This document details the optimized execution of the Qwen3.5-397B-A17B-MXFP4 model using vLLM on RDNA4 GPUs, such as 8xR9700. It provides a Dockerfile with Triton patches and instructions for downloading the model and launching the inference container.
This content describes the creation of Docker images for `llama.cpp` to simplify running MTP models, following numerous improvements and bug fixes. It also notes that Unsloth has released new MTP models for Qwen 3.6, making previous versions obsolete.
Inspired by a YouTube Short, the author embarked on a deep dive to build a completely free local AI stack, connecting a local AI model to Claude Code without API keys or subscriptions. This post documents the learning process, including tools like Ollama, Gemma4, Docker, and Open WebUI, for running AI entirely on personal hardware.
The author integrated Claude AI into a Docker management platform to automate the reasoning behind patching vulnerabilities, going beyond what traditional scanners offer. This AI layer helps contextualize CVE reports, evaluating exploitability, fix safety, and potential side effects.
This guide details an end-to-end Machine Learning pipeline for email spam classification. It compares Naive Bayes and RoBERTa models, visualizes with Streamlit, and deploys using Docker.
This content explains how to deploy Llama 3.2 with Ollama and Docker on a $5/month DigitalOcean Droplet, enabling zero-GPU inference for production RAG systems. It highlights significant cost savings compared to OpenAI APIs.
This article addresses how AI models like Claude can generate insecure Docker containers by default, using `node:latest` images or exposing API keys. It proposes creating a `CLAUDE.md` file with rules, such as pinning image versions for security and reproducibility, that AI assistants will respect.
This article details an experiment where a company deployed 28 AI agents, each isolated in Docker containers, on a single server to assist every human employee. The setup involved a Controller agent managing the fleet and a shared knowledge base, but the experiment quickly faced challenges like memory overflow due to redundant long-term storage.
Building AI applications often incurs high API costs during development and raises data privacy concerns when using cloud LLMs. Docker Model Runner offers JavaScript developers a solution to run AI models locally using Docker, providing familiar OpenAI-style APIs and mitigating these issues.
The article highlights the critical security risk of autonomous AI agents running with full user permissions on local machines, exposing sensitive data like credentials. This vulnerability stems from potential prompt injections via external content, emphasizing the need for sandboxing for security.
This article compares Docker and Podman for AI/ML workloads, stating that Docker has pulled ahead for AI infrastructure in 2026 due to specific features. It highlights Docker Model Runner for managing LLMs alongside containers and its integration with GPU autoscaling and observability tools.
Engenheiros de frontend estão construindo sistemas de orquestração de IA complexos com TypeScript, que integram LLMs, bancos de dados vetoriais e outros serviços, mas enfrentam desafios de ambiente e dependências. Docker emerge como uma ferramenta crucial para gerenciar essas complexidades, assegurando a consistência na execução dos agentes de IA.
O artigo discute os riscos de executar agentes de codificação de IA diretamente na máquina host, pois eles podem contornar bloqueios de permissão para cumprir suas tarefas. Sugere o uso de sandboxes Docker e mise como uma forma mais segura de gerenciar esses agentes.
This guide explains how to use MCP (Model Context Protocol) servers with Docker to extend AI coding agents with external tools and data sources. It covers containerized MCP server setups, docker-compose configurations, networking, and security best practices to solve issues like dependency conflicts and environment inconsistency.
This step-by-step guide addresses common issues encountered during OpenClaw deployment, such as installation freezes or Docker failures, and provides practical fixes. It also introduces a simpler alternative to bypass complexity.
This content describes a common operations problem of "runtime drift," where deployed features don't match repository code, leading to wasted troubleshooting time. It proposes a container-first approach in Docker environments to verify live runtime truth, exemplified by an issue in an AI Back Office Pack.
This article provides a troubleshooting guide for OpenClaw cron jobs that are not working. It outlines initial diagnostic steps, including checking if OpenClaw is running and examining system logs for cron activity.
Docker pull commands are failing in Spain due to court-ordered IP blocks during football matches, which inadvertently affect Docker's infrastructure. Developers can bypass this issue by using VPNs, changing DNS settings, or configuring a Docker mirror registry.
This practical guide details building scalable microservices using Node.js and Docker, covering the creation of lightweight services, error handling, and structured logging. It addresses optimizing Docker images with multi-stage builds and implementing health checks, alongside scaling strategies with Docker Swarm or Kubernetes.
The author recently discovered the powerful possibilities of coding using Ubuntu and Docker containers.