Anthropic: How we built our multi-agent research system

Anthropic writes about its Claude’s Research feature using a multi-agent system that distributes research tasks across specialized subagents via an orchestrator-worker pattern. The architecture boosts performance by parallelizing exploration and token usage, with key insights into prompt engineering (delegation, scaling, tool design), evaluation (LLM-as-judge, human-in-the-loop), and production hardening (stateful runs, debugging, orchestration).

LinkedIn: Introducing Northguard and Xinfra: scalable log storage at LinkedIn

LinkedIn unveils Northguard, a Kafka replacement built to handle over 32 trillion daily records by addressing scalability, operability, and durability challenges at hyperscale. Northguard introduces a sharded log architecture with minimal global state, decentralized coordination (via SWIM), and log striping for balanced load, backed by a pluggable storage engine using WALs, Direct I/O, and RocksDB LinkedIn developed Xinfra—a virtualized Pub/Sub layer with dual-write and staged topic migration to enable seamless migration, ensuring zero-downtime interoperability between Kafka and Northguard.

Canva: Measuring Commercial Impact at Scale at Canva

Canva writes about its internal app “IMPACT,” a Streamlit-on-Snowflake app that automates measurement of business metrics like MAU and ARR across 1,800+ annual experiments. Built with Snowpark, Cortex, and the Snowflake Python connector, the app replaces manual, error-prone analysis with a self-serve interface that aligns with finance models, supports pre/post-experiment workflows, and stores results for downstream use. Its modular architecture and PR-driven dev workflow enable scalable collaboration, while natural language summaries and scheduled metric calculations streamline impact analysis from hours to minutes.