Join us

ContentUpdates and recent posts about Grafana Tempo..
Link
@devopslinks shared a link, 3 weeks, 6 days ago
FAUN.dev()

CodeBreach: Supply Chain Vuln & AWS CodeBuild Misconfig

Wiz Research dropped details onCodeBreach, a serious flaw that cracked open AWS SDK GitHub repos, yes, including the popular JavaScript one. The root problem? Leakyregex filtersin CodeBuild pipelines. They missed anchors, so attackers slipped in rogue pull requests, dodged build rules, and stole hig.. read more  

CodeBreach: Supply Chain Vuln & AWS CodeBuild Misconfig
 Activity
News FAUN.dev() Team Trending
@kala shared an update, 3 weeks, 6 days ago
FAUN.dev()

OpenClaw - Former Moltbot, Former Clawdbot - Went Viral Overnight. Then Security Reality Hit.

OpenClaw

OpenClaw, an open-source AI assistant platform, has been launched, evolving from Clawdbot and Moltbot. It features new plugins, enhanced security, and support for new models, while addressing a major security vulnerability. The platform emphasizes community involvement and invites contributions for its development.

OpenClaw - Former Moltbot, Former Clawdbot - Went Viral Overnight. Then Security Reality Hit.
 Activity
@kala added a new tool OpenClaw , 3 weeks, 6 days ago.
News FAUN.dev() Team Trending
@kaptain shared an update, 3 weeks, 6 days ago
FAUN.dev()

Cluster API v1.12 Released: In-Place Updates and Chained Upgrades

Kubernetes

Cluster API v1.12 introduces in-place updates and chained upgrades to enhance Kubernetes cluster management. In-place updates modify existing machines without deletion, while chained upgrades streamline multi-version upgrades. The release also includes improvements to immutable rollouts and various bug fixes.

Cluster API v1.12 Released: In-Place Updates and Chained Upgrades
Story
@laura_garcia shared a post, 1 month ago
Software Developer, RELIANOID

Shield Your Core 🛡️

Cybersec Asia is coming to Bangkok on February 4–5, 2026 — the key APAC event for cybersecurity, cloud, and data protection. 🌏 Bringing together global and regional leaders to tackle evolving threats and unlock new opportunities across CLMVT & APAC. 🤝 Meet RELIANOID and discover how we deliver secur..

cybersec asia 2026 RELIANOID
Story Keploy Team Trending
@sancharini shared a post, 1 month ago

Verification vs Validation Explained for Beginners in QA

Learn the difference between verification vs validation in QA. This beginner-friendly guide explains how both ensure software is built correctly and meets user expectations.

Verification vs Validation
Story
@laura_garcia shared a post, 1 month ago
Software Developer, RELIANOID

🚗🔐 Automotive Cybersecurity: Connected Cars and a Vulnerable Supply Chain

We originally published this article back in November, but it remains highly relevant today. Sharing it again in case you missed it 👇 Connected cars are no longer just mechanical machines — they are computers on wheels, embedded in complex digital ecosystems. As shown in the “Supply Chain in the aut..

Supply-Chain-in-the-Automotive-Industry_RELIANOID
Story
@laura_garcia shared a post, 1 month ago
Software Developer, RELIANOID

New Article: Emerging Cyber Threats Impacting Today’s Financial Ecosystem

Financial institutions continue to face rising cyber risks—not just from direct attacks, but from the vast networks of third-party suppliers that support their operations. Recent industry analyses reveal critical insights: Many essential vendors are far more important than organisations realise. ..

Story
@nelly96 shared a post, 1 month ago
Marketing specialist, Winston AI

How Accurate Are AI Detectors? (What the Data Actually Shows in 2026)

Do you also wonder, “Are AI detectors accurate?” and think the answer is a simple yes or no? The problem lies in the expectation. AI detectors don’t work like switches. They assign a probability of the text being AI-generated. The job of an AI detector is to estimate the likelihood, not to give verdicts. 

how-accurate-are-AI-detectors
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.