Show HN: Skill that lets Claude Code/Codex spin up VMs and GPUs (cloudrouter.dev)
When an agent writes code, it usually needs to start a dev server, run tests, open a browser to verify its work. Today that all happens on your local machine. This works fine for a single task, but the agent is sharing your computer: your ports, RAM, screen. If you run multiple agents in parallel, it gets a bit chaotic. Docker helps with isolation, but it still uses your machine's resources, and doesn't give the agent a browser, a desktop, or a GPU to close the loop properly. The agent could handle all of this on its own if it had a primitive for starting VMs.
CloudRouter is that primitive — a skill that gives the agent its own machines. The agent can start a VM from your local project directory, upload the project files, run commands on the VM, and tear it down when it's done. If it needs a GPU, it can request one.
cloudrouter start ./my-project
cloudrouter start --gpu B200 ./my-project
cloudrouter ssh cr_abc123 "npm install && npm run dev"
cloudrouter browser open cr_abc123 "http://localhost:3000"
cloudrouter browser snapshot -i cr_abc123
# → @e1 [link] Home @e2 [link] Settings @e3 [button] Sign Out
cloudrouter browser click cr_abc123 @e2
cloudrouter browser screenshot cr_abc123 result.png
What surprised me is how this inverted my workflow. Most cloud dev tooling starts from cloud (background agents, remote SSH, etc) to local for testing. But CloudRouter keeps your agents local and pushes the agent's work to the cloud. The agent does the same things it would do locally — running dev servers, operating browsers — but now on a VM. As I stopped watching agents work and worrying about local constraints, I started to run more tasks in parallel.
The GPU side is the part I'm most curious to see develop. Today if you want a coding agent to help with anything involving training or inference, there's a manual step where you go provision a machine. With CloudRouter the agent can just spin up a GPU sandbox, run the workload, and clean it up when it's done. Some of my friends have been using it to have agents run small experiments in parallel, but my ears are open to other use cases.
Would love your feedback and ideas. CloudRouter lives under packages/cloudrouter of our monorepo https://github.com/manaflow-ai/manaflow.
25 comments
[ 3.4 ms ] story [ 55.0 ms ] thread[1] https://github.com/manaflow-ai/manaflow/issues/1711
https://news.ycombinator.com/item?id=47009617
I much prefer independent, loosely coupled, highly cohesive, composeable, extensible tools. It's not a very "programmery" solution, but it makes it easier as a user to fix things, extend things, combine things, etc.
The Docker template you have bundles a ton of apps into one container. This is problematic as it creates a big support burden, build burden, and compatibility burden. Docker works better when you make individual containers of a single app, and run them separately, and connect them with tcp, sockets, or volumes. Then the user can swap them out, add new ones, remove unneded ones, etc, and they can use an official upstream project. Docker-in-docker with a custom docker network works pretty well, and the host is still accessible if needed.
As a nit-pick: your auth code has browser-handling logic. This is low cohesion, a sign of problems to come. And in your rsync code:
I was just commenting the other day on here about how nobody checks SSH host keys and how SSH is basically wide-open due to this. Just leaving this here to show people what I mean. (It's not an easy problem to solve, but ignoring security isn't great either)We recently also added support for agents: https://skills.sh/dstackai/dstack/dstack
Our approach though is more tide-case agnostic and in the direction of brining full-fledged container orchestration converting from development to training and inference
One suggestion for improvement: Add some more info to your website/GitHub about the need for a provider and which providers are compatible. It took me a bit to figure that out because there was no prominent info about it. Additionally, none of the demos showed a login or authentication part. To me, it seemed like the VMs just came out of nowhere. So at first, I thought "Cloudrouter" was a project/company that gave away free VMs/GPUs (e.g. free tier/trial thing). But that seemed too good to be true. Later, I noticed the e2b.app domain and then I also found the little note way down at the bottom of the site that says "Provider selection" and "Use E2B provider (default)". Then I got it. However, I should mention that I don't know much about this whole topic. I hadn't heard of E2B or Modal before. Other people might find it more clear.
For those that are wondering about this too, you will need to use a provider like https://e2b.dev/ or https://modal.com/ to use this skill, and you pay them based on usage time.
I do with it Pulumi, bc you can write some python or typescript for your infrastructure. But there are many infrastructure as code tools to choose from.
One challenge I keep running into on the other side of this: even with a full VM and browser available, the bottleneck is often teaching the agent what to do. Writing detailed step-by-step instructions for each workflow is tedious and error-prone.
I've been working on a complementary tool (SkillForge — https://skillforge.expert) that takes a different angle: you record your screen doing the task once, and AI extracts every click/keystroke/navigation into a structured skill file the agent can replay.
The combo of something like CloudRouter (agent gets its own machine) + structured skill extraction (agent knows exactly what to do) feels like it could make agent automation much more practical for non-trivial workflows.