# Docker Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/docker
> Markdown URL: https://aitinkerers.org/technologies/docker.md
> Technology record last updated: 2026-09-18T15:14:00Z
> Generated: 2026-09-21T23:41:00Z

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: 147
- Result page: 1 of 7

## Recent Public Talks and Demos

### [Cut your AI and SaaS bill in half](https://barcelona.aitinkerers.org/talks/rsvp_andGu4eTve0)

We built Tarvis, an interface for deploying and managing self-hosted applications, AI tools, and coding workflows across your own servers. I’ll show the working system live, starting with power of self-hosting and self-hosted tools like SearXNG connected to coding agents through MCP and a containerized agent harness with Git access. Then I’ll show how we use Tarvis to deploy and manage these applications, connect tools to different model providers, and show a few other tools that can help reduce the cost of both SaaS and AI.

- Event context: AI Tinkerers Barcelona - September Demo Night — 2026-09-17 — Barcelona
- Public talk page: https://barcelona.aitinkerers.org/talks/rsvp_andGu4eTve0

### [Run your autonomous business like crowd control at music festivals](https://durango.aitinkerers.org/talks/rsvp_l-EjDuk3piI)

Using Paperclip primitives for cases, pipelines, agents and remote runtimes I have (re?)built most of my dark factory for product building, and merge + rollout. Instead of letting the agents roam the field free, I established items (merch, food, beer) and crowd control (lines, queues) along with workers to help merch get signed, food finished properly and cold beers poured. Unlike the music festival, I also established high fidelity "more than just text" agents/visualizations to better observe and collaborate with the system.

- Event context: AI Tinkerers Durango — September Lunch Builder Meetup — 2026-09-15 — Durango
- Public talk page: https://durango.aitinkerers.org/talks/rsvp_l-EjDuk3piI

### [\[Case Study\] 90,000 rejections, 100 meetings: the AI outbound engine I optimized to not send](https://valencia.aitinkerers.org/talks/rsvp_9Hq5aZKj4fk)

An AI-run cold outbound engine for a link-building agency — a CRM I own end to end. It sources prospects, enriches them, finds their contact data automatically, scores them 0–5 against our ICP, then writes personalized multichannel sequences (email + LinkedIn) built from each prospect's website, LinkedIn profile, and recent activity — and hands them to a sender, while every reply flows back into one channel-agnostic thread model I can query in plain language. To date: ~257k prospects processed, 175k messages generated, 1,000+ replies, 100+ sales meetings, and six figures of revenue directly attributable. The counterintuitive part — and what I'll show live — is that the machine is optimized to reject, not to convert. Every send spends finite domain and sender reputation, so between each stage sits a gate whose job is to throw work away: the score gate permanently drops ~90k non-fit prospects before writing a word, and a send-time re-check has blocked 1,591 already-approved messages. Live I'll walk the pipeline dashboard and per-job run history, the score distribution and its cutoff, real generated messages (a good one and one that was embarrassingly wrong), and the per-prospect cost ledger — 1.64M rows — that prices every decision.

- Event context: GTM Teardown August — AI Tinkerers Valencia — 2026-08-11 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_9Hq5aZKj4fk

### [When the AI Judge Is Wrong: A Verifiable Floor for LLM Evaluations](https://dubai.aitinkerers.org/talks/rsvp_e3czbFAtNPA)

I am building Lithrim, an open-source eval harness that pairs a configurable LLM judge council with a deterministic grounding floor. The grounding floor changes an outcome only when it can prove the change against an external oracle. Live on the real UI, I'll grade a few AI-generated clinical notes (no real patient data) using a two-judge council. The judges catch the planted defects, but they also over-flag, so recall is high and precision is low. The floor then clears a provable false positive through a grounding check against a SNOMED CT terminology server. It also independently verifies a real upcoded diagnosis against the terminology graph, without another model call and with the same result on every run. The final verdict accuracy visibly improves, and the floor has never cleared a genuine defect; that counter is shown on every run. Everything shown runs against the live system with a per-run audit trail. The repo is public.

- Event context: AI Tinkerers Dubai — August Demo Day — 2026-08-08 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_e3czbFAtNPA

### ["QA a la velocidad de la luz (y sin fuga de datos): potenciando testing exploratorio con modelos de IA ejecutados 100% en local](https://panama.aitinkerers.org/talks/rsvp_RO5JPx08rOc)

FullQA.ai es una plataforma de QA asistida por IA que genera, documenta y automatiza casos de prueba a partir de la exploración de una aplicación web, ejecutando los modelos de lenguaje y visión completamente en local — sin depender de APIs externas ni exponer datos sensibles. En la demo mostraré en vivo un flujo completo de punta a punta: El sistema funcionando en tiempo real: una extensión de navegador captura la sesión exploratoria (clics, navegación, formularios) y el backend la convierte en documentación de QA estructurada con capturas por paso. Arquitectura: cómo se conectan la extensión, la app de escritorio, el servicio API y los modelos locales servidos vía Ollama, con fallback a disco y generación de imágenes deterministas por paso. Generación de código: cómo se produce automáticamente un script de Playwright listo para ejecutar a partir de la sesión grabada. Repo y logs: enseñaré el código real, la estructura de proyectos con contexto, y los logs de inferencia local corriendo sobre la GPU.

