DevOps

Dev.to · about 8 hours ago

Terraform e YAML - Padrões Avançados e Escalabilidade

This article discusses advanced Terraform patterns for managing complex and scalable infrastructure in corporate environments. It covers deep merge of configurations, managing multiple YAML files, and integrating with CI/CD pipelines. To achieve deep merge, a custom function is required to merge maps hierarchically, where specific values override or complement generic values. This is necessary because Terraform's native merge function only performs a superficial merge, which is insufficient for nested maps.

DevOps.com · about 23 hours ago

These are 10 CI/CD Pipeline Mistakes That Slow Down Engineering Teams

CI/CD pipelines can become a source of friction as they grow in complexity, slowing down engineering teams. This can happen when small-scale systems become overly complicated. To avoid this, teams should focus on simplicity and scalability. Regular pipeline maintenance and monitoring are crucial to prevent issues. By doing so, teams can maintain rapid code development and deployment.

DevOps.com · 2 days ago

Building Reliable EMR Pipelines With Custom AMIs and Step Functions

Custom AMIs in EMR pipelines are not about creating one for every workload, but rather making dependency management, patching, and recovery explicit platform responsibilities to reduce repetitive tasks. This approach improves reliability and efficiency. Engineers should focus on automating these tasks using Step Functions. By doing so, they can ensure consistent and reliable EMR pipelines.

Dev.to · 2 days ago

Introducing tinbase — local Supabase without Docker (one process, real Postgres)

Supabase has introduced a new local development tool called tinbase, which allows developers to run Supabase locally without Docker. It uses a single 58 MB process and supports real Postgres 17, including RLS, auth, and triggers. This reduces the installation footprint and memory usage compared to the traditional 12-container Supabase setup. Developers can try it by running 'npx tinbase start'.

The New Stack · 3 days ago

Agents keep changing their answers. Harness just built delivery pipelines that don’t care.

Harness has built delivery pipelines that can handle agents without requiring them to conform to specific answers, addressing the issue of agents changing their responses. This change is significant in the DevOps space as it improves the reliability and efficiency of software delivery. Developers can now integrate agents into their pipelines without worrying about their responses. This update is a step forward in streamlining the software delivery lifecycle.

The New Stack · 4 days ago

Kubernetes made deploying easy. Nobody warned you about the databases.

Kubernetes simplified deployment, but overlooked database management. This oversight affects DevOps teams, who must now address database complexity. To mitigate this, teams should prioritize database planning and management. This ensures smooth operations and scalability.

Dev.to · 4 days ago

Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics

A team replaced 'looks good to me' with automated evaluation, catching 92% of hallucinations before deployment. They realized the need for domain-specific judges, speed, regression detection, CI/CD integration, and golden dataset management. To achieve this, they created a pipeline with test cases, LLM under test, judge ensemble, and metrics & regression detection. This approach ensures production-grade LLM evaluation and prevents hallucinations.

DevOps.com · 5 days ago

HashiCorp Introduces tfpolicy, a Native Policy Framework for Terraform

HashiCorp introduced tfpolicy, a native policy framework for Terraform, enabling policy-as-code governance using HCL and lifecycle-aware infrastructure checks.

The New Stack · 6 days ago

Self-healing GPU nodes in Kubernetes: What we learned building the EKS node monitoring agent

Kubernetes nodes, especially those with GPUs, frequently break due to PCIe issues. This is a problem for large-scale deployments like Amazon EKS. To address this, a monitoring agent was built to help self-healing. This agent can detect and respond to node failures. It's a solution for large-scale Kubernetes environments.

Dev.to · 6 days ago

I measured every millisecond of my real-time AI pipeline. The LLM was the fast part.

A real-time AI pipeline was instrumented to measure performance. The language model was not the bottleneck, but rather the transcription and delivery stages. The model choice was also important, with a smaller model being faster and more suitable for real-time applications. Engineers should prioritize model speed over intelligence when working with real-time features.