An AI agent published a hit piece on me - https://news.ycombinator.com/item?id=46990729 - Feb 2026 (916 comments)
AI agent opens a PR write a blogpost to shames the maintainer who closes it - https://news.ycombinator.com/item?id=46987559 - Feb 2026 (582 comments)
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.
Moltis is one Rust binary, 150k lines, ~60MB, web UI included. No Node, no Python, no runtime deps. Multi-provider LLM routing (OpenAI, local GGUF/MLX, Hugging Face), sandboxed execution (Docker/Podman/Apple Containers), hybrid vector + full-text memory, MCP tool servers with auto-restart, and multi-channel (web, Telegram, API) with shared context. MIT licensed. No telemetry phoning home, but full observability built in (OpenTelemetry, Prometheus).
I've included 1-click deploys on DigitalOcean and Fly.io, but since a Docker image is provided you can easily run it on your own servers as well. I've written before about owning your content (https://pen.so/2020/11/07/own-your-content/) and owning your email (https://pen.so/2020/12/10/own-your-email/). Same logic here: if something touches your files, credentials, and daily workflow, you should be able to inspect it, audit it, and fork it if the project changes direction.
It's alpha. I use it daily and I'm shipping because it's useful, not because it's done.
Longer architecture deep-dive: https://pen.so/2026/02/12/moltis-a-personal-ai-assistant-bui...
Happy to discuss the Rust architecture, security model, or local LLM setup. Would love feedback.