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@devopslinks shared a link, 1 month, 2 weeks ago
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AI Isn't Replacing SREs. It's Deskilling Them.

This post discusses the impact of AI on the role of Site Reliability Engineers (SREs) by drawing parallels to historical research on automation. It highlights the risk of deskilling and never-skilling for SREs who heavily rely on AI tools for incident response. The post also suggests potential appro.. read more  

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@devopslinks shared a link, 1 month, 2 weeks ago
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Introducing Agentic Observability in NGINX: Real-time MCP Traffic Monitoring

NGINX ships an open-sourceAgentic ObservabilityJS module. It parsesMCPtraffic and extracts tool names, error statuses, and client/server identities. The module uses nativeOpenTelemetryto export spans. A Docker Compose reference wires upOTel collector,Prometheus, andGrafanafor realtime throughput, la.. read more  

Introducing Agentic Observability in NGINX: Real-time MCP Traffic Monitoring
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@devopslinks shared a link, 1 month, 2 weeks ago
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AWS RDS Cost Optimization Guide: Cut Database Costs in 2026

Amazon RDS costs are not fixed - they vary based on configuration and usage. Making informed configuration and governance decisions is key to optimizing costs. Graviton instances offer better price-performance for common databases, while storage costs can be reduced by decoupling performance from ca.. read more  

AWS RDS Cost Optimization Guide: Cut Database Costs in 2026
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@devopslinks shared a link, 1 month, 2 weeks ago
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Building a Database on S3

This paper from 2008 proposes a shared-disk design over Amazon S3 for cloud-native databases, separating storage from compute. Clients write redo logs to Amazon SQS instead of directly to S3 to hide latency. The paper presents a blueprint for serverless databases before the term existed... read more  

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@eon01 published a course, 1 month, 2 weeks ago
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Learn Git in a Day

GitLab git Ubuntu

Everything you need, nothing you don't

Learn Git in a Day
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Kubernetes best practices for DevOps engineers

Kubernetes

Have to manage Kubernetes in production but don’t feel confident about its many moving parts, complex architecture, and configurations? Here’s a selection of technical guides from experienced engineers for Kubernetes beginners looking to master this orchestration tool for running containerised apps efficiently and reliably.

Best practices for Kubernetes
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@pramod_kumar_0820 shared a link, 1 month, 3 weeks ago
Software Engineer, Teknospire

⚡ Why Your Spring Boot API Takes 3 Seconds to Respond (And How to Fix It)

A practical breakdown of the most common Spring Boot performance bottlenecks — and how we optimized our API from 3 seconds to 200 ms.

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@devopslinks shared an update, 1 month, 3 weeks ago
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Microsoft Project Silica: Your Data, Stored in a Pyrex Dish, for 10,000 Years

Microsoft's Project Silica encodes data in borosilicate glass using femtosecond lasers, offering long-term storage for up to 10,000 years. This method overcomes traditional storage limitations and is cost-effective, though write speed remains a challenge. The research phase is complete, but no product release has been announced.

Microsoft Project Silica: Your Data, Stored in a Pyrex Dish, for 10,000 Years
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@varbear shared an update, 1 month, 3 weeks ago
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Operating Systems as Age Gatekeepers: The Law That Could Reshape the Internet

California's Digital Age Assurance Act mandates operating systems to share users' age data with app developers via a real-time API by 2027. The law faces criticism for depending on self-reported ages, potentially affecting its efficacy.

Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.