This article discusses the transition from AWS to bare-metal infrastructure, detailing the cost savings and operational changes experienced over two years. The authors address common questions from the tech community, highlighting their significant savings, improved reliability, and ongoing cloud utilization where it makes sense.

This article discusses the rapid emergence of stablecoin neobanks and questions their reliability compared to traditional banks. It highlights the systemic risks these neobanks face due to their dependence on centralized infrastructure, emphasizing the need for robust and reliable systems to gain user trust.

Anthropic has identified and resolved three infrastructure bugs that degraded the output quality of its Claude AI models over the summer of 2025. The company is implementing changes to its processes to prevent future issues, while also facing challenges associated with running its service across multiple hardware platforms. Community feedback highlights the complexity of maintaining model performance across these diverse infrastructures.

The article discusses advancements in Chef infrastructure at Slack, focusing on improving safety and reliability without causing disruptions. It highlights the implementation of new practices and technologies that enhance system resilience while maintaining operational continuity.

Klaviyo successfully migrated its event processing pipeline from RabbitMQ to a Kafka-based architecture, handling up to 170,000 events per second while ensuring zero data loss and minimal impact on ongoing operations. The new system enhances performance, scales for future growth, and improves operational efficiency, positioning Klaviyo to meet the demands of over 176,000 businesses worldwide. Key design principles focused on decoupling ingestion from processing, eliminating blocking issues, and ensuring reliability in the face of transient failures.

Harvey's AI infrastructure effectively manages model performance across millions of daily requests by utilizing active load balancing, real-time usage tracking, and a centralized model inference library. Their system prioritizes reliability, seamless onboarding of new models, and maintaining high availability even during traffic spikes. Continuous optimization and innovation are key focuses for enhancing performance and user experience.

LinkedIn has developed a new high-performance DNS Caching Layer (DCL) to enhance the resilience and reliability of its DNS client infrastructure, addressing limitations of the previous system, NSCD. DCL features adaptive timeouts, exponential backoff, and dynamic configuration management, allowing for real-time updates without service interruptions, thus improving overall DNS performance and debugging capabilities. The implementation of DCL has significantly improved visibility into DNS traffic, enabling proactive monitoring and faster resolution of issues across LinkedIn's vast infrastructure.