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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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🚀 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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BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).