quackd：用统一命令行协调机器人

One CLI for all your robots. Connect them, command them, and let them work together, each with an LLM for a brain (Claude, OpenAI, Gemini…

quackd One CLI for all your robots. Connect them, command them, and let them work together, each with an LLM for a brain. quackd, pronounced “quacked”, began as the brain daemon the Microduck was missing, named like that robot's own robotd, mediad, padd and tofd. That is where the ducks come from: a task is a .duck file and a group of robots is a flock. Nine robots today, one of them driven on real hardware and the other eight still in a simulator or a mock, and one of them a duck you can print and build yourself. Register each robot once, by name. Then state a goal from a terminal or from a chat with Claude, to one robot or to a flock of them, and the same contract decides which of each robot's skills its model may use, how many steps it gets, and when it has to ask you first. Hey, my name is Rok and this is why I built quackd 👋 I see quackd as a ChatGPT like moment for robotics. Let me explain what I mean. LLMs existed long before ChatGPT. What ChatGPT actually did was take LLMs and hand them to ordinary people in a chat interface everyone already knew, like Facebook Messenger or Instagram. That was the real unlock. Right now, in 2026, most people still think robots belong in science fiction movies or in a lab at Tesla. That is not true anymore. There are already open source robots you can build yourself for under $1000. And they actually work. They can go to your fridge, open it, grab a can of Coke, close the fridge and bring it to you. The problem is they have a huge limitation. You can teach them dozens of moves, like "get a coke". But the robot itself is still dumb. It knows the moves, it just cannot connect them on its own. For robots to become truly useful, they need to become AI first and agentic. You give them a goal and they figure out the steps themselves. To get there, robots need a brain. And here is the catch. Today's robots simply do not have enough hardware on board to think, reason and plan. Their skull is too small for the brain this kind of intelligence needs. So the brain has to live outside the robot, in the cloud or on your own computer, where it can grow as big as you need. The robot itself stays small and light while all the heavy thinking happens somewhere else. That is what quackd started as. A brain for one robot. But here is what I think comes next. In the future everyone will have a flock of robots. At home, in the office, wherever. And they will not all be the same robot. Different types, different capabilities, even different companies. The first challenge is having all of them in ONE place to command. That is what quackd is now. You connect every robot you own to one CLI, and you command all of them from there, with an LLM as the brain of each one. But even when you have them all in one place, that is still not enough. What you ask for will be complex, and robots will probably be very specialized. One can walk, one can grab, one can carry. For a bunch of different robots to be useful with as little of your involvement as possible, they need to start working together towards the goals you give them. Which means they need to communicate between each other. So that is the other half of quackd. You give the flock a goal, each robot's brain reads what the others can do, and they divide the work between themselves. Imagine telling your robots "I want to eat and drink something". The one with wheels goes to the fridge, checks what is inside and tells the others what it found. The one with arms grabs a plate and some cutlery. One of them brings it all to you and asks what you would like, you choose, and they go back for the food and wish you a good meal. Sounds like science fiction, right? We are closer than you think! That is basically what ChatGPT did for LLMs. It took something powerful and put it in one place everyone could reach. And that is why I see quackd as a ChatGPT like moment for robotics. — Rok Benko, September 2026 A real arm, a real model, one sentence. An SO-101 follower arm on 2026-09-15, piloted by OpenAI's gpt-6-astra through quackd run --goal "Wave to the camera with an extended arm". The model was handed the arm's five verbs and its datasheet and chose one call at a time: raise the shoulder and elbow, roll the wrist four times, return, stop. Nobody wrote a wave. The whole run at ten times speed, filmed on a phone: it opens on the command going into a terminal, watches the run scroll, then pans to the bench, and the wave itself is the part where the wrist rocks. What the model was told, all ten of its calls and what went wrong are in What happened in that run, and how every recording here was made is in docs/assets. quackd is a command line for the robots you own. Each one joins through an adapter that declares, as a manifest, what the body is and what it can do, and you register it once by name with how to reach it. Give a robot a goal like "find the ball and kick it" and a large language model picks one skill at a time from the list that manifest declares, quackd runs it, looks at the camera, and asks again until the job is done or clearly impossible. Give the same goal to a flock and every robot in it gets a model of its own, and they divide the work by telling each other what they are going to do. A goal can arrive from a chat, a command line or a .duck task file, and whichever way it comes, quackd enforces a contract the model cannot talk its way out of: which skills are allowed, how many steps, when a human must say yes, when to abort. Claude, OpenAI, Gemini, Grok, Mistral, DeepSeek, Cohere, Qwen, Kimi, GLM and Meta work over their APIs, and so do the models OpenRouter routes to, behind one key of its own. Open source models work on your own machine through Ollama, vLLM, llama.cpp or LM Studio, with no key. A model is the pilot on every run. --decision-llm optionally puts something in front of it that is not a language model and cannot be used as one: a decision LLM generates no text at all, it scores a fixed set of options against a sta