About us
What We’re About
Build & Learn: AI with Coffee is a community for people who want to understand AI by actually building with it.
The AI world is changing quickly. New tools, frameworks and job titles appear constantly, and it can be difficult to know what to learn, what matters and where to begin.
Instead of trying to keep up with every new trend alone, we learn together through practical projects.
Our focus is simple: Build something. Learn from it. Share what worked—and what didn’t.
Who Is This For?
This group is especially useful for:
- Curious builders who learn best by doing (Tech curious beginners welcome!)
- Analysts and Python users who want to build real AI applications
- Software developers exploring LLM and GenAI systems
- Technical professionals trying to navigate the changing AI landscape
- Data scientists moving toward AI engineering
BUILD. LEARN. EXPERIMENT.
🌱 Start Building — At Any Pace
Free resources from previous build cycles — guides, templates, and exercises — are available anytime in our 📁 Google Drive
🌱 Go at Your Own Pace?
Reach out when you want a human in your corner.
📍 Weekly meetups in Berlin · 💬 Join our Discord → https://discord.gg/dPnmzcCP8w
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Upcoming events
4
Build & Learn: From Data Science to AI Engineering Week 2
Octupus Bar, Pestalozzistraße 5-8, 13187 Berlin, Berlin, DE📅 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.12 attendeesBuild & Learn: From Data Science to AI Engineering Week 3
Octupus Bar, Pestalozzistraße 5-8, 13187 Berlin, Berlin, DE📅 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.10 attendeesBuild & Learn: From Data Science to AI Engineering Week 4
Octupus Bar, Pestalozzistraße 5-8, 13187 Berlin, Berlin, DE📅 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.6 attendees
Past events
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