# Docker Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/docker?page=2
> Markdown URL: https://aitinkerers.org/technologies/docker.md?page=2
> Technology record last updated: 2026-02-22T17:49:29Z
> Generated: 2026-08-24T18:35:36Z

Docker is the open-source platform that packages applications and dependencies into standardized, portable containers for consistent execution across any environment.

Docker is the industry-standard containerization platform, enabling developers to build, ship, and run applications efficiently. It uses the Docker Engine (the core runtime) to create lightweight, isolated environments called containers: these units bundle an application’s code, libraries, and configuration. This self-contained approach guarantees consistency, eliminating the 'it works on my machine' problem across development, testing, and production environments (local workstations, cloud, or on-premises). Docker debuted in 2013 and now serves over 20 million developers monthly, simplifying complex workflows like CI/CD and microservices architecture by leveraging tools like Docker Hub for image sharing and Docker Compose for multi-container applications.

- Official technology site: https://www.docker.com
- Public AI Tinkerers demos and talks: 144
- Result page: 2 of 6

## Recent Public Talks and Demos

### [Openclaw for token-efficient business needs](https://ho-chi-minh-city.aitinkerers.org/talks/rsvp_gbkGqEbMvy8)

I built flowser, a platform that enable non-technical users to use openclaw easily with built-in integrations, security, and real life business workflows.

- Event context: AI Tinkerers Ho Chi Minh City: AI Coding Agents & Orchestrators — 2026-04-18 — Ho Chi Minh City
- Public talk page: https://ho-chi-minh-city.aitinkerers.org/talks/rsvp_gbkGqEbMvy8

### [Soul, Memory, Skills, Brain: Building a Zero-PII Personal AI Agent in Docker](https://austin.aitinkerers.org/talks/rsvp_11W1r8whPMs)

Rent-An-Agent.AI is a self-hostable personal AI agent platform where each agent is a four-layer artifact — Soul (persona in SOUL.md), Memory (SQLite+FTS5 with daemon-thread fact extraction), Skills (52 marketplace skills exposed through an OpenAI-compatible bridge API), and Brain (user-configurable LLM: DeepSeek, Ollama, Claude). It runs on a Mac Mini in Austin, ships as Docker containers on GitHub, exposes itself via Cloudflare Tunnel, and uses a 48-character hex token as the only identity — no email, no password, no PII. Live demo: provision a fresh agent in 10 seconds with persona picker, have a conversation, show the daemon thread silently extracting facts into SQLite, rewrite the Soul mid-conversation („Luna, sei ab jetzt formeller"), fire a marketplace skill call through the Bridge, and settle a tier upgrade with a real BOLT11 Lightning invoice via the Alby webhook.

- Event context: AI Tinkerers Austin: April 2026 Demo Night — 2026-04-16 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_11W1r8whPMs

### [Computer Vision Pipeline for Drone Image Processing](https://st-louis.aitinkerers.org/talks/rsvp_yf6HEWzPJW4)

With Claude Code and GitHub SpecKit, I built a local web app which allows me to upload images, define regions of interest, and spin up a docker container to process images from my drone to do things like count cars. The container is CUDA aware and uses the GPU for faster processing. Single image processing takes about 30 seconds per image (super easy to demo quickly)

- Event context: AI Tinkerers St. Louis: April 2026 Meetup — 2026-04-14 — St. Louis
- Public talk page: https://st-louis.aitinkerers.org/talks/rsvp_yf6HEWzPJW4

### [Independent Agents to Hive Mind - agent/chatbot/human collaboration via hiveWiki.ai](https://upstate-ny.aitinkerers.org/talks/rsvp_l5dVgas09AQ)

Wiki-based agent collaboration tool accessed by chatbots and agents via MCP - and by humans via the wiki artifact. Other systems try to workflow agents like employees. I'll demonstrate Claude Chat, a human, and Cursor Agent collaborating as experts using a shared, human-usable context to design and deploy a feature.

