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@adrian_schmidt started using tool Amazon SES , 1 day, 14 hours ago.

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@adrian_schmidt started using tool Amazon S3 , 1 day, 14 hours ago.

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@adrian_schmidt started using tool Amazon EC2 , 1 day, 14 hours ago.

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@adrian_schmidt started using tool Amazon Cloudfront , 1 day, 14 hours ago.

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@adrian_schmidt started using tool Amazon ALB , 1 day, 14 hours ago.

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@laura_garcia shared a post, 1 day, 15 hours ago

Software Developer, RELIANOID

𝗕𝗲𝘁𝘁 𝗕𝗿𝗮𝘀𝗶𝗹 𝟮𝟬𝟮𝟲

📍 São Paulo, Brazil 📅 May 5–8, 2026 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 is heading to 𝗕𝗲𝘁𝘁 𝗕𝗿𝗮𝘀𝗶𝗹 𝟮𝟬𝟮𝟲 — 𝘵𝘩𝘦 𝘭𝘢𝘳𝘨𝘦𝘴𝘵 𝘌𝘥𝘛𝘦𝘤𝘩 𝘦𝘷𝘦𝘯𝘵 𝘪𝘯 𝘓𝘢𝘵𝘪𝘯 𝘈𝘮𝘦𝘳𝘪𝘤𝘢. 🚀 46,000+ professionals 💡 270+ companies 🌍 One shared goal: transforming education Let’s talk about secure, scalable, and high-performance digital learning. 👉 See you at Expo Cen..

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@koukibadr shared a link, 1 day, 16 hours ago

Mobile Developer, Nventive

LiveData vs StateFlow

LiveData and StateFlow both stream data reactively, but differ in two key ways:

Initialization — LiveData needs no initial value; StateFlow requires one.

Lifecycle — LiveData is lifecycle-aware by default; StateFlow is not, so you need to wrap it in repeatOnLifecycle to avoid memory leaks.

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@pramod_kumar_0820 shared a post, 1 day, 20 hours ago

Software Engineer, Teknospire

How To Crack Senior Java Interviews (6–10 YOE) In 4 Weeks

#system-... #Intervi... #spring ... #java #Microse...

A practical 4-week roadmap to crack Senior Java Developer interviews (6–10 YOE), covering Core Java, Spring Boot internals, Microservices, System Design, and real-world interview strategies.

Senior Java Interviews (6–10 YOE) In 4 Weeks

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@smh started using tool TypeScript , 1 day, 21 hours ago.

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@smh started using tool Terraform , 1 day, 21 hours 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.