Join us

ContentUpdates and recent posts about Grafana Tempo..
Link
@devopslinks shared a link, 4 weeks ago
FAUN.dev()

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
Link
@devopslinks shared a link, 4 weeks ago
FAUN.dev()

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
Link
@devopslinks shared a link, 4 weeks ago
FAUN.dev()

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
Link
@devopslinks shared a link, 4 weeks ago
FAUN.dev()

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
Link
@devopslinks shared a link, 4 weeks ago
FAUN.dev()

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  

News FAUN.dev() Team
@varbear shared an update, 4 weeks, 1 day ago
FAUN.dev()

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

chrome-devtools-mcp

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
 Activity
@varbear added a new tool chrome-devtools-mcp , 4 weeks, 1 day ago.
News FAUN.dev() Team Trending
@varbear shared an update, 4 weeks, 1 day ago
FAUN.dev()

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

AWS Lambda

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
News FAUN.dev() Team
@kaptain shared an update, 4 weeks, 1 day ago
FAUN.dev()

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

Kubernetes Talos Linux

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
 Activity
@kaptain added a new tool Talos Linux , 4 weeks, 1 day ago.
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.