- Event context: AI Tinkerers Upstate NY: Agents and Automation — 2026-04-14 — Upstate NY
- Public talk page: https://upstate-ny.aitinkerers.org/talks/rsvp_l5dVgas09AQ

### [Prediciendo el futuro con inteligencia de enjambre](https://manizales.aitinkerers.org/talks/rsvp_QR6Nx_VHh04)

MiroFish es un motor de predicción con IA de nueva generación basado en tecnología multi-agente. La inteligencia de enjambre lleva acompañándonos mucho tiempo, pero MiroFish lleva este concepto al siguiente nivel: mediante la extracción de información semilla del mundo real (noticias, señales financieras, borradores de políticas), construye automáticamente un mundo digital paralelo de alta fidelidad donde miles de agentes inteligentes con personalidades independientes, memoria a largo plazo y lógica conductual interactúan libremente. Puedes inyectar variables dinámicamente para deducir trayectorias futuras con precisión. En esta charla presentaremos MiroFish-ES, el fork en español del proyecto, y haremos una demo en vivo de sus capacidades de predicción.

- Event context: 🚀 ¡14vo Encuentro de AI Tinkerers Manizales! 🤖 — 2026-03-25 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_QR6Nx_VHh04

### [Framewrok de Agentes OpenClaw](https://pereira.aitinkerers.org/talks/rsvp_kdDC8bs2SDg)

Visión general de las capacidades de OpenClaw y una revisión completa de su arquitectura.

- Event context: AI Tinkerers Pereira — The Builders Lab — 2026-03-25 — Pereira
- Public talk page: https://pereira.aitinkerers.org/talks/rsvp_kdDC8bs2SDg

### [\[UofT\] Print Anything: Building the Pipeline from Natural Language to 3D Object](https://toronto.aitinkerers.org/talks/rsvp_z0pA85I3o0k)

What if you could 3D print something simply by describing it in plain English, just as you ask ChatGPT a question? That is the idea behind our project. Our system fine-tunes a Qwen 3 model with LoRA in Google Colab to generate ImplicitCAD code from natural language prompts. The generated code is then compiled into STL files, which can be used for 3D printing. ImplicitCAD is a code-based CAD tool, similar to OpenSCAD. Many people in the 3D printing community like it because script-based design allows precise and reproducible measurements. However, the downside is that users need to know how to write code. To address this, we built a two-phase training pipeline. In the first phase, the model learns ImplicitCAD syntax using real examples collected and filtered from the Thingiverse dataset on HuggingFace. This helps the model understand how 3D geometry is written in code. In the second phase, we introduce logical and spatial reasoning problems so the model can go beyond simple pattern matching. In addition to the model, we also built a web-based preview environment and a Dockerized version of ImplicitCAD. This removes the usual edit-compile-wait workflow that makes these tools difficult to use. The project is meant to be a starting point that future teams can build on and extend.

- Event context: AI Tinkerers Toronto - March - hosted by Mozilla! — 2026-03-25 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_z0pA85I3o0k

### [What If Your OS Was Built for Agents, Not Humans?](https://la.aitinkerers.org/talks/rsvp_7OKp4tR0VHs)

AitherOS is a full operating system kernel for autonomous AI agents. Where traditional frameworks treat agents as stateless function calls, AitherOS gives every agent a persistent identity, a five-tier memory hierarchy, cryptographically signed capability tokens, and native inter-agent communication — the same primitives an OS kernel provides to processes, elevated to the agent abstraction. The system orchestrates 29 specialized agents across 12 architectural layers, from bare infrastructure through cognition, perception, memory, security, and training. Every request passes through intent classification and effort-based model routing, dynamically selecting the right LLM weight class for the task — from a 1B parameter model for simple lookups to a 14B reasoning model for complex planning — reducing inference costs by up to 90% without sacrificing capability. When a problem demands it, the swarm coding engine marshals 11 agents through a four-phase pipeline — architect, build, review, judge — producing tested, reviewed code autonomously. Security is not bolted on. Every agent capability is gated by HMAC-SHA256 signed tokens under a default-deny policy. Agents cannot acquire permissions they were not explicitly granted. The system continuously improves itself through a pain-driven learning loop: execution failures are captured, analyzed, and converted into corrective training examples that feed back into model fine-tuning automatically. AitherOS ships with an open Agent Development Kit supporting the MCP protocol, enabling third-party agents to plug into the full OS stack. The system running at demo.aitherium.com is not a prototype — it is the production deployment.

- Event context: AI Tinkerers LA: March Builder Meetup & Live Demos — 2026-03-20 — Los Angeles
- Public talk page: https://la.aitinkerers.org/talks/rsvp_7OKp4tR0VHs

### [VLLM and Qdrant - GPU goes Brrrr!](https://manchester-nh.aitinkerers.org/talks/rsvp_RGPw96tcjiA)

This demo goes over the fundamentals of VLLM and the QDrant vector database. We'll spin up some Docker containers with the LLM, Database and Embedding model, and then run some interesting benchmarks. I'll demonstrate just how much more powerful VLLM can be on hardware when compared to sequential model runners.

