Der Event-Katalog für Developer
Finde auf unserer Eventseite jede Menge Veranstaltungen für Developer!
5. März 2026
Tooling and MCP with LangChain4j
Transform your AI from a text generator into an autonomous agent that takes real action. Build custom tools the AI decides wh...en to use, chain multiple operations together, and handle failures gracefully. Then discover the Model Context Protocol—an open standard for shareable AI tools—and connect to ecosystem servers for file systems, Git repositories, and databases running in Docker containers.
9. März 2026
AI Builders Episode 7: Engineering Autonomous AI Agents at Scale
This session gives developers a grounded view of how to build and operate autonomous and semi autonomous AI agents responsibl...y. Learn how to architect, evaluate, and deploy agentic systems that work reliably in enterprise environments.
10. März 2026Hamburg
Microsoft Migrate & Modernize MicroHack Day
Erleben Sie in dieser kostenfreien, exklusiven Veranstaltung die zukunftsweisende Cloud-Strategie von Microsoft aus erster Ha...nd. Entdecken Sie, wie Microsoft Azure Ihnen ermöglicht, Daten aus komplexen Cloud- und On-Premises-Umgebungen effizient zu sammeln, zu analysieren und darauf proaktiv zu reagieren.
12. März 2026
Safety, Reliability & Best Practices in LangChain4j
Learn how to build safe, reliable, and enterprise-ready AI applications in Java, including how to protect API keys and model ...endpoints, validate tool output, enforce content filters, and keep LLMs from stepping outside their intended boundaries. See how to design prompts defensively, restrict system capabilities, and use structured interfaces to avoid injection attacks. We'll also explore patterns for safe RAG, secure memory, and audit-ready logging that make your Langchain4j applications trustworthy, enterprise-grade, and ready for real users.Learn how to build safe, reliable, and enterprise-ready AI applications in Java, including how to protect API keys and model endpoints, validate tool output, enforce content filters, and keep LLMs from stepping outside their intended boundaries. See how to design prompts defensively, restrict system capabilities, and use structured interfaces to avoid injection attacks. We'll also explore patterns for safe RAG, secure memory, and audit-ready logging that make your Langchain4j applications trustworthy, enterprise-grade, and ready for real users.
19. März 2026
VS Code Live: 1.109 Release
Join us for VS Code Live! This is your chance to catch up on all the major updates to VS Code and GitHub Copilot, with live d...emos from members of the product teams - you won't want to miss this!
23. März 2026
AI Builders Episode 8: Production-Ready LLM Systems: Performance, Reliability, and Cost
This session focuses on the realities of running LLM powered systems in production. Learn engineering patterns that improve r...eliability, reduce cost, and ensure consistent performance across workloads.
16. April 2026
VS Code Live: 1.110 Release
Join us for VS Code Live! This is your chance to catch up on all the major updates to VS Code and GitHub Copilot, with live d...emos from members of the product teams - you won't want to miss this!
14. Mai 2026
VS Code Live: 1.111 Release
Join us for VS Code Live! This is your chance to catch up on all the major updates to VS Code and GitHub Copilot, with live d...emos from members of the product teams - you won't want to miss this!
15. OktoberMicrosoft Reactor
Python + AI: Structured outputs
In our fifth stream of the Python + AI series, we'll discover how to get LLMs to output structured responses that adhere to a... schema. In Python, all we need to do is define a @dataclass or a Pydantic BaseModel, and we get validated output that meets our needs perfectly. We'll focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples will demonstrate the many ways you can use structured responses, like entity extraction, classification, and agentic workflows.
14. OktoberWebinar
Scaling and Maintaining AI Apps on Azure
In dieser Folge der Reihe geht es um die praktischen Aspekte des Betriebs Ihrer Anwendungen auf Azure. Liam konzentriert sich... dabei auf die Azure Developer CLI (azd) und zeigt Ihnen, wie Sie mithilfe spezifischer Entwicklertools Ihre Anwendungen warten und skalieren können. Er erklärt Ihnen, wie Sie z.B. vorhandene azd-Vorlagen mit Azure OpenAI nutzen, eigene Vorlagen erstellen und als Open Source beitragen sowie bewährte Methoden anwenden, den Zustand Ihrer Anwendung überwachen und auftretende Probleme mithilfe von Azure Developer CLI (azd) identifizieren und beheben.
