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Building a digital doorman

Larson runs a dual-agent system. A tiny public doorman,nullclaw, lives on a $7 VPS. A private host,ironclaw, runs over Tailscale. Nullclaw sandboxes repo cloning. It routes heavy work to ironclaw viaA2AJSON‑RPC. It enforcesUFW, Cloudflare proxying, and single‑gateway billing... read more  

Building a digital doorman
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Multi-Agent AI Systems: Architecture Patterns for Enterprise Deployment

Last quarter, a mid-sized insurance company struggled to deploy an AI agent that collapsed in production due to cognitive overload. Enterprises are facing similar challenges when building single-agent AI systems and are moving towards multi-agent architectures to distribute responsibilities effectiv.. read more  

Multi-Agent AI Systems: Architecture Patterns for Enterprise Deployment
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How OpenAI Codex Works

Engineering leaders report limited ROI from AI, often missing full lifecycle costs. OpenAI's Codex model for cloud-based coding required significant engineering work beyond the AI model itself. The system's orchestration layer ensures rich context for the model to execute tasks effectively... read more  

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Inside our approach to the Model Spec

OpenAI introduces Model Spec, a formal framework defining behavioral rules for their AI models to follow, aiming for transparency, safety, and public insight. The Model Spec includes a Chain of Command to resolve instruction conflicts and interpretive aids for consistent gray area decisions, emphasi.. read more  

Inside our approach to the Model Spec
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Software engineer interviews for the age of AI

AI is becoming more prevalent in coding interviews, sparking interest from experienced candidates tired of traditional methods. Hiring great engineers is crucial for maintaining reliable services, especially in the era of AI-generated code. System design interviews help identify candidates with hand.. read more  

Software engineer interviews for the age of AI
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Why system architects now default to Arm in AI data centers

Architects rebase infrastructure torack-levelsystems. They anchor designs onArm NeoverseCPUs. Goal: balance energy, thermals, memory bandwidth, and sustained throughput. Benchmarks showGraviton4(Neoverse) outperforms comparableAMDandIntelEC2instances on price/performance for generative AI, DB, ML, a.. read more  

Why system architects now default to Arm in AI data centers
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How I Use LLMs for Security Work

LLMs like Claude, Cursor, and ChatGPT help tackle complex problems, but prompting them like Google won't cut it. Use role-stacking for varied perspectives (e.g.: you are a senior security engineer and sr. software engineer with experience in Docker, Kubernete..) and always specify your tools for bet.. read more  

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5 Suggestions to Upgrade your OpenTofu/Terraform & AWS Development Experience

The article covers tools and scripts to reclaim focus and improve workflow for OpenTofu, Terraform, and AWS CLI users. Suggestions include tools for easily swapping between versions, summarizing plans, linting code, switching AWS profiles, and customizing prompts. Bonus recommendation includes Task .. read more  

5 Suggestions to Upgrade your OpenTofu/Terraform & AWS Development Experience
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The Software Factory: Why Your Team Will Never Work the Same Again

The current models and tooling are enough to build software factories. In a software factory, developers stop writing code by hand, and AI coding agents implement features and fix bugs while developers design and improve the factory. Tools like Claude Code and Gas Town enable this shift towards a mo.. read more  

The Software Factory: Why Your Team Will Never Work the Same Again
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@devopslinks shared an update, 2 weeks, 1 day ago
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Systemd Gets a birthDate Field - and a "Liberated" Fork in Response

Age verification laws just reached the Linux init system. Systemd added an optional birthDate field to user records - not a policy engine, just a data slot other projects can build on. That was not enough to stop a fork. Liberated systemd removes it entirely, and the debate is not going away.

AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.