- Event context: AI Tinkerers Manchester (Bedford), NH - March 2026 Meetup — 2026-03-18 — Manchester NH
- Public talk page: https://manchester-nh.aitinkerers.org/talks/rsvp_RGPw96tcjiA

### [Elasticity, scale and production realities](https://paris.aitinkerers.org/talks/rsvp_g6P1G7Bvt40)

When does elasticity outweigh the benefits of owning compute? What are the hidden costs of staying 100% cloud for AI experimentation? How do hybrid architectures actually look in production environments today? How to store data in a secure and GDPR way? How to easily ship MCP and use database on cloud? ( PAAS)

- Event context: High-Performance Local AI Development: Kick-off ThinkStation PGX — 2026-03-17 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_g6P1G7Bvt40

### [Benchmarking Small Language Models Where It Actually Matters](https://paris.aitinkerers.org/talks/rsvp_ow7csxTS5J0)

Most SLM benchmarks answer the wrong question. They tell you how a model scores — not whether it works. This platform is designed for teams who care about real execution, not paper metrics. It lets you benchmark Small Language Models on Python and Polars code generation, under strictly controlled hardware conditions, with full visibility on performance, cost, and failure modes. Participants connect to the platform through a web interface and run benchmarks on large datasets and realistic workloads. The backend runs inside a Docker environment and can execute jobs directly on GPUs, whether locally or on dedicated infrastructure. Each run is configurable: quantization, decoding parameters, and runtime settings are part of the experiment, not hidden defaults. The focus on Polars is intentional. Most language models are very good at generating code for older, widely used libraries like pandas or NumPy. These libraries have been present in training data for years and appear in millions of examples online. Polars is different. It is a newer, high-performance data processing library designed for large-scale workloads, built around vectorized execution, query planning, and expression-based transformations. While it offers major performance advantages for large datasets, its programming model is significantly different from traditional Python data tools. As a result, many models struggle with it. They may generate code that looks correct but fails to run, produces incorrect results, or uses inefficient patterns that defeat the performance benefits of the library. This makes Polars an ideal stress test for evaluating whether a model truly understands modern data-processing workflows. Beyond model metrics (tokens/sec, VRAM usage, GPU utilization), the platform evaluates the generated code itself: Does it run? Does it produce the correct result? Is it efficient, or just “technically correct but slow”? To make progress measurable and engaging, the platform also supports a hackathon-style workflow: Real-time leaderboards Full attempt history per team and per benchmark Analytics dashboards highlighting where models systematically fail The goal is simple: give teams a new language to talk about model quality — not “good vs bad,” but correct, efficient, reliable, and production-ready. If you don’t measure this, you might end up shipping models that look good in demos… and quietly break in real workloads.

- Event context: High-Performance Local AI Development: Kick-off ThinkStation PGX — 2026-03-17 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_ow7csxTS5J0

### [Building a Live Digital Ecosystem Intelligence Layer (DEIP.app)](https://valencia.aitinkerers.org/talks/rsvp_0XSNT1ucZeg)

Most companies don’t actually know how their digital ecosystem works - they just use tools. In this demo, I’ll show how I’m building DEIP.app - a live intelligence layer that maps tools, costs, dependencies, data flows, and AI agents into a structured system that both humans and AI can understand. I’ll walk through: - The data model (how tools, services, AI, Workflows, people, and costs are structured) - How I built the hierarchy (categories → subcategories → items → relations) - How cost calculations work - How I connect it to AI agents via MCP/APIs - How relations are visualized (1D lists → 2D maps → 3D/VR maps) - How does this becomes usable context for AI systems

- Event context: AI Tinkerers Valencia March Meetup — 2026-03-17 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_0XSNT1ucZeg

### [Speech to Music with Story Jam](https://portland.aitinkerers.org/talks/rsvp_sxCV-CokrdM)

I developes music generation systems empowered by Elevenlaps Speech transcription I have a demo for audience to try live to create chord stories from their words

- Event context: AI Tinkerers Portland: Building Voice Agents & Conversational AI Stacks — 2026-03-06 — Portland
- Public talk page: https://portland.aitinkerers.org/talks/rsvp_sxCV-CokrdM

### [Flexible RPA with Playwright MCP and Deep Agents](https://columbus.aitinkerers.org/talks/rsvp_gJn6jUGLYFQ)

How do we build RPA that doesn't take more time fixing it, than writing it? By using LLMs! Let me show you how I built out sessionized, distinct, parallelisable RPA process using pydantic deep-agents, the Playwright MCP, and a little bit of creativity to take web automation to the next level!

