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@kala shared a link, 2 days, 21 hours 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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@kaptain shared a link, 2 days, 21 hours ago
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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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@kaptain shared a link, 2 days, 21 hours ago
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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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@kaptain shared a link, 2 days, 21 hours ago
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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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@kaptain shared a link, 2 days, 21 hours 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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@devopslinks shared a link, 2 days, 21 hours ago
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LLMs Are Good at SQL. We Gave Ours Terabytes of CI Logs.

Mendral's agent runs ad‑hocSQLagainst compressedClickHouselogs. It traces flaky tests across months and scans up to 4.3B rows per investigation. They denormalize 48 metadata columns per log line. They compress 5.31 TiB down to ~154 GiB (~21 bytes/line) — a 35:1 ratio. That turns arbitrary filters in.. read more  

LLMs Are Good at SQL. We Gave Ours Terabytes of CI Logs.
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@devopslinks shared a link, 2 days, 21 hours ago
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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
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@devopslinks shared a link, 2 days, 21 hours ago
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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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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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@devopslinks shared a link, 2 days, 21 hours ago
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The real cost of random I/O

Therandom_page_costwas introduced ~25 years ago, and its default value has remained at 4.0 since then. Recent experiments suggest that the actual cost of reading a random page may be significantly higher than the default value, especially on SSDs. Lowering therandom_page_costmay not always be the be.. read more  

The real cost of random I/O
Grafana Tempo is a distributed tracing backend built for massive scale and low operational overhead. Unlike traditional tracing systems that depend on complex databases, Tempo uses object storage—such as S3, GCS, or Azure Blob Storage—to store trace data, making it highly cost-effective and resilient. Tempo is part of the Grafana observability stack and integrates natively with Grafana, Prometheus, and Loki, enabling unified visualization and correlation across metrics, logs, and traces.

Technically, Tempo supports ingestion from major tracing protocols including Jaeger, Zipkin, OpenCensus, and OpenTelemetry, ensuring easy interoperability. It features TraceQL, a domain-specific query language for traces inspired by PromQL and LogQL, allowing developers to perform targeted searches and complex trace-based analytics. The newer TraceQL Metrics capability even lets users derive metrics directly from trace data, bridging the gap between tracing and performance analysis.

Tempo’s Traces Drilldown UI further enhances usability by providing intuitive, queryless analysis of latency, errors, and performance bottlenecks. Combined with the tempo-cli and tempo-vulture tools, it delivers a full suite for trace collection, verification, and debugging.

Built in Go and following OpenTelemetry standards, Grafana Tempo is ideal for organizations seeking scalable, vendor-neutral distributed tracing to power observability at cloud scale.