Let LLMs Wander: Engineering RL Environments
What if, instead of learning only from examples, Language Models could explore crafted Environments, little worlds where they can act and improve autonomously?
Join me to see how Reinforcement Learning Environments work, how to build them with open-source tools, and how to use them to evaluate and train LLMs/Agents.
Writes, 3 ways: Postgres, Apache Kafka® and Apache Iceberg™
Learning new things is hard, but a useful way to think about new things is by comparing them to things you already know. In this talk, we’ll compare writes between 3 different popular data services: Postgres, Apache Kafka and Apache Iceberg. In doing so, we’ll learn a bit about the evolution of how we’ve thought of data storage as developers.
Building a Local News RAG: The Quest for Trustworthiness
We will show you how we build a local newspaper rag and all the problems that came along the way. From trustworthiness to customer wishes, search optimization and generation problems. Local villages, that LLMs know nothing about, content that is semantically the same and outdated information are only a part of the journey we made.
No 0-day required, just target the AI coding assistant!
Discover how attackers can manipulate AI coding assistants through hidden text, typosquatting and code errors. Learn to detect concealed instructions and set up trusted dependencies to keep unsafe code out of your environment.
Why Choose One: Multi-Engine Analytics with Apache Wayang
Choosing the best engine for each data task sounds right, but in modern data stacks doing so requires expertise and effort. Apache Wayang, a recently graduated TLP, addresses this by decoupling logical dataflows from execution engines. From big data platforms to SQL and ML engines, Wayang enables cross-platform execution that maximizes performance.
Low-Resource Languages as Stress Tests for NLP Data
Low-resource languages expose weaknesses in NLP systems that are often hidden by benchmark data. Drawing on experience annotating fieldwork data, this talk shows how ambiguity and annotation decisions reveal fundamental data quality issues relevant to real-world NLP pipelines.
Apache Spark Declarative Pipelines in Action
Learn Spark 4.1’s brand-new Declarative Pipelines, a paradigm shift replacing imperative code with simple declarations. We’ll build a real-time data pipeline together, processing streaming ADS-B flight data from tens of thousands of aircraft overhead.
Ultraviolet: Turn Hidden Document Data into an AI Advantage
Every PDF hides a world of structure, metadata and embedded signals that can silently influence AI based processing. With ultraviolets, we reveal how those can be exploited for malicious purposes and even become powerful tools for smarter applications. Designing for both humans and machines become a vital aspect of AI experience design.
From Legacy Search to Vespa: What a Real PoC Taught Us
For years, Germany’s largest classifieds website relied on a search-first relevance approach because structured data was sparse. This talk shares how we introduced Vespa in the Motors category, enriched signals with embeddings and extracted attributes, and migrated step by step; what worked, what failed, and which lessons only a real PoC reveals.
Text-to-Struct: Fine-tuning SLMs for Query Intent
Hybrid search fails on complex intent: vector search misses constraints, keywords miss nuance. This talk explores fine-tuning SLMs for ‘Query Understanding’—transforming vague inputs into structured requests. Learn to extract metadata, expand terms, and route intent to build a search engine that does the hard work for your users.