u/rgancarz

Grab is using AI agents to automate analytics workflows, reducing the share of routine work handled by analysts and shortening the time required to answer business questions. The company reports that the share of mechanical tickets handled by analysts fell from 44% in February to 30% in June, covering tasks such as data preparation, alerting, and reporting.

Netflix migrated most of its batch workloads onto Kueue, an open-source cloud-native batch job execution system that has outgrown its homegrown solution over the years. The company mapped the capabilities previously created in-house to Kueue’s functionality and also benefited from new features that would have been costly to incorporate into its homegrown solution. Engineers used API parity with the existing system to derisk the project and allow for a gradual and seamless migration.

Netflix has described how it redesigned the streaming pipeline behind Service Topology, its real-time map of service dependencies, to support its production scale. The system now uses three stages to separate intermediary resolution from enrichment and persistence, propagates backpressure to Kafka rather than dropping records, and uses server-sent events instead of gRPC for high-volume internal transfers.

Canva has redesigned its session revocation infrastructure to support hundreds of millions of active sessions while avoiding networked database lookups for most authentication requests. The new architecture stores revocation data in Amazon S3 as compact, immutable records and distributes the data to application gateways as in-memory indexes. Canva said the approach improved deployment speed, reduced database infrastructure, and cut the memory footprint of its revocation cache by 87.5%.

HubSpot has redesigned its Just-In-Time Access (JITA) authorization system using a rule engine architecture to make access decisions more observable and explainable. The system evaluates temporary access requests through independent rules instead of embedded conditional logic, allowing engineers to inspect how individual policies contribute to access decisions and manage authorization requirements as they change.

Dropbox has introduced a new engineering approach that connects security design artifacts directly with code review workflows using Model Context Protocol (MCP) and its internal knowledge system, Dash. The initiative aims to address a persistent gap in large engineering organizations: security requirements are typically defined during design reviews, but enforcement and verification occur much later, during code reviews, often without full context.

Includes Q&A with the author of the original article.

The engineering team at Zalando recently described the design and implementation of an in-process, client-side load balancer for a high-throughput API handling around 1 million requests per second. The result was more predictable latency, a drop in infrastructure costs, and better visibility into where failures actually originate.

DoorDash has developed Entity Cache, a transparent proxy caching platform to reduce redundant service-to-service requests across its microservices architecture. The company built the platform to address repeated requests for frequently accessed but infrequently changing data, which increased backend load, consumed additional compute resources, and contributed to higher tail latency as its microservices ecosystem expanded. According to DoorDash, the platform supports more than 100 endpoints across 50 services, serving over 1.5 million requests per second with 99.99999% availability.

GitHub has redesigned the navigation architecture behind GitHub Issues to reduce perceived latency for developers by moving more work to the client-side. The engineering team introduced client-side caching, predictive prefetching, and service worker-based request handling to improve navigation performance, increasing instant navigation experiences from 4% to 22%. The changes address a common challenge in large-scale web applications: reducing delays caused by repeated network requests and client initialization during frequently repeated workflows.

DoorDash has shared the architecture behind Ask DoorDash, its conversational AI assistant that helps consumers discover restaurants, plan meals, and build grocery carts via natural-language interactions. Over the course of a three-part engineering deep dive, the company described how it built the system using large language models, specialized AI agents, Model Context Protocol (MCP)-based tooling, persistent consumer memory, and automated evaluation infrastructure to operate AI-driven experiences in production.