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@dwisiswant0 shared a post, 1 week, 4 days ago

The most practical, fast, tiny command sandboxing for AI agents

Need to run one sketchy command without a full container? Here is the most practical, lightweight way to lock down one risky command in your AI pipeline. No daemon, no root, no image build.

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@laura_garcia shared a post, 1 week, 4 days ago
Software Developer, RELIANOID

🚀 Deploy RELIANOID Community Edition v7 on Microsoft Azure using Terraform.

⚡ Infrastructure ready in minutes ⚡ Official Terraform module ⚡ Fully automated Azure deployment Simple. Fast. Reproducible. #Terraform#Azure#DevOps#IaC#LoadBalancer#CloudInfrastructure#RELIANOID https://www.relianoid.com/resources/knowledge-base/community-edition-v7-administration-guide/deploy-reli..

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@laura_garcia shared a post, 1 week, 5 days ago
Software Developer, RELIANOID

🚀 See you at DeveloperWeek — February 18–20, 2026!

🚀 See you at DeveloperWeek — February 18–20, 2026! The world’s largest independent software development & AI engineering conference lands in San Jose, bringing together developers, architects, and tech leaders shaping the future of software. From AI & cloud-native to DevSecOps and developer experien..

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@kala shared an update, 1 week, 5 days ago
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OpenAI Hires OpenClaw Creator Peter Steinberger; Project Moves to Independent Foundation

OpenClaw

Peter Steinberger, creator of OpenClaw, is joining OpenAI to work on bringing AI agents to a broader audience, while OpenClaw will move to an independent open-source foundation and continue development outside OpenAI’s direct control.

OpenAI Hires OpenClaw Creator Peter Steinberger; Project Moves to Independent Foundation
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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.