14. OktoberMicrosoft Reactor
Python + AI: Vision models
Vision models are LLMs that can accept both text and images, like GPT 4o and 4o-mini. You can use those models for image capt...ioning, data extraction, question-answering, classification, and more! We'll use Python to send images to vision models, build a basic chat-on-images app, and build a multimodal search engine.
9. OktoberMicrosoft Reactor
Python + AI: Retrieval Augmented Generation
In our fourth Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Gener...ation. RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain. The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search.
9. OktoberMicrosoft Reactor
Turning Data into Insights with Copilot and Data Agents in Microsoft Fabric
AI in Microsoft Fabric is designed to help you work smarter with your data. In this session, you will learn how Copilot accel...erates analytics by generating queries, building models, and creating visualizations using natural language. We will explore AI functions in notebooks, which make it easy to apply enrichment such as summarization, sentiment analysis, and entity extraction with a single line of code. You will also see how AI-powered transforms on OneLake shortcuts can automatically process text files for summarization, translation, or PII detection without building pipelines. Finally, we will show how Data Wrangler provides an interactive way to clean and shape your data inside notebooks while generating reusable code. Through a live demo, you will see how these capabilities combine to help you deliver insights and intelligent features quickly for your hackathon project.
8. OktoberMicrosoft Reactor
GHAS Without the Guesswork: Streamlined Enablement with Modus Create
Enabling GHAS features can be a daunting task, particularly for organizations with complex ecosystems. This presentation expl...ores how teams can accelerate their GHAS journey through a balance of structured guidance and practical enablement. By using bootstrap scripts designed to remove barriers and simplify adoption as well as aligning security practices with established frameworks, teams can quickly build momentum and establish strong security foundations in GitHub. Together, these approaches reduce uncertainty, eliminate guesswork, and allow leaders and practitioners to focus on achieving meaningful outcomes. Attendees will gain insight into how alignment, enablement, and actionable guidance combine to create a more confident, efficient, and results-driven approach to security.
8. OktoberMicrosoft Reactor
Python + AI: Vector embeddings
In our second session of the Python + AI series, we'll dive into a different kind of model: the vector embedding model. A vec...tor embedding is a way to encode a text or image as an array of floating point numbers. Vector embeddings make it possible to perform similarity search on many kinds of content. In this session, we'll explore different vector embedding models, like the OpenAI text-embedding-3 series, with both visualizations and Python code. We'll compare distance metrics, use quantization to reduce vector size, and try out multimodal embedding models.
7. OktoberMicrosoft Reactor
Python + AI: Large Language Models
In our second session of the Python + AI series, we'll dive into a different kind of model: the vector embedding model. A vec...tor embedding is a way to encode a text or image as an array of floating point numbers. Vector embeddings make it possible to perform similarity search on many kinds of content. In this session, we'll explore different vector embedding models, like the OpenAI text-embedding-3 series, with both visualizations and Python code. We'll compare distance metrics, use quantization to reduce vector size, and try out multimodal embedding models.
25. DezemberMeetup
Stammtisch (gemeinsam mit der Functional Programmers UG KA)
5. Januar 2026Meetup
Azure Meetup Konstanz & Region - Monthly
7. Januar 2026DortmundMeetup
AI-Driven Development mit GitHub Copilot in Visual Studio
7. Januar 2026HamburgMeetup
Hamburg C# and .NET User Group Meetup
8. Januar 2026BerlinMeetup
Math and Coding: Solving Real-Life Probability Problems - FLINTA* only
8. Januar 2026DresdenMeetup
JavaScript Meetup
13. Januar 2026Frankfurt am MainMeetup
Monster-Meeting Rhein-Main Januar 2026
13. Januar 2026DüsseldorfMeetup