- Event context: AI Tinkerers - Columbus March Meetup — 2026-03-02 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_gJn6jUGLYFQ

### [Securing OpenClaw for fun and profit](https://nyc.aitinkerers.org/talks/rsvp_K_o1lgWmG00)

Talk through how I built z0, an OpenClaw host and through the various security mechanisms, tools, approaches that I integrated to take open claw from a fun individual agent to one that can be possibly run in a corporate settings.

- Event context: 🦞ClawHack NY with Convos, ft ElevenLabs, Convex, & more — 2026-02-28 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_K_o1lgWmG00

### [HQ: Construyendo un sistema multiagente con openclaw](https://manizales.aitinkerers.org/talks/rsvp_EoIkJJzrSxg)

¿Qué pasa cuando un "squal lead" IA gestiona su propio equipo de agentes especializados? En esta charla exploraremos HQ, un sistema de orquestación de agentes con openclaw que permite colabora entre agentes

- Event context: 🚀 ¡13vo Encuentro de AI Tinkerers Manizales! 🤖 — 2026-02-26 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_EoIkJJzrSxg

### [De la idea a la implementación: cómo convertir "charlas" en proyectos reales](https://manizales.aitinkerers.org/talks/rsvp_kc6m3ZReNlg)

En esta charla voy a profundizar en cómo ha sido el proceso detrás de los diferentes proyectos que hemos mostrado a lo largo del año en AI Tinkerers Manizales: desde la primera chispa de la idea, pasando por la fase de experimentación, hasta llegar a prototipos funcionales y usos reales. Voy a compartir qué cosas funcionaron, qué no, qué atajos tomé, qué errores cometí y qué aprendí en el camino, para que otros tinkerers puedan acelerar sus propios proyectos de IA y llevarlos más rápido del “sería chévere hacer esto” a “ya lo tengo corriendo”.

- Event context: Cierre 2025 de AI Tinkerers: 11.º meetup del capítulo Manizales — 2026-02-26 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_kc6m3ZReNlg

### [From Benchmark to Agent: A2A‑Driven Werewolf Arena for Evaluating LLM Agents](https://bogota.aitinkerers.org/talks/rsvp_w6B43riADns)

Werewolf Arena is a multi‑agent social‑deduction benchmark built on the AgentX–AgentBeats stack, developed as part of the second cohort of TribuPapers and the 2025 Berkeley Agentic AI MOOC, and submitted to the AgentX–AgentBeats competition. It instantiates the Werewolf game as an 8‑player environment where a Green Agent orchestrator manages role assignment, day/night phases, interaction flow, and ELO‑style ratings, while multiple LLM‑driven Purple Agents connect via the A2A protocol to debate, vote, and execute actions under partial information. The project provides a research‑oriented evaluation pipeline that stress‑tests core agentic capabilities such as probabilistic reasoning, persuasion, deception and detection, role‑conditioned behavior, and narrative consistency in noisy multi‑agent settings. Each game produces both quantitative metrics (win‑rate by role, survival, vote accuracy, ELO adjusted by opponent strength) and qualitative scores via an LLM‑as‑a‑Judge component inspired by G‑Eval, which rates agents along multiple dimensions (reasoning quality, persuasive power, deceptive skill, adaptability, and consistency) and generates textual justifications. Technically, the system is implemented in Python with FastAPI for Green/Purple A2A endpoints, uses OpenAI LLMs (configurable models such as gpt‑4o‑mini) for both policies and judge, exposes a JavaScript/HTML/CSS leaderboard UI, and is fully containerized with Docker/Docker Compose and automated via GitHub Actions CI to run tournaments and update metrics reproducibly. The project was developed collaboratively by Daniel Santiago Sandoval Higuera, Sadid Alexis Romero Mahecha, Julian Anibal Henao Garcia, and Andres Felipe Garcia Sanchez.

