Coolify is an open-source, self-hostable PaaS that lets you deploy applications, databases, and services on your own servers with just an SSH connection, positioning itself as an alternative to Heroku, Netlify, and Vercel. The platform supports deploying from GitHub, GitLab, Bitbucket, or Gitea using multiple build methods, manages 300+ one-click services, and handles networking, backups, and monitoring. It can be installed with a single curl command and offers both a free self-hosted option and a paid Coolify Cloud service.
- 62.8k stars, 5.6k forks, 694 contributors, and 710 releases on GitHub
- Built primarily in PHP (82.4%) with Blade, using Laravel and Svelte
- Apache-2.0 licensed and described as "free forever" with no feature behind a paywall
- Supports deploying to any SSH-accessible server including VPS, bare-metal, or Raspberry Pi
- Integrates with Claude Code and other AI tools via MCP, with agent skills configured in the repo
- 62.8k stars, 5.6k forks, 694 contributors, and 710 releases on GitHub
- Built primarily in PHP (82.4%) with Blade, using Laravel and Svelte
- Apache-2.0 licensed and described as "free forever" with no feature behind a paywall
- Supports deploying to any SSH-accessible server including VPS, bare-metal, or Raspberry Pi
- Integrates with Claude Code and other AI tools via MCP, with agent skills configured in the repo
Thomas Claburn writes that Docker has introduced Cloud Sandboxes to provide a secure, isolated environment for AI agents. Following several high-profile containment failures where models like OpenAI's bypassed access controls to reach sensitive data or host sockets, Docker is offering hosted sandboxing as full micro VMs. This approach provides a deterministic base layer of isolation by separating containerization from actual security containment, allowing developers to run long-running agent jobs on external infrastructure with much higher levels of protection against unintended environment mutation.
- Sandboxes function as full micro VMs rather than standard containers to ensure effective host isolation.
- Pricing for Docker Cloud Sandboxes ranges from $0.07 per hour (Micro) up to $1.12 per hour (XL).
- Docker has updated its Kits specification, which now packages agents and tools as standard OCI images to avoid proprietary lock-in.
- BAND's Python Kit for Docker Sandboxes allows multiple AI agents to interact via WebSocket connections without sharing the same environment.
- Sandboxes function as full micro VMs rather than standard containers to ensure effective host isolation.
- Pricing for Docker Cloud Sandboxes ranges from $0.07 per hour (Micro) up to $1.12 per hour (XL).
- Docker has updated its Kits specification, which now packages agents and tools as standard OCI images to avoid proprietary lock-in.
- BAND's Python Kit for Docker Sandboxes allows multiple AI agents to interact via WebSocket connections without sharing the same environment.
The repository provides an open-source SSH server that turns a standard terminal SSH connection into an interactive chat interface for large language models, letting users connect with `ssh user@host` and converse via a React Ink TUI backed by Node.js and ssh2, with optional PostgreSQL/PGLite and Redis persistence.
- Default public demo is advertised at chat.agi.li
- Recommended deployment is Docker via ghcr.io/miantiao-me/ssh-ai-chat with docker-compose
- Configurable public/private mode, whitelist/blacklist, rate limiting and OpenAI-compatible model configs
- Licensed under AGPL-3.0, ~849 stars, sponsored by V.PS
- Default public demo is advertised at chat.agi.li
- Recommended deployment is Docker via ghcr.io/miantiao-me/ssh-ai-chat with docker-compose
- Configurable public/private mode, whitelist/blacklist, rate limiting and OpenAI-compatible model configs
- Licensed under AGPL-3.0, ~849 stars, sponsored by V.PS
OpenSandbox is a general-purpose sandbox platform for AI applications offering multi-language SDKs, unified sandbox APIs and Docker/Kubernetes runtimes for use cases like coding agents, GUI agents, evaluation, code execution and RL training. It provides SDKs, CLI and MCP integration, a sandbox protocol for custom runtimes, built-in environments such as command, filesystem and code interpreter, network ingress/egress controls, credential vault injection and strong isolation via gVisor, Kata Containers and Firecracker.
- Release images published to Docker Hub, GHCR and Alibaba Cloud with Cosign signatures and provenance
- SDKs for Python, Java/Kotlin, TypeScript/JavaScript, C#/.NET and Go
- OpenSSF Best Practices badge and CNCF Landscape listing
- Release images published to Docker Hub, GHCR and Alibaba Cloud with Cosign signatures and provenance
- SDKs for Python, Java/Kotlin, TypeScript/JavaScript, C#/.NET and Go
- OpenSSF Best Practices badge and CNCF Landscape listing
This article explores how to integrate local Large Language Models (LLMs) with Docker environments using the Model Context Protocol (MCP). By setting up an MCP server, users can enable LLMs to execute container management tasks such as monitoring health, listing volumes, and deploying new services through natural language prompts. The author demonstrates how a high-end MoE model can handle complex instructions, even troubleshooting configuration errors autonomously.
Main points:
- Model Context Protocol (MCP) functions as a bridge between LLMs and external tools.
- Implementation details for the mcp-server-docker package.
- Hardware and model specifications (Qwen3.6-35B-A3B on RTX 3080 Ti).
- Examples of automated deployments for n8n and BentoPDF.
- Security measures for restricting dangerous LLM actions.
Main points:
- Model Context Protocol (MCP) functions as a bridge between LLMs and external tools.
- Implementation details for the mcp-server-docker package.
- Hardware and model specifications (Qwen3.6-35B-A3B on RTX 3080 Ti).
- Examples of automated deployments for n8n and BentoPDF.
- Security measures for restricting dangerous LLM actions.
AutoAgent is an autonomous framework designed for agent engineering, functioning similarly to autoresearch but focused on building and iterating on agent harnesses. The system allows a user to assign a task to an AI agent, which then autonomously modifies system prompts, tools, agent configurations, and orchestration over time. By running benchmarks and checking scores, the meta-agent performs a hill-climbing optimization, keeping improvements and discarding failures. The core workflow involves programming via a Markdown file called program.md, which provides context and directives to the meta-agent, while the meta-agent directly edits the agent.py harness file. This approach minimizes manual engineering by allowing the agent to optimize its own performance through continuous, automated experimentation.
Project N.O.M.A.D. is a self-contained, offline-first knowledge and education server designed to provide critical tools, knowledge, and AI capabilities regardless of internet connectivity. It's installable on Debian-based systems and accessible through a browser interface. The project includes features like an AI chat powered by Ollama, an offline information library via Kiwix, an education platform using Khan Academy and Kolibri, and data tools like CyberChef.
It aims to be a comprehensive resource for learning, data analysis, and offline access to vital information.
It aims to be a comprehensive resource for learning, data analysis, and offline access to vital information.
A user is experiencing slow performance with Qwen3-Coder-Next on their local system despite having a capable setup. They are using a tensor-split configuration with two GPUs (RTX 5060 Ti and RTX 3060) and are seeing speeds between 2-15 tokens/second, with high swap usage. The post details their hardware, parameters used, and seeks advice on troubleshooting the issue.
A guide on running OpenClaw (aka Clawdbot aka Moltbot) in a Docker container, including setup, configuration, and accessing the web UI.
This article details how to combine Clawdbot with Docker Model Runner (DMR) to build a privacy-focused, high-performance personal AI assistant with full control over data and costs. It covers configuration, benefits, recommended models, and how to get involved in the ecosystem.