- Event context: AI Tinkerers Panamá: Construyendo el futuro con inteligencia artificial — 2026-07-31 — Panama
- Public talk page: https://panama.aitinkerers.org/talks/rsvp_RO5JPx08rOc

### [Taskless: I Stopped Writing Tasks and Let My Notes Do It](https://islamabad-rawalpindi.aitinkerers.org/talks/rsvp_R83DVSpQNFA)

Taskless is a local automation that reads my raw, messy Obsidian thought-dumps and turns them into ClickUp tasks on its own, no manual task entry, no project-manager busywork. Live, I'll drop a real note dump into the watched vault and you'll watch n8n pick it up, the LLM classify each line as new / done / progress / ignore, and ClickUp create new tasks and close finished ones in real time from plain prose like "fixed the auth login bug today." I'll show the working n8n workflow, the LLM classification + dedup-by-hash steps, the execution logs as it runs, and the repo.

- Event context: AI Tinkerers Islamabad - July 18, 2026 — 2026-07-18 — Islamabad Rawalpindi
- Public talk page: https://islamabad-rawalpindi.aitinkerers.org/talks/rsvp_R83DVSpQNFA

### [Turn your Homelab into a Dark Factory](https://cologne.aitinkerers.org/talks/rsvp_iWZxTrC9hQ8)

I turned my Homelab into a dark factory where I run an orchestrating agent that schedules and delegates work to other agents. There are two parts to this system: the control plane, which handles where agents run and how they run, and the data plane, which handles the actual work you commit (or not). We're going to see how I interact with a personal agent (Hermes, if I have time, I'll show my own agent) and how Hermes decides what to do next depending on our conversation. Instead of making Hermes write the code, it will delegate the tasks to different coding agents running in our own sandbox (built with Firecracker on top of a Proxmox VM). I'll walk you through the different components, how it works and why it matters (in particular the sandbox part).

- Event context: AI Tinkerers Cologne: World Cup Edition — 2026-07-15 — Cologne
- Public talk page: https://cologne.aitinkerers.org/talks/rsvp_iWZxTrC9hQ8

### [Powering Local AI Agents With Real-Time Analytics](https://paris.aitinkerers.org/talks/rsvp_5xC5rIekf1Q)

We'll look at AI agents as a "new user persona" for real-time databases and explore how systems can adapt to their workloads. Finally, we'll demonstrate a fully local agentic workflow: a small open model running on-device with Docker Model Runner, connected through the ClickHouse MCP Server to ClickHouse Cloud — zero API keys,

- Event context: Offstage w/ Docker feat ClickHouse — 2026-07-07 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_5xC5rIekf1Q

### [Job Seeker](https://paris.aitinkerers.org/talks/rsvp_rUKvMBG0Ezg)

I build a software to help to find a job

- Event context: Offstage w/ Docker feat ClickHouse — 2026-07-07 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_rUKvMBG0Ezg

### [Being a dungeon master for agents](https://columbus.aitinkerers.org/talks/rsvp_bciO_4-2f2c)

I built a system where you can DM a contiguous story for agents, describing their light attributes and DM for them entirely via voice. Built with Svelte and node/express

- Event context: AI Tinkerers - Columbus July Meetup — 2026-07-06 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_bciO_4-2f2c

### [Protecting Secrets from Long-running Agents](https://boston.aitinkerers.org/talks/rsvp_qu4vU02kztE)

I built Sesame which is an auth broker proxy for AI agents that protects your secrets from never getting exposed to the agents. Sesame brokers the request and protects you from any unintended actions. Sesame offers full monitoring and alerting mechanisms so human operators can make safe agentic mutation especially in production environments.

- Event context: AI Tinkerers Boston: GTM Agentic AI Launch — 2026-06-29 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_qu4vU02kztE

### [Does your coding harness actually do anything? Measuring it, fairly.](https://boston.aitinkerers.org/talks/rsvp_bx0CiDi1_uo)

Everyone's bolting "harnesses" onto coding agents — Superpowers, GSD, Agent-Skills, Compound Engineering — but does the scaffolding actually make the model build better software, or just feel better? CodingHarness.xyz is an open eval that pits these frameworks head-to-head: same spec, same model, isolated sandboxes, and an evidence-based rubric (does it meet the PRD? is the code any good?). I'll share the surprising result — an inverse-scaling effect lifted straight from a recent Xiaomi paper and reproduced on real runs: harnesses help most exactly where the base model is weakest, and barely at all where it's already strong. Even better, it's framework-dependent — one framework tracks the curve almost perfectly, another inverts it and only helps strong models. Live demo of the eval studio: watch two frameworks build the same app, stream the agent's work turn-by-turn, and see the marginal-gain scorecard light up green/red. Plus a sneak peek at turning framework competitions into a spectator sport (think FIFA bracket, but for coding agents).