- Event context: AI Tinkerers Bogotá: El Primer Meetup de 2026 — 2026-02-26 — Bogotá
- Public talk page: https://bogota.aitinkerers.org/talks/rsvp_w6B43riADns

### [Building a Digital Supermax: Architecting Air-Gapped Runtimes for Untrusted Agents](https://dc.aitinkerers.org/talks/rsvp__aic7NqY23M)

I will walk through my design of a system capable of hosting isolated, zero egress, OpenClaw/ClawdBot/MoltBot instances. The system is, in principle, scalable to all AI agents. I can discuss the cloud infrastructure that makes this possible at the network level and still allows OpenClaw to run as root within the VM while allowing end-users to run untrusted code. Think: Codex Cloud but for OpenClaw.

- Event context: AI Tinkerers - DC Metro Meetup - February 24th, 2026 — 2026-02-24 — DC
- Public talk page: https://dc.aitinkerers.org/talks/rsvp__aic7NqY23M

### [SecureClaw: Building a Secure AI Agent Wrapper, Live Dev Workflow](https://tokyo.aitinkerers.org/talks/rsvp_VtmrCXF7m0Q)

Carapace (a.k.a. SecureClaw) is a security layer for AI agents that prevents credential leakage and unauthorized access. It network-jails the agent in Docker with no DNS, routes all traffic through a MITM proxy with phone-based domain approval, and hides API credentials behind restrictive host-side proxies. The agent can read your email but never sees the API key, and can't send without you.

- Event context: AI Tinkerers Tokyo - Toranomon Meetup - February 19, 2026 — 2026-02-19 — Tokyo
- Public talk page: https://tokyo.aitinkerers.org/talks/rsvp_VtmrCXF7m0Q

### [GPU-Streaming Omniverse to the Browser Per‑Org Sessions over WebRTC](https://miami.aitinkerers.org/talks/rsvp_Mq1fLtz3A-E)

Live build walkthrough of a prototype that spins up an isolated NVIDIA Omniverse/Kit session per organization, then streams it into a standard web browser via WebRTC. I’ll show the end-to-end flow: request → provision Azure GPU instance → boot the Kit runtime → establish a WebRTC connection → interact in-scene → teardown/cleanup. Focus is on the state machine, orchestration, and the practical failure modes (startup latency, ICE/networking, cleanup).

- Event context: AI Tinkerers Miami Meetup — The 2026 Builder Kickoff — 2026-02-11 — Miami
- Public talk page: https://miami.aitinkerers.org/talks/rsvp_Mq1fLtz3A-E

### [Radiology Lab - Medical Imaging Segmentation Lab](https://raleigh.aitinkerers.org/talks/rsvp_M1DsMV5sU6A)

An early preview of a web application built to create segmentation and 3D modeling training data directly within research and clinical workflows in Medical Imaging, using SAM3 and Gaussian Splatting APIs. It supports multiple imaging modalities; including Cardiac MRI, Cardiac CT, CCTA, and Brain MRI. This training data can be quickly plugged in to fine-tune or build machine learning models, which can loop back into the web application. Creating an iterative cycle of improving machine learning models, segmentations and 3D models across all modalities in Medical Imaging.

- Event context: AI Tinkerers Raleigh Meetup — February 11, 2026 — 2026-02-11 — Raleigh
- Public talk page: https://raleigh.aitinkerers.org/talks/rsvp_M1DsMV5sU6A

### [Building a Cloud-Hosted AI Agent Sandbox with Claude Agent SDK](https://seattle.aitinkerers.org/talks/rsvp_ZYnsYVYcytM)

This presentation will cover the steps taken to build a GCP-hosted AI agent sandbox with support for the Claude Agent SDK. All deployment and configuration is done via API (MCP support coming soon) so users can deploy new agents and delegate tasks to be completed async. Since it's API accessible, this works with any coding agent to give your agents sub-agent superpowers!

- Event context: AI Tinkerers Seattle: January Meetup — 2026-01-31 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_ZYnsYVYcytM

### [building and app with claude code vs cursor](https://valencia.aitinkerers.org/talks/rsvp_5jcbVV0_M7U)

I am building an app twice. Once with cursor and once with claude code. Also included is a local LLM using Ollama. The audience should see how to get set up to develop with both tools.

- Event context: AI Tinkerers Valencia January Meetup — 2026-01-29 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_5jcbVV0_M7U

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