, and fun. The Study Buddy Agent is a small step toward that vision, a way to make data science feel less like a mountain and more like a hike with a good friend. Try it out, share your feedback, and let’s keep building tools that make learning magical. Join us on Discord to share your feedback.The Future of AI: Reduce AI Provisioning Effort - Jumpstart your solutions with AI App Templates
In the previous post, we introduced Contoso Chat – an open-source RAG-based retail chat sample for Azure AI Foundry, that serves as both an AI App template (for builders) and the basis for a hands-on workshop (for learners). And we briefly talked about five stages in the developer workflow (provision, setup, ideate, evaluate, deploy) that take them from the initial prompt to a deployed product. But how can that sample help you build your app? The answer lies in developer tools and AI App templates that jumpstart productivity by giving you a fast start and a solid foundation to build on. In this post, we answer that question with a closer look at Azure AI App templates - what they are, and how we can jumpstart our productivity with a reuse-and-extend approach that builds on open-source samples for core application architectures.The Future of AI: Developing Lacuna - an agent for Revealing Quiet Assumptions in Product Design
A conversational agent named Lacuna is helping product teams uncover hidden assumptions embedded in design decisions. Built with Copilot Studio and powered by Azure AI Foundry, Lacuna analyzes product documents to identify speculative beliefs and assess their risk using design analysis lenses: impact, confidence, and reversibility. By surfacing cognitive biases and prompting reflection, Lacuna encourages teams to validate assumptions through lightweight evidence-gathering methods. This experiment in human-AI collaboration explores how agents can foster epistemic humility and transform static documents into dynamic conversations.The Future of AI: Creating a Web Application with Vibe Coding
Discover how vibe coding with GPT-5 in Azure AI Foundry transforms web development. This post walks through building a Translator API-powered web app using natural language instructions in Visual Studio Code. Learn how adaptive translation, tone and gender customization, and Copilot agent collaboration redefine the developer experience.The Future of AI: Vibe Code with Adaptive Custom Translation
This blog explores how vibe coding—a conversational, flow-based development approach—was used to build the AdaptCT playground in Azure AI Foundry. It walks through setting up a productive coding environment with GitHub Copilot in Visual Studio Code, configuring the Copilot agent, and building a translation playground using Adaptive Custom Translation (AdaptCT). The post includes real-world code examples, architectural insights, and advanced UI patterns. It also highlights how AdaptCT fine-tunes LLM outputs using domain-specific reference sentence pairs, enabling more accurate and context-aware translations. The blog concludes with best practices for vibe coding teams and a forward-looking view of AI-augmented development paradigms.Using an AI Agent to Automate Jira Updates, PR Reviews, and Code Deployment
In modern software development, teams juggle multiple tools: Jira for project management, GitHub/GitLab for code collaboration, and CI/CD pipelines for deployment. Developers often spend significant time switching contexts—updating Jira tickets, reviewing pull requests, and triggering deployments. An AI agent can automate much of this workflow, acting as a “digital teammate” that fetches Jira data, helps review PRs, and pushes deployments. In this blog, we’ll explore how to set it up step by step. https://dellenny.com/supercharging-your-workflow-using-an-ai-agent-to-automate-jira-updates-pr-reviews-and-code-deployment/Getting Started with Microsoft Playwright Testing Features and How to Use It
In today’s fast-paced development environment, delivering high-quality web applications is crucial. Automated testing plays a key role in ensuring stability, performance, and user experience across browsers and devices. Microsoft Playwright Testing is one of the most powerful tools available for end-to-end (E2E) testing, offering speed, reliability, and cross-browser support. In this post, we’ll explore what Playwright Testing is, its key features, how to use it, and how it integrates with Azure for cloud-scale testing. https://dellenny.com/getting-started-with-microsoft-playwright-testing-features-and-how-to-use-it/Top 10 Things You Can Do with GitHub Copilot as a New Developer
If you’re just starting your coding journey, GitHub Copilot can feel like having a mentor right inside your code editor. It doesn’t just autocomplete code—it helps you learn, experiment, and ship projects faster. Here are the top 10 things you can do with GitHub Copilot as a new developer: https://dellenny.com/top-10-things-you-can-do-with-github-copilot-as-a-new-developer-2/
Build Custom Engine Agents in AI Foundry for Microsoft 365 Copilot
If you already have a multi‑agent AI application, you can surface it inside Microsoft 365 Copilot without adding another orchestration layer. Use a thin “proxy agent” built with the Microsoft 365 Agents SDK to handle Copilot activities and forward a simple request to your existing backend (in this example, we will use a simple Semantic Kernel multi‑agent workflow on top of Azure AI Foundry that writes and SEO‑optimizes blog posts). Develop fast and deploy to Azure with the Microsoft 365 Agents Toolkit for VS Code.
What's the future of RDLC ("client-side SSRS", aka "ReportViewer")?
This is the information I could gather so far: Getting an RDLC renderer for .NET 5+ is currently the https://feedback.azure.com/d365community/idea/ec1af842-4d25-ec11-b6e6-000d3a4f0da0. Unfortunately, there are currently no plans to do that (see https://devblogs.microsoft.com/dotnet/announcing-net-5-0-preview-6/). There are some enthusiast ports/recompilations floating around on github and nuget, but they are not official. The https://docs.microsoft.com/en-us/archive/blogs/sqlrsteamblog/ is dead, the last entry is from 2018. There's a third-party company providing an RDLC renderer, but https://docs.microsoft.com/en-us/archive/blogs/sqlrsteamblog/microsoft-acquires-report-rendering-technology-from-forerunner-software. Nothing has been heard since. There is currently no ReportViewer designer for Visual Studio 2022. Getting one is currently the https://developercommunity.visualstudio.com/search?space=8&sort=votes&q=2022. From a business perspective, I can totally understand that Microsoft is not giving this highly-loved feature the resources it needs. After all, they are basically giving away a great reporting engine for free, undermining their own SQL Server and Power BI sales. And they are not even hiding the fact that they'd rather have people purchase Power BI subscriptions, which is perfectly fine. They are a company, not a charity. Unfortunately, adding a dependency to a third-party cloud service is a no-go for many software development scenarios. Thus, I would like to start a discussion on the following points: It seems to me that MS no longer wants people to use their RLDC reporting engine in new projects. Is this observation correct? If you have a large repository of RDLC reports in your project, what are your migration plans? Are there drop-in replacements from third parties? Would Microsoft consider open-sourcing the RLDC engine, so that the community can "keep the product alive" for legacy scenarios and prevent this from being a blocker in .NET 5+ migrations? Best regards Heinzi
