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@kala shared a link, 3 weeks, 6 days ago
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What if you don't need MCP at all?

MostMCP serversstuffed into LLM agents are overcomplicated, slow to adapt, and hog context. The post calls them out for what they are: a mess. The alternative? Scrap the kitchen sink. UseBash, leanNode.js/Puppeteer scripts, and a self-bootstrappingREADME. That’s it. Agents read the file, spin up the.. read more  

What if you don't need MCP at all?
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@kala shared a link, 3 weeks, 6 days ago
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How to write a great agents.md: Lessons from over 2,500 repositories

A GitHub Copilot feature allows for custom agents defined inagents.mdfiles. These agents act as specialists within a team, each with a specific role. The success of an agents.md file lies in providing a clear persona, executable commands, defined boundaries, specific examples, and detailed informati.. read more  

How to write a great agents.md: Lessons from over 2,500 repositories
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@devopslinks shared a link, 3 weeks, 6 days ago
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AWS to Bare Metal Two Years Later: Answering Your Toughest Questions About Leaving AWS

OneUptime ditched the cloud bill and rolled their own dual-site setup. Thinkbare metal, orchestrated withMicroK8s, booted byTinkerbell, patched together withCeph,Flux, andTerraform. Result?99.993% uptimeand$1.2M/year saved—76% cheaper than even well-optimized AWS. They run it all with just~14 engine.. read more  

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@devopslinks shared a link, 3 weeks, 6 days ago
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Monitor network performance and traffic across your EKS clusters with Container Network Observability

Amazon EKS just leveled up withContainer Network Observability- no extra tools needed. It now ships withservice maps,flow tables, andperformance metrics, all lit up by CloudWatch Network Flow Monitor. You get pod- and node-levelnetwork telemetryout of the box. Zoom in on service-to-service links. Si.. read more  

Monitor network performance and traffic across your EKS clusters with Container Network Observability
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@devopslinks shared a link, 3 weeks, 6 days ago
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S3 Storage Classes: Fast Access

A cost deep-dive breaks down three AWS S3 storage classes -Standard,Standard-IA, andGlacier Instant Retrieval- with sharp, interactive visualizations. It maps out the tradeoffs: storage cost, access frequency, and early deletion pain. Key tipping points surface: - UseStandard-IAif you read the objec.. read more  

S3 Storage Classes: Fast Access
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@devopslinks shared a link, 3 weeks, 6 days ago
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A complete guide to HTTP caching

A fresh guide reframes HTTP caching as less of a tweak, more of an architectural move. It breaks caching into layers - browser memory, CDNs, reverse proxies, app stores - and shows how each one plays a part (or gets in the way). It gets granular with headers likeCache-Control,ETag, andVary, calling .. read more  

A complete guide to HTTP caching
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@devopslinks shared a link, 3 weeks, 6 days ago
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Terraform Stacks: A Deep-Dive for Azure Practitioners in Europe

Terraform Stacksjust hit GA onHCP Terraform, and they bring some real structure to the chaos. Think modular, declarative, and way less workspace spaghetti. Build reusablecomponents(a.k.a. modules), bundle them intodeployments, and wire up stacks usingpublish/consume patterns- complete with automated.. read more  

Terraform Stacks: A Deep-Dive for Azure Practitioners in Europe
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@devopslinks shared a link, 3 weeks, 6 days ago
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WTF is ... - AI-Native SAST?

AI-native SAST is replacing the “LLM as magic scanner” myth. Instead, the smart play is combining language models with real static analysis. That’s how teams are catching the gnarlier stuff - like business logic bugs - that usually slip through. The trick?Use static analysis to grab clean, relevant .. read more  

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Unlocking self-service LLM deployment with platform engineering

A new platform stack - Port+GitHub Actions+HCP Terraform** - is turning LLM deployment into a clean self-service flow. The result => predictable, governed pipelines that ship faster. Infra gets standardized. Provisioning? Handled through GitHub Actions. Policies? Baked in via HCP Terraform. Port tie.. read more  

Unlocking self-service LLM deployment with platform engineering
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Post-quantum (ML-DSA) code signing with AWS Private CA and AWS KMS

AWS Private CA now supportspost-quantum ML-DSA X.509 certificates. That means quantum-resistant roots of trust - for code signing, mTLS, and device auth. It's wired up with AWS KMS, so you can handle signing workflows usingML-DSA keysand verify them with standard tools like OpenSSL usingCMS detached.. read more  

Post-quantum (ML-DSA) code signing with AWS Private CA and AWS KMS
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.