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@devopslinks added a new tool JFrog Xray , 1 month, 2 weeks ago.
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@devopslinks added a new tool OWASP Dependency-Check , 1 month, 2 weeks ago.
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@varbear added a new tool pre-commit , 1 month, 2 weeks ago.
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@devopslinks added a new tool detect-secrets , 1 month, 2 weeks ago.
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@devopslinks added a new tool Gitleaks , 1 month, 2 weeks ago.
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Founder, FAUN.dev

DevSecOps in Practice

TruffleHog Flask NeuVector detect-secrets pre-commit OWASP Dependency-Check Docker checkov Bandit Hadolint Grype KubeLinter Syft GitLab CI/CD Trivy Kubernetes

A Hands-On Guide to Operationalizing DevSecOps at Scale

DevSecOps in Practice
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@tairascott shared a post, 1 month, 2 weeks ago
AI Expert and Consultant, Trigma

How Do Large Language Models (LLMs) Work? An In-Depth Look

Discover how Large Language Models work through a clear and human centered explanation. Learn about training, reasoning, and real world applications including Agentic AI development and LLM powered solutions from Trigma.

How do Large Language Models (LLMs) Work Banner
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@laura_garcia shared a post, 1 month, 2 weeks ago
Software Developer, RELIANOID

🔐 RELIANOID at Gartner IAM Summit 2025 | Dec 8–10, Grapevine, TX

We’re heading to the Gartner Identity & Access Management Summit to showcase how RELIANOID’s intelligent proxy and ADC platforms empower modern IAM: enhancing Zero Trust enforcement, adaptive access, and hybrid/multi-cloud security. Join us to explore AI-driven automation, ITDR, and identity governa..

Gartner Identity and Access Management Summit 2025 relianoid
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@varbear shared a link, 1 month, 2 weeks ago
FAUN.dev()

Confessions of a Software Developer: No More Self-Censorship

A mid-career dev hits pause after ten years in the game -realizing core skills likepolymorphism, SQL, and automated testingnever quite clicked. Leadership roles, shipping products, mentoring junior devs - none of it filled those gaps. They'd been writingC#/.NETfor a while too. Not out of love, just .. read more  

Confessions of a Software Developer: No More Self-Censorship
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.