DevSecOps in Practice
A Hands-On Guide to Operationalizing DevSecOps at Scale
Updates and recent posts about NanoClaw.. Activity
@devopslinks added a new tool JFrog Xray , 4 months, 1 week ago.
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@devopslinks added a new tool OWASP Dependency-Check , 4 months, 1 week ago.
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@varbear added a new tool pre-commit , 4 months, 1 week ago.
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@devopslinks added a new tool GitGuardian , 4 months, 1 week ago.
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@devopslinks added a new tool detect-secrets , 4 months, 1 week ago.
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@devopslinks added a new tool Gitleaks , 4 months, 1 week ago.
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@eon01 published a course, 4 months, 1 week ago
Founder, FAUN.dev
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@tairascott shared a post, 4 months, 1 week 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.
Story
@laura_garcia shared a post, 4 months, 1 week 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..
The agent integrates with messaging platforms such as WhatsApp and Telegram, allowing users to interact with their AI assistant directly through familiar chat applications. Each conversation group operates independently and maintains its own memory and execution environment.
A core design principle of NanoClaw is security through isolation. Every agent session runs inside its own container using Docker or Apple Container, ensuring that the agent can only access files and resources that are explicitly mounted. This approach relies on operating system–level sandboxing rather than application-level permission checks.
The architecture is intentionally simple: a single orchestrator process manages message queues, schedules tasks, launches containerized agents, and stores state in SQLite. Additional functionality can be added through a modular skills system, allowing users to extend capabilities without increasing the complexity of the core codebase.
By combining a minimal architecture with container-based isolation and messaging integration, NanoClaw aims to provide a transparent, customizable personal AI agent that users can run and control entirely on their own infrastructure.
