What If We’ve Been Scaling Stream Processing Wrong All Along
We’ve normalised extraordinary inefficiency in stream processing. Thousands of events/sec don’t justify repartition storms, serialization overhead, state migration. This talk explores a different path: Kafka Streams DSL, adopt Flink-like exactly-once semantics, Project Loom, and challenging the assumption that stream processing must be distributed.
Kafi Streams: Complex Stream Processing Made Simple
You can finally stop caring about co-partitioning, state stores and eventual consistency. Kafi Streams, built on (Py)DBSP, treats streaming like batch — strongly consistent, no special concepts. An Open Source Python library for the 80% of use cases that don’t need extreme scale. Fully incremental stream processing for everyone, from day one.
AI in the physical world: from observation to discovery
In 2026, AI is moving beyond digital tasks into the physical world. It increasingly interacts with instruments, experiments, and real-world data. Physicists stand at this frontier, using deep learning, LLMs, and agents to analyze nature itself. What have we learned about AI when it meets reality?