Conferences, demo days, pitch nights and meetups. Anything we can show a source for is linked back to the page it came from.
11 listed worldwide, in date order.
This event showcases an agentic AI system that creates digital twins, exploring AI applications in a practical setting. It is for AI practitioners and enthusiasts interested in innovative, project-based AI work. Attendees can engage with novel AI technologies and concepts in an AI-focused environment.
📅 Week 1: Kickoff — From Data Science to AI Engineering After a short break, Build & Learn is back with a new seven-week focus: learning how to build and ship real AI applications. 👩💻 AI engineering is becoming an increasingly important path for data scientists, analysts and Python developers. The biggest shift is not simply learning more machine learning theory—it is learning how to turn an AI prototype into a working application that other people can use. Over seven weeks, we will build AND deploy basic end to end LLM/AI project touching key technologies ( FastAPI, Postgres, Docker, a real AI pipeline. ) Week 1 — Kickoff & Setup: Set up Python, GitHub, Docker, API keys, and run the starter project locally. Week 2 — Learn the LLM specific layer. (OpenAI API) Week 3 — Build production ready AI backends. (Fast API, MCP server) Week 4 — Connect AI to your data with RAG (vector database) Week 5 — Lean into evals and observability (tracing, evaluation) Week 6 — Build & Ship Your Version Week 7 — Demo Day By the end of the 7 weeks you will have a working AI application in your own GitHub repository and experience building an AI pipeline, backend, database and frontend. ✨ Who is this for? This cycle is designed for data scientists, analysts, software developers and anyone interested in transitioning toward AI engineering. No previous LLM or AI engineering experience is required. However, you should already understand basic Python or be willing to complete some preparation before the first build session. ✨ Who’s hosting? I’m Lindsey, a senior data scientist working on AI systems, causal inference and data products. I’ve worked on machine learning, uplift modelling, fraud detection and production LLM systems. I care about learning through building—and about moving beyond AI hype toward applications that actually work. 💻 Bring: Your laptop and curiosity. Please also grab ☕ at the octopus bar to support their business and providing the location!
This event features hands-on sessions focused on IBM Bob, a new AI-powered development tool. It is designed for makers, innovators, and developers interested in learning and experimenting with AI technologies. Attendees can gain practical experience and insights into AI-powered software development.
This event offers a hybrid hands-on session around IBM Bob for makers, innovators, and developers. Participants can attend online or in person in Berlin to engage with practical technology applications. It is designed for those interested in gaining direct experience with IBM Bob tools and technology.
📅 Week 2: The LLM Application Layer This event is part of our seven-week AI Engineering program, where we build an end-to-end AI application step by step and present our finished projects during Demo Day in Week 7. This week, we’ll work through the key layers of a modern AI application: Go through the entire OpenAI API documentation and Python SDK (authentication, requests, structured outputs, tool calling). Learn prompt engineering fundamentals and the core building blocks of LLM systems. Understand that effective AI systems use as little AI as possible. Deterministic logic first, LLMs where they earn their place. Build agents from scratch before reaching for frameworks, and learn the 5 levels of AI agents. Learn context engineering to understand why agents fail and how to feed them the right information. By the end of the 7 week cycle, you should have a working AI application in your own GitHub repository and practical experience with AI pipelines, APIs, databases, retrieval, evaluation and deployment. You are welcome to follow the shared project or apply the weekly concepts to your own project. ✨ Who is this for? This cycle is designed for data scientists, analysts, software developers and Python users interested in moving toward AI engineering. No previous LLM or AI engineering experience is required. However, you should already understand basic Python or be willing to complete some preparation independently. ✨ Who’s hosting? I’m Lindsey, a senior data scientist working on AI systems, causal inference and data products. I’ve worked on machine learning, uplift modelling, fraud detection and production LLM systems. I care about learning through building—and moving beyond AI hype toward applications that actually work. 💻 Bring: Your laptop, your project and your curiosity. ☕ Please grab a coffee or drink from the Octopus Bar to support the business and thank them for providing the space.
PlebWalk Berlin is a casual outdoor event focused on spending a relaxed day in nature to support physical and mental well-being. It is for people interested in health, wellness, and outdoor activities. Attendees can enjoy nature and activities that promote a balanced life away from the tech or startup hustle.
Global AI Construct Berlin is a full-day hands-on event focused on AI and machine learning technologies. It is designed for developers and practitioners interested in applying Neo4j graph databases and agentic AI frameworks. Attendees can gain practical skills and network in Berlin's tech community.
📅 Week 3: Build Production ready AI backends This event is part of our seven-week AI Engineering program, where we build an end-to-end AI application step by step and present our finished projects during Demo Day in Week 7. This week, we’ll work through the key layers of a modern AI application: Build APIs with FastAPI and validate everything with Pydantic. Containerize with Docker and work with PostgreSQL for storage. Manage configuration and secrets safely with environment variables. Understand MCP servers and how they extend your AI applications. By the end of the 7 week cycle, you should have a working AI application in your own GitHub repository and practical experience with AI pipelines, APIs, databases, retrieval, evaluation and deployment. You are welcome to follow the shared project or apply the weekly concepts to your own project. ✨ Who is this for? This cycle is designed for data scientists, analysts, software developers and Python users interested in moving toward AI engineering. No previous LLM or AI engineering experience is required. However, you should already understand basic Python or be willing to complete some preparation independently. ✨ Who’s hosting? I’m Lindsey, a senior data scientist working on AI systems, causal inference and data products. I’ve worked on machine learning, uplift modelling, fraud detection and production LLM systems. I care about learning through building—and moving beyond AI hype toward applications that actually work. 💻 Bring: Your laptop, your project and your curiosity. ☕ Please grab a coffee or drink from the Octopus Bar to support the business and thank them for providing the space.
This event focuses on Bitcoin and privacy topics in an engaging, day-long format. It offers attendees the opportunity to network and discuss relevant subjects. It is designed for people interested in cryptocurrency and privacy in Berlin.
This event gathers old friends and introduces new topics relevant to the startup and VC ecosystem in Berlin. It provides an opportunity to reconnect and engage in discussions with peers. Attendees can expect informal networking and knowledge exchange in a local setting.
📅 Week 4: Connect AI to your data with RAG This event is part of our seven-week AI Engineering program, where we build an end-to-end AI application step by step and present our finished projects during Demo Day in Week 7. This week, we’ll work through the key layers of a modern AI application: Learn how to chunk documents and create embeddings. Use a vector database such as pgvector. Build an ingestion pipeline to embed and store documents. Implement similarity search and hybrid search. Learn to evaluate retrieval quality and identify failure cases. By the end of the 7 week cycle, you should have a working AI application in your own GitHub repository and practical experience with AI pipelines, APIs, databases, retrieval, evaluation and deployment. You are welcome to follow the shared project or apply the weekly concepts to your own project. ✨ Who is this for? This cycle is designed for data scientists, analysts, software developers and Python users interested in moving toward AI engineering. No previous LLM or AI engineering experience is required. However, you should already understand basic Python or be willing to complete some preparation independently. ✨ Who’s hosting? I’m Lindsey, a senior data scientist working on AI systems, causal inference and data products. I’ve worked on machine learning, uplift modelling, fraud detection and production LLM systems. I care about learning through building—and moving beyond AI hype toward applications that actually work. 💻 Bring: Your laptop, your project and your curiosity. ☕ Please grab a coffee or drink from the Octopus Bar to support the business and thank them for providing the space.