Overlord is a trust kernel for AI agents that provides transactional execution, provenance tracking, and reversibility for untrusted code. It runs on Linux using kernel primitives like overlayfs and user namespaces, allowing users to inspect and approve changes before committing them to disk.
A comprehensive open-source guide for hardening Ubuntu Desktop 24.04/26.04 LTS systems with enterprise-grade security controls. Covers hardware protection, kernel hardening, sandboxing, network isolation, browser security, and anti-forensics techniques for journalists, human rights defenders, and security professionals across 17 languages.
A July-September 2026 scandal involves AI labs (OpenAI, Anthropic, Google DeepMind) conducting offensive security tests through Israeli vendor Irregular, founded by former IDF cyber unit members. Misconfigured sandboxes allowed models to access real internet and exploit actual organizations; the incident was later used to justify AI safety regulations, raising concerns about conflicts of interest within the EA-aligned safety ecosystem.
A technical analysis of why major Android apps use multiple processes, examining fifteen app manifests to understand process architecture. Apps separate processes for security isolation and sandboxing of untrusted content like web pages and images, limiting what compromised code can access through restricted permissions and user IDs. Examples from Chrome and Firefox show how sandboxed worker processes protect the main app while maintaining performance through shared memory and IPC channels.
Homebrew 7.0.0 released with major improvements including faster installations through concurrent operations, enhanced security with sandboxing and vulnerability checks, a native macOS app, and end of support for macOS 10.15 and Intel Macs moving to Tier 3.
Prompt engineering alone is insufficient for safety; hooks systems provide better guardrails by intercepting tool use and feeding feedback back to the model. Rather than blocking commands blindly, hooks can suggest alternatives (like replacing rm with trash) and guide the model's next action, closing the loop between enforcement and learning.
A guide to setting up local LLM development on Mac using OpenCode, Ollama, and Docker sandboxes. The setup enables running large models like Qwen 3.8 27B and Gemma 4 31B with proper resource management and sandboxing for safe experimentation.