- Event context: AI Tinkerers Boston: GTM Agentic AI Launch — 2026-06-29 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_bx0CiDi1_uo

### [Building a Narrative Intelligence Graph and a narrative App from 200+ News Sources Using LLM Agents](https://austin.aitinkerers.org/talks/rsvp_vxPl4WEYVfg)

I built a narrative intelligence system that continuously ingests articles from 200+ news sources and automatically extracts events, actors, narratives, affiliations, and stance changes over time. The system uses multiple LLM-powered agents to transform raw news into a structured intelligence graph that can answer questions such as: • Who is pushing a specific narrative? • Which actors changed their position? • Which organizations consistently align on issues? • How did a narrative evolve over time? During the demo I'll show the live pipeline, extraction workflow, graph construction process, and how the system generates narrative intelligence from real-world news events.

- Event context: AI Tinkerers Austin: GTM Engineering & Agentic Sales Systems — 2026-06-26 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_vxPl4WEYVfg

### [What you can do with a used GPU](https://nurnberg.aitinkerers.org/talks/rsvp_YlYa6jJt-us)

A private Telegram-based personal knowledge assistant that lets me save notes from my phone, store and retrieve them with ChromaDB on a VPS accessible anytime, and ask questions answered by a local llama.cpp model running on my home GPU over a private network i setup. Will show probably everything, or as much as i can in the time i have, but demo's are fun and I want to show how the information propagates through what parts of the code to what parts of my telegram UX and use it on stage with suggestions from the crowd for notes. and maybe also show how we can change the prompt handling this assistant on the fly without touching the code using the agenta prompt management platform. maybe also show how i setup lamacpp for maximum inference time even for low vram machines with big beefy models.

- Event context: AI Tinkerers Nürnberg: June Meetup (Community Hub) — 2026-06-24 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_YlYa6jJt-us

### [From One Startup a Month to Firing Yourself](https://valencia.aitinkerers.org/talks/rsvp_lyo5PIPqYP4)

Sage Router: Locally-hosted open source AI model routing for serious agents. One endpoint. Any provider subscription. The router figures out the rest.

- Event context: AI Tinkerers Valencia June Meetup ft. PostHog — 2026-06-16 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_lyo5PIPqYP4

### [Contextual Synthesis: Engineering a Multi-Agent Context Extraction Pipeline for Stakeholder Audio](https://doha.aitinkerers.org/talks/rsvp_X1LkLLGS_0I)

I built a multi-agent workflow engine that ingests long-form, unstructured stakeholder meeting transcripts and dynamically transforms them into targeted, context-aware corporate assets—including domain-specific LinkedIn posts, official press releases, and structured follow-up emails with clear action items.

- Event context: AI Tinkerers Doha: Round 2 — 2026-06-15 — Doha
- Public talk page: https://doha.aitinkerers.org/talks/rsvp_X1LkLLGS_0I

### [VOXMAP - Painel de diálogo - Analisador de Sentimentos](https://saopaulo.aitinkerers.org/talks/rsvp_f2hd9JW9cho)

Desenvolvi o VOXMAP, um assistente inteligente de atendimento e conciliação que transforma conversas em insights acionáveis. A aplicação permite que equipes analisem interações com clientes (como chats ou atendimentos), gerando automaticamente resumos estruturados, identificação de sentimento, propostas de solução e próximos passos recomendados — tudo em tempo real. Além disso, o sistema oferece análises visuais complementares, como nuvem de palavras e grafos de relacionamento, ajudando a entender padrões, conflitos e oportunidades dentro das conversas. O VOXMAP foi pensado para reduzir o esforço operacional em atendimento, aumentar a clareza nas decisões e acelerar a resolução de conflitos com apoio de IA.

- Event context: AI Tinkerers SP - Meetup de Maio - Kiro & AWS — 2026-05-28 — São Paulo
- Public talk page: https://saopaulo.aitinkerers.org/talks/rsvp_f2hd9JW9cho

### [Edge Power](https://pereira.aitinkerers.org/talks/rsvp_SRDHdkG1fCM)

I built an IoT-based energy monitoring and optimization platform developed with Next.js, designed to collect, process, and visualize real-time data from distributed devices and edge computing systems. During the live demo, I will showcase the working platform, including the web dashboard, real-time device communication workflow, system architecture, Dockerized deployment structure, source code organization, MQTT/WebSocket data flow, logs, and the integration between embedded devices and the visualization platform. I will also present the repository structure and the overall data processing pipeline running in a functional environment.

