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Dapr Deployment Models

Daprstarted as a humble Kubernetes sidecar. Now? It's a full-blownmulti-mode runtimethat runs wherever you need it,edge,VM, orserverless APIs. Diagrid’sCatalysttakes that further. It wraps Dapr in a fully managed API layer that’s detached from your app’s lifecycle. No infra lock-in, just token-based.. read more  

Dapr Deployment Models
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v1.35: Job Managed By Goes GA

In Kubernetes v1.35,spec.jobControllerManagedByhits GA. That means full handoff of Job reconciliation to external controllers is now official. It unlocks tricks likeMultiKueue, where a single management cluster fires off Jobs to multiple worker clusters, without losing sight of what’s running where... read more  

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Troubleshooting Cilium network policies: Four common pitfalls

Cilium’s Day 2 playbook covers the real work: dialing inL7 policy controls, tuningHubble observability, and wringing performance fromBPF. It's how you keep big Kubernetes clusters sane. The focus?Multi-tenant isolation,node-to-node encryption, and scaling cleanly withexternal etcdso the network does.. read more  

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93% Faster Next.js in (your) Kubernetes

Next.js brings advanced capabilities to developers out-of-the-box, but scaling it in your own environment can be challenging due to uneven load distribution and high latency. Watt addresses these issues by leveragingSO_REUSEPORTin the Linux kernel, resulting in significantly improved performance met.. read more  

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1.35: In-Place Pod Resize Graduates to Stable

In-Place Pod Resizehits GA in Kubernetes 1.35. You can now tweak CPU and memory on live pods without restarts. This is finally production-ready! What’s new since beta? It now handlesmemory limit decreases, doesprioritized resizes, and gives you betterobservabilitywith fresh Kubelet metrics and Pod e.. read more  

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Kubernetes OptimizationInPlace Pod Resizing,ZoneAware Routin

Halodoc cut EC2 costs and shaved latency by leaning into two Kubernetes tricks: In-place pod resizing(v1.33) lets them dial pod resources up or down on the fly, especially handy during off-peak hours. Zone-aware routingviatopology-aware hintskeeps inter-service traffic close to home (same AZ), skipp.. read more  

Kubernetes OptimizationInPlace Pod Resizing,ZoneAware Routin
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Avoiding Zombie Cluster Members When Upgrading to etcd v3.6

etcd v3.5.26 patches a nasty upgrade bug. It now syncsv3storefromv2storeto stop zombie nodes from corrupting clusters during the jump to v3.6. The core issue: Older versions let stale store states bring removed members back from the dead... read more  

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@kala shared a link, 6 months ago
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Chinese AI in 2025, Wrapped

Chinese AI milestones in 2025: Big models from DeepSeek and others, AGI discussions at Alibaba, US-China chip war swings, Beijing's AI Action plan, and more. DeepSeek led the way with an open-source model, setting off a wave of Chinese companies going open-source. China's push for AGI and involvemen.. read more  

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Review of Deep Seek OCR

DeepSeek-OCRflips the OCR script. Instead of feeding full image tokens to the decoder, it leans on an encoder to compress them up front, trimming down input size and GPU strain in one move. That context diet? It opens the door for way bigger windows in LLMs. Why it matters:Shoving compression earlie.. read more  

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Evaluating AI Agents in Security Operations

Cotool threw frontier LLMs at real-world SecOps tasks using Splunk’s BOTSv3 dataset.GPT-5topped the chart in accuracy (62.7%) and gave the best results per dollar.Claude Haiku-4.5blazed through tasks fastest, just 240 seconds on average, maxing out tool integrations.Gemini-2.5-proflopped on both acc.. read more  

Evaluating AI Agents in Security Operations
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.