Rust AI Begins: Introduction — Alexy Khrabrov
Abstract
FunctionalTVAlexy Khrabrov opens Rust AI Begins, introduces the San Francisco Rust-and-AI community, and previews the evening’s projects and speakers.
Meet the builders using Rust across AI, data systems, agents, and infrastructure. This complete FunctionalTV release brings together the event introduction, ten sessions, and a speaker interview from the first San Francisco gathering.
10 sessions · introduction · interview
Presented in curated event order, followed by a bonus speaker interview.
Alexy Khrabrov opens Rust AI Begins, introduces the San Francisco Rust-and-AI community, and previews the evening’s projects and speakers.
Shehab Amin presents Sail’s Rust-native, JVM-free Spark-compatible architecture, including planning, shuffle, Spark Connect, PySpark, Arrow, DataFusion, Delta Lake, and Iceberg integration.
Kranti Parisa explains LaserData’s Apache Iggy-based streaming platform and its use of thread-per-core execution, io_uring, zero-copy serialization, and Kafka-compatible streaming concepts.
Linghua Jin presents CocoIndex, a Rust-core context engine that incrementally turns changing unstructured assets into extracted structures, search indices, knowledge graphs, and other fresh AI views.
Manvika Tuteja surveys Valkey and its Rust ecosystem, including the GLIDE client core, the Valkey Modules Rust SDK, and Valkey-Bloom.
Alexy Khrabrov presents the QueryGraph lakehouse stack: Sail, an agentic semantic layer, LakeCat governance, Typesec and TypeDID security, and the Grust graph API.
Oussama Saoudi and Scott Sandre explain how delta-kernel-rs encapsulates Delta Lake metadata and protocol semantics behind an Engine Trait, reducing duplicated implementations across DuckDB, Polars, DataFusion, and other engines.
Emil Sadek explains why ADBC’s vendor-neutral, Arrow-native interface better serves fast, cross-system agent workflows than row-oriented APIs and system-specific SDKs.
Melanie Warrick and Melissa Herrera demonstrate a Rust-and-Temporal audience agent that survives worker redeployment while preserving workflow state, retries, and human input.
The Polygres founders explain how pgGraph adds cache-conscious, multi-hop graph traversal inside PostgreSQL and how graph and vector retrieval provide richer agent context.
Everett Kleven and Srinivas Lade describe how Daft bridges Python coroutines into a Rust Tokio runtime with PyO3, enabling concurrent model, embedding, and external-service calls without blocking workers or fighting the GIL.
Shehab Amin discusses rebuilding the Spark-compatible lakehouse ecosystem in Rust with Sail, Apache Arrow, Apache DataFusion, Delta Lake, and Apache Iceberg REST.
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