- Event context: AI Tinkerers Pereira: Builders Session - Engineering Week Edition — 2026-05-21 — Pereira
- Public talk page: https://pereira.aitinkerers.org/talks/rsvp_SRDHdkG1fCM

### [Learnings of integrating OpenClaw & Local LLM into my Home Assistant instance](https://hong-kong.aitinkerers.org/talks/rsvp_4b4SPWWvW4A)

Does OpenClaw elevate my smart home experience on top of the already powerful Home Assistant? By leveraging the OpenClaw add-on in Home Assistant and connect it to its MCP Server, I managed to control part of my smart home via Telegram. I explored the capabilities of the latest local LLMs (or "SLMs"?), such as Gemma 4 E4B, Qwen 3.5 9B, GPT-OSS-20B etc., and experienced some hit-and-misses along the way. I will share what worked well, what worked, and what didn't.

- Event context: AI Tinkerers Hong Kong at AWS: Agentic AI in Action (April) — 2026-04-29 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_4b4SPWWvW4A

### [ibaAgent: Agentic time-series analysis.](https://nurnberg.aitinkerers.org/talks/rsvp_DcXjk1ibxVU)

I developed an agentic system using LangGraph to analyze high-resolution time-series data via natural language. Since high-resolution data is too token-intensive for LLMs, the agent operates "blind" by dynamically writing and executing Python analysis scripts to process data locally. This is supported by a specialized memory architecture that allows the agent to iteratively build an internal map of undocumented, messy data stores, and a rigorous Langfuse experiment pipeline used to tune hyperparameters and prompts for predictable reasoning and precise tool use. Currently transitioning from proof-of-concept to product at iba AG, my demo will showcase LangGraph traces of the agent’s execution and its self-correction within the Python environment. I will also highlight our Langfuse evaluation workflow and how the memory layer enables the agent to navigate complex data structures over time. I’m looking to exchange ideas with fellow tinkerers specifically on memory persistence, planning strategies, and robust evaluation for agentic workflows.

- Event context: AI Tinkerers Nürnberg: April Meetup — 2026-04-22 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_DcXjk1ibxVU

### [Docker Sandboxes](https://paris.aitinkerers.org/talks/rsvp_q-ziE0PuCC8)

AI is everywhere and easier to use. We think it can be even simpler. With AI, we either write a prompt, use a pre-built agent, or code complex agents. We think there's room for ad hoc agents written in seconds, without code. Why? Because AI is often useful for tasks never seen before, and it's a superpower to assemble models, prompts, and tools into a team of agents created for that task.

- Event context: AI Tinkerers Paris: Docker Agentic Workflows (Devoxx Kickoff) — 2026-04-21 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_q-ziE0PuCC8

### [Docker Agent: Simplifying AI Agent Creation](https://paris.aitinkerers.org/talks/rsvp_5hoKr2cgNMw)

AI is everywhere and easier to use. We think it can be even simpler. With AI, we either write a prompt, use a pre-built agent, or code complex agents. We think there's room for ad hoc agents written in seconds, without code. Why? Because AI is often useful for tasks never seen before, and it's a superpower to assemble models, prompts, and tools into a team of agents created for that task.

- Event context: AI Tinkerers Paris: Docker Agentic Workflows (Devoxx Kickoff) — 2026-04-21 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_5hoKr2cgNMw

### [Docker Agent: Simplifying AI Agent Creation](https://paris.aitinkerers.org/talks/rsvp_QMDTiAwXhfE)

AI is everywhere and easier to use. We think it can be even simpler. With AI, we either write a prompt, use a pre-built agent, or code complex agents. We think there's room for ad hoc agents written in seconds, without code. Why? Because AI is often useful for tasks never seen before, and it's a superpower to assemble models, prompts, and tools into a team of agents created for that task.

- Event context: AI Tinkerers Paris: Docker Agentic Workflows (Devoxx Kickoff) — 2026-04-21 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_QMDTiAwXhfE

### [Compose and Dragons: Tiny Language Models in Action](https://paris.aitinkerers.org/talks/rsvp_lNiq-CojExE)

Let's debunk some beliefs about (very) small LLMs, those that make less than 4b of parameters. We often hear: They are useless and do not know how to do anything, they know nothing, they are bad at calling (so no MCP) This is partly wrong, and we can fix the rest and build generative AI systems with these very small models. Among other things, we will see how to create NPCs with a personality, a master dungeon that will manage your movements, fights... and allow you to talk to this or that NPC ...

- Event context: AI Tinkerers Paris: Docker Agentic Workflows (Devoxx Kickoff) — 2026-04-21 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_lNiq-CojExE

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