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@devopslinks shared a link, 3 months 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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@devopslinks shared a link, 3 months ago

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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

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@devopslinks shared a link, 3 months 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

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@varbear shared an update, 3 months ago

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New MCP Release v0.10.0 Supercharges AI-Assisted Web Development

#AI codi... #Model C... #perform... #Debuggi... #Chrome ...

Chrome DevTools MCP v0.10.0 unlocks deeper AI-powered debugging with new tools for DOM access, network request detection, page reload automation, performance insights, and snapshot saving.

Google Launches Chrome DevTools MCP Server Preview for AI-Driven Web Debugging

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@varbear added a new tool chrome-devtools-mcp , 3 months ago.

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@varbear shared an update, 3 months ago

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AWS Lambda Gets Python 3.14: Faster, Smarter, and More Serverless-Friendly

#aws #Python ... #Lambda@... #AWS Lam...

Python 3.14 is now available in AWS Lambda, enabling developers to leverage new Python features for serverless applications.

AWS Lambda Gets Python 3.14: Faster, Smarter, and More Serverless-Friendly

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@kaptain shared an update, 3 months ago

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The Most Absurd (and Brilliant) Kubernetes Cluster at KubeCon 2025

#kubecon #PETAFLO... #LattePa... #nvidia #kuberne...

Engineer Justin Garrison showcased a backpack-sized PETAFLOP Kubernetes cluster at KubeCon 2025, demonstrating localized AI capabilities without cloud reliance.

The Most Absurd (and Brilliant) Kubernetes Cluster at KubeCon 2025

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@kaptain added a new tool Talos Linux , 3 months ago.

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@kaptain shared an update, 3 months ago

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Google Breaks Kubernetes Limits Again: Inside the 130,000-Node GKE Cluster

#GKE #Google ... #Google #AI #kuberne...

Google successfully operates a 130,000-node Kubernetes cluster to enhance GKE's scalability for AI workloads.

Control plane throughput: Sustaining up to 1,000 operations per second for both Pod creation and Pod binding during intense scheduling phases.

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@kaptain added a new tool kueue , 3 months ago.

AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.