Posts tagged with sre
Why it matters: This article demonstrates how a Durable Execution platform like Temporal can drastically improve the reliability of critical cloud operations and continuous delivery pipelines, reducing complex failure handling and state management for engineers.
- •Netflix significantly improved the reliability of its Spinnaker deployments by adopting Temporal, reducing transient failures from 4% to 0.0001%.
- •Temporal is a Durable Execution platform that allows engineers to write resilient code, abstracting away complexities of distributed system failures.
- •The previous Spinnaker architecture suffered from complex, undifferentiated internal orchestration, retry logic, and a homegrown Saga framework within its Clouddriver service.
- •Prior to Temporal, Clouddriver's instance-local task state led to lost operation progress if the service crashed, impacting deployment reliability.
- •Temporal helped streamline cloud operations by offloading complex state management and failure handling, allowing services like Clouddriver to focus on core infrastructure changes.
Why it matters: This article details how Netflix built a robust, high-performance live streaming origin and optimized its CDN for live content. It offers insights into handling real-time data defects, ensuring resilience, and optimizing content delivery at scale.
- •Netflix Live Origin is a multi-tenant microservice bridging cloud live streaming pipelines and Open Connect CDN, managing content distribution.
- •It ensures resilience through redundant regional pipelines and server-side failover, utilizing epoch locking for intelligent segment selection.
- •The Origin detects and mitigates live stream defects (e.g., short, missing segments) by selecting valid candidates from multiple pipelines.
- •Open Connect's nginx-based CDN was optimized for live streaming, extending proxy-caching and adding millisecond-grain caching.
- •Live Origin "holds open" requests for yet-to-be-published segments, reducing network chatter and improving efficiency.
- •HTTP headers are leveraged for scalable streaming metadata, providing real-time event notifications to client devices via OCAs.
Why it matters: This article introduces "Continuous Efficiency," an AI-driven method to embed sustainable and efficient coding practices directly into development workflows. It offers a practical path for engineers to improve code quality, performance, and reduce operational costs without manual effort.
- •"Continuous Efficiency" integrates AI-powered automation with green software principles to embed sustainability into development workflows.
- •This approach combines LLM-powered Continuous AI for CI/CD with Green Software practices, aiming for more performant, resilient, and cost-effective code.
- •It addresses the low priority of green software by enabling near-effortless, always-on optimization for efficiency and reduced environmental impact.
- •Implemented via Agentic Workflows in GitHub Actions, it allows defining engineering standards in natural language for scalable application.
- •Benefits include declarative rule authoring, semantic generalizability across languages, and intelligent remediation like automated pull requests.
- •Pilot projects demonstrate success in applying green software rules and Web Sustainability Guidelines, yielding measurable performance gains.
Why it matters: The article details how GitHub Actions' core infrastructure was re-architected to support massive scale and deliver crucial features. This ensures improved reliability, performance, and flexibility for developers using CI/CD pipelines, addressing long-standing community requests.
- •GitHub Actions underwent a significant re-architecture of its core backend services to handle massive growth, now processing 71 million jobs daily.
- •This re-architecture improved performance, scalability, and reliability, laying the foundation for future feature development.
- •Key quality-of-life improvements recently shipped include support for YAML anchors to reduce workflow duplication.
- •Non-public workflow templates enable consistent, private CI scaffolding across organizations.
- •Reusable workflow limits were increased, allowing for more modular and deeply nested CI/CD pipelines.
- •The cache size limit per repository was removed, addressing a pain point for large projects with heavy dependencies.
Why it matters: This report highlights common infrastructure challenges like rate limiting, certificate management, and configuration errors. It offers valuable insights into incident response, mitigation strategies, and proactive measures for maintaining high availability in complex distributed systems.
- •GitHub experienced three incidents in November 2025, affecting Dependabot, Git operations, and Copilot services.
- •A Dependabot incident was caused by hitting GitHub Container Registry rate limits, resolved by adjusting job rates and increasing limits.
- •All Git operations failed due to an expired TLS certificate for internal service-to-service communication, mitigated by certificate replacement and service restarts.
- •A Copilot outage for the Claude Sonnet 4.5 model resulted from a misconfiguration in an internal service, which was resolved by reverting the change.
