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@kala shared a link, 2 months ago
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Introducing helm

helm usesTypeScripttypes to registerskillsas typed functions with structured I/O. Permissions follow a clear precedence: exact→wildcard→skill→global. Agents get a keywordsearchtool and a code-execution tool that runs JS inside anSESsandbox. A recursiveproxyforwards calls overIPCto the parent, which .. read more  

Introducing helm
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Do you need an MCP to build your native app?

Do you need an MCP to build your native app? Surprisingly, modern agents succeed either way. The real difference is how much time, cost, and context you waste along the way... read more  

Do you need an MCP to build your native app?
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The Pentagon is making a mistake by threatening Anthropic

Anthropic's Claude Gov, optimized for national security uses, has fewer restrictions than regular versions. The Pentagon is threatening retaliation if Anthropic does not waive these restrictions by Friday, including invoking the Defense Production Act or declaring Anthropic a supply chain risk. Anth.. read more  

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@kaptain shared a link, 2 months ago
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Spotlight on SIG Architecture: API Governance

Kubernetes SIG Architecture’s API Governance crew is tightening the screws on stability, consistency, and cross-cutting sanity across the whole API surface. Not just REST. They’re eyeing the overlooked stuff too - CLI flags, config formats, anything that shapes how users and tools touch the system. .. read more  

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Before You Migrate: Five Surprising Ingress-NGINX Behaviors You Need to Know

The K8s blog exposesIngress-NGINXdefaults that clash withGateway API. These include case-insensitive prefix regexes. Host-wide annotation effects. Path rewrites. Slash redirects. URL normalization. Kubernetes retiresIngress-NGINXinMarch 2026.Gateway API 1.5graduatesListenerSetand theHTTPRoute CORS.. read more  

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From Chaos to Clarity: How We Built a Self-Healing CI/CD Pipeline That Talks to JIRA

Transitioning JIRA tickets to trigger deployments was key for this team struggling with manual deploys, leading to significant savings in time and reduction in errors. The architecture involved a JIRA Controller Pipeline, a Project Deployment Pipeline, and a JIRA Manager Pipeline, all aimed at seaml.. read more  

From Chaos to Clarity: How We Built a Self-Healing CI/CD Pipeline That Talks to JIRA
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I Built a Production-Grade Kubernetes Platform in 48 Hours.

A dev built a production-grade Kubernetes platform in 48 hours, encountering challenges and solutions along the way. The setup included multiple layers such as infrastructure, cluster, platform, delivery, and observability, each requiring troubleshooting and adjustments. The process involved deployi.. read more  

I Built a Production-Grade Kubernetes Platform in 48 Hours.
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@devopslinks shared a link, 2 months ago
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Rendering 100M pixels a second over ssh

A massively multiplayer snake game accessible over ssh, capable of handling thousands of concurrent players and rendering over a hundred million pixels a second. The game utilizes bubbletea for rendering frames and custom techniques to reduce bandwidth usage to around 2.5 KB/sec. Performance improve.. read more  

Rendering 100M pixels a second over ssh
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How to scale GitOps in the enterprise: From single cluster to fleet management

In GitOps, the "Argo Ceiling" is the point where tooling that worked at a small scale becomes unmanageable as you scale up to multiple clusters. To address this, you can consider using OCI registries and ConfigHub as alternative state store options. When it comes to secrets management, options like .. read more  

How to scale GitOps in the enterprise: From single cluster to fleet management
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Google API Keys Weren't Secrets. But then Gemini Changed the Rules

A report reveals Google Cloud'sAPI keysuse the same format for public IDs and secret auth. That overlap lets public keys reach theGemini API. New keys default toUnrestricted. Existing keys can be retroactively granted Gemini access. Google will add scoped defaults, block leaked keys, and notify affe.. read more  

Google API Keys Weren't Secrets. But then Gemini Changed the Rules
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.