Real-Time AI Inference: Latency Optimization at Scale
January 19, 2026
Achieving sub-millisecond AI inference latency through model optimization, batching strategies, and hardware acceleration techniques.
January 19, 2026
Achieving sub-millisecond AI inference latency through model optimization, batching strategies, and hardware acceleration techniques.
January 17, 2026
Building AI systems capable of autonomous operation over extended periods, handling multi-day projects with adaptive planning and robust error recovery.
January 15, 2026
Strategies for deploying AI models to edge devices, from mobile phones to IoT sensors, with WebAssembly and optimized runtimes.
January 13, 2026
Exploring the mature Rust ecosystem in 2026, from web services to distributed systems, with practical patterns for production deployments.
January 11, 2026
Implementing comprehensive governance frameworks for AI systems in production, covering model approval, usage policies, and regulatory compliance.
January 9, 2026
Strategies for deploying reasoning-focused AI models at scale, balancing compute costs, latency requirements, and quality objectives.
January 7, 2026
Comprehensive security frameworks for AI systems, covering threat modeling, defense strategies, and compliance requirements for production deployments.
January 5, 2026
Exploring emerging platforms and standards for orchestrating multi-agent systems, from communication protocols to deployment patterns.
December 20, 2025
Reflecting on the architectural lessons learned from deploying AI systems in production, and what the evolution of AI architecture means for 2026
November 18, 2025
Architectural approaches to building comprehensive observability for AI systems, from model inference to agent reasoning chains and multi-step decision processes
July 14, 2025
Architectural considerations for building high-performance WebAssembly runtimes with robust security isolation
March 18, 2025
Architectural patterns for building robust LLMOps platforms that handle model serving, prompt management, observability, and cost optimization at scale
December 28, 2024
Reflecting on a year of building and scaling AI infrastructure—key architectural insights, patterns that worked, mistakes made, and what's next for production AI systems.
November 18, 2024
Core architectural principles and design patterns for building AI systems that are reliable, maintainable, and scalable in production environments.
October 20, 2024
Architectural patterns for building comprehensive observability into AI systems, from model performance monitoring to feature drift detection and production debugging.
August 11, 2024
Exploring architectural approaches to building distributed training infrastructure that scales from single machines to hundreds of GPUs across multiple data centers.
December 20, 2023
Reflecting on the major trends, technologies, and lessons learned in infrastructure and platform engineering throughout 2023
November 12, 2023
A framework for evolving platform engineering practices from ad-hoc scripts to mature internal developer platforms
October 8, 2023
Architectural patterns for designing robust control planes that manage distributed infrastructure at scale
September 11, 2023
Deep dive into optimizing data path performance for high-throughput, low-latency systems with practical techniques and measurements
August 16, 2023
Exploring security challenges unique to edge computing and practical solutions for protecting distributed edge infrastructure
July 19, 2023
Designing and operating highly available systems across multiple cloud providers with practical patterns and real-world trade-offs
June 14, 2023
Deploying eBPF programs for production observability, security monitoring, and network optimization at scale
May 20, 2023
A practical exploration of adopting Rust for high-performance systems programming, including real-world migration patterns and lessons learned
January 15, 2023
Exploring the architectural patterns and design decisions that enable effective AI-driven security platforms at scale
October 27, 2022
Architectural patterns for building scalable, resilient data platforms in the cloud, covering storage strategies, compute orchestration, and multi-region data management.
September 23, 2022
Architectural approaches to designing APIs that evolve gracefully over years, balancing stability for existing clients with innovation for new capabilities.
August 19, 2022
How team structure shapes system architecture and vice versa, with practical patterns for organizing engineering teams around microservices and distributed systems.
July 14, 2022
Architectural approaches to implementing distributed tracing at scale, covering design decisions, trade-offs, and patterns for observability in microservices architectures.
June 22, 2022
Exploring data mesh principles and architectural patterns for scaling data platforms across large organizations with distributed ownership and federated governance.
May 18, 2022
Architectural patterns and design decisions for building scalable ML feature pipelines that serve predictions in real-time while maintaining consistency and reliability.
March 17, 2022
Practical strategies for operating dozens of microservices, from service mesh to observability, deployment automation, and organizational patterns that work.
December 30, 2021
Reflecting on a year of building distributed systems, managing large engineering teams, and the key technical and organizational lessons learned.
November 18, 2021
Strategies for building internal developer platforms that improve productivity, reduce cognitive load, and enable teams to move faster while maintaining reliability.
October 21, 2021
Practical guide to implementing GraphQL Federation for microservices, enabling teams to build a unified API while maintaining service autonomy.
September 16, 2021
Architectural patterns and implementation strategies for deploying applications across multiple regions while maintaining consistency, performance, and availability.
August 19, 2021
Exploring eBPF technology for deep system observability, performance monitoring, and network analysis without kernel modifications or application changes.
May 22, 2021
Real-world strategies for deploying and scaling machine learning systems in production, from model serving to feature pipelines and monitoring.
March 20, 2021
Step-by-step approach to decomposing monolithic applications into microservices, with real-world patterns, pitfalls to avoid, and migration strategies that work.
December 28, 2020
Reflecting on architectural trends, lessons learned, and emerging patterns from a transformative year in cloud-native infrastructure and security
November 23, 2020
Architecture for embedding security throughout the software delivery lifecycle including shift-left patterns, automated testing, and continuous compliance
October 19, 2020
Architectural patterns for building internal developer platforms including self-service infrastructure, golden paths, and team topologies
September 21, 2020
Architectural approaches to cloud migration including modernization strategies, data migration patterns, hybrid architecture, and risk mitigation
August 17, 2020
Architectural approaches to implementing distributed tracing across thousands of services including sampling strategies, storage patterns, and query optimization
July 20, 2020
Architectural patterns for embedding security controls throughout continuous integration and deployment pipelines including secrets management, artifact signing, and vulnerability scanning
June 22, 2020
Architectural trade-offs between communication patterns in distributed systems including request-response, event-driven, and message-based approaches
May 18, 2020
Framework design patterns for automated security posture assessment, policy enforcement, and compliance validation across cloud infrastructure
February 18, 2020
Exploring topology strategies, federation approaches, and cross-cluster communication patterns for distributed Kubernetes deployments