- •Post-incident actions include adding new monitoring, auditing certificates, accelerating automation for certificate management, and improving cross-service deploy safeguards.
Why it matters: Engineers can leverage AI for rapid development while maintaining high code quality. This article introduces tools and strategies, like GitHub Code Quality and effective prompting, to prevent "AI slop" and ensure reliable, maintainable code in an accelerated workflow.
- •AI significantly accelerates development but risks generating "AI slop" and technical debt without proper quality control.
- •GitHub Code Quality, leveraging AI and CodeQL, ensures high standards by automatically detecting and suggesting fixes for maintainability and reliability issues in pull requests.
- •Key features include one-click enablement, automated fixes for common errors, enforcing quality bars with rulesets, and surfacing legacy technical debt.
- •Engineers must "drive" AI by providing clear, constrained prompts, focusing on goals, context, and desired output formats to maximize quality.
- •This approach allows teams to achieve both speed and control, preventing trade-offs between velocity and code reliability in the AI era.
Why it matters: This expansion provides engineers with more Azure regions and Availability Zones, enabling highly resilient, performant, and geographically diverse cloud architectures for critical applications and AI workloads.
- •Microsoft is significantly expanding its cloud infrastructure in the US, including a new East US 3 region in Atlanta by early 2027.
- •The East US 3 region will incorporate Availability Zones for enhanced resiliency and support advanced Azure workloads, including AI.
- •Five existing US Azure regions (North Central US, West Central US, US Gov Arizona, East US 2, South Central US) will also gain Availability Zones by 2026-2027.
- •These expansions aim to meet growing customer demand for cloud and AI services, offering greater capacity, resiliency, and agility.
- •The new infrastructure emphasizes sustainability, with the East US 3 region designed for LEED Gold Certification and water conservation.
- •Leveraging Availability Zones and multi-region architectures is highlighted for improving application performance, latency, and overall resilience.
Why it matters: As AI agents become integrated into development, ensuring their output is safe and predictable is critical. This system provides a blueprint for building trust in automated code generation through rigorous feedback loops and validation.
- •Spotify's system focuses on making AI coding agents predictable and trustworthy through structured feedback loops.
- •The architecture ensures that agent-generated code is validated against existing engineering standards and tests.
- •Background agents operate asynchronously to improve code quality without disrupting the primary developer workflow.
- •The framework addresses the challenge of moving from experimental AI generation to production-ready software engineering.
- •Automated verification steps are integrated to prevent the introduction of bugs or technical debt by autonomous agents.
Why it matters: This article provides a blueprint for implementing "shift left" security and IaC at enterprise scale, crucial for preventing misconfigurations, enhancing consistency, and improving operational efficiency in large, complex environments.
- •Cloudflare adopted "shift left" principles and Infrastructure as Code (IaC) to manage its critical platform securely and consistently at enterprise scale.
- •All production account configurations are managed via IaC using Terraform, integrated with a custom CI/CD pipeline (Atlantis, GitLab, tfstate-butler).
- •A centralized monorepo holds all configurations, with teams owning their specific sections, promoting accountability and consistency.
- •Security baselines are enforced through Policy as Code (Open Policy Agent with Rego), shifting validation to the earliest stages of development.
- •Policies are automatically checked on every merge request, preventing misconfigurations before deployment and minimizing human error.
Why it matters: Achieving sub-second latency in voice AI requires rethinking performance metrics and optimizing every microservice. This article shows how semantic end-pointing and synthetic testing are critical for building responsive, human-like voice agents at scale.
- •Developed the Flash Reasoning Engine to achieve sub-second Time to First Audio (TTFA) for natural, human-fast voice interactions.
- •Optimized the real-time voice pipeline by shaving hundreds of milliseconds from microservices, synchronous calls, and serialization paths.
- •Implemented semantic end-pointing algorithms that use confidence thresholds to distinguish between meaningful pauses and true utterance completion.
- •Created AI-driven synthetic customer testing frameworks to generate repeatable data sets and eliminate noise in performance metrics.
- •Resolved measurement inaccuracies where initial tests incorrectly reported 70-second latencies by focusing on TTFA instead of total output duration.