harshdoesdev

thanks! you can also checkout demos built using webmotion @ https://webmotion.superhq.ai/.

Hi guys, we are super excited about the launch of remote.superhq.ai - remote control for your dev environment. please do check it out and share your feedback.

i too wanted to purchase 5-6 3D printers and start a business - basically my version of goose farming after i leave the software dev space for the greater good of mankind :)

bhatti's cli looks very ergonomic! great job!

also, yes, shuru was (still) a wrapper over the Virtualization.framework, but it now supports Linux too (wrapper over KVM lol)

Someone built a project called AgentFM that tries to use a peer-to-peer network of everyday computers to run AI workloads, similar to what SETI@home used to do for crunching radio telescope data.

nice! for most local workloads, it is actually sufficient. so, do you ship a complete disk snapshot of the machines?

+1. i built something similar called shuru.run because i wanted an easy way to set up microVM sandboxes to run some of my AI apps, and firecracker wasn't available for macOS (and, as you said, it is just too heavy for normal user-level workloads).

its a really innovative idea! very interested in the subsecond coldstart claim, how does it achieve that?

had to wait for two minutes just to refresh the PR page. their UI was always a bit clanky, this new wave of "AI productivity" is just making it worse.

thanks! depends on your machine but it is surprisingly lightweight since it uses Apple's Virtualization.framework under the hood. I have comfortably run 3-4 sandboxes on an 8GB Mac. you can also configure CPUs, memory and disk per sandbox from the settings.

glad you liked it! I am currently exploring options for Linux support. will share an update soon.

lume is a much more full featured VM manager, macOS and Linux VMs, API server, prebuilt images, python SDK etc. shuru is intentionally minimal.

apple container is more of a docker-style workflow, OCI images, registries, etc. shuru is just micro VMs with checkpointing, much simpler scope.

cool, would love to see it!

glad to hear it, that's exactly the thinking behind it. alpine is the only option right now yeah. what kind of dependencies are you running into issues with? would help me figure out what to prioritize next.

thanks! let me know how it goes

haven't thought about multi-agent communication yet. each sandbox is fully isolated which is the point. checkpoints help a bit here though, you can branch multiple agents from the same checkpoint so they all start from the same state.

OrbStack is great but it is solving a different problem. it's a full Docker Desktop replacement. shuru is just a thin layer over Virtualization.framework for spinning up throwaway sandboxes.

containers work fine for a lot of this. shuru is just what felt more natural to me. less config overhead and i wanted to learn by building it.

yeah, it just means everything runs on your machine. there are services like E2B, sprites.dev and others that give you sandboxes in the cloud. shuru runs VMs locally using Apple's Virtualization.framework, so nothing leaves your Mac.

Lima can do a lot of what shuru does if you set it up for it. the difference is mostly in defaults and how much you have to configure upfront. with shuru you get ephemeral VMs, no networking, and a clean rootfs on every run without touching a config file. shuru run and you're in. Checkpoints and branching are built into the CLI rather than being an experimental feature you have to figure out. Lima is a much bigger and more mature project though. Shuru is something I am building partly to learn and partly because I wanted something with saner defaults for this specific use case.

that's exactly the goal - aggregating context from slack, notion, and other services. but for now, it works a bit differently than just passing your repo context to cursor. right now, multiple team members can access the shared memory that your AI agents are generating. so when your colleague has a conversation with claude code about some architecture decision, you can pick up right where they left off when you switch to cursor or windsurf. the context persists across different AI tools through MCP servers, rather than everyone starting fresh each time. think of it as a shared brain for your team's AI development tools.