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https://lnkd.in/p/eKvTFiwE.

Our latest milestone in exchanging capabilities between devices.

🔥 First We Borrowed a GPU. Today, We Borrowed the Internet.

We borrowed a GPU. Now we're experimenting with borrowing the Internet.

Kanverse started with a simple question:

What if computing resources didn't have to belong to the device using them?

We've already been experimenting with borrowing remote GPU capability.

Thank you, Product Hunt — this is only the beginning for Kanverse

Thank you to everyone who discovered, supported, upvoted and followed Kanverse GPU Borrow during our Product Hunt launch.

We now have our first 13 followers here. That may be a small beginning, but for an experimental technology project, it means a lot to us.

What is Kanverse?

Kanverse is an independent experimental technology lab where we build and test ideas around distributed computing, AI agents, device collaboration, security and new ways for computers and phones to share capabilities.

A small Kanverse experiment: connecting the real world to Roblox

I ve been experimenting with something new at Kanverse.

A real-world message can enter Roblox, reach an avatar, and actions inside the virtual world can be verified and reported back outside.

It s still an early prototype the avatar is player-controlled for now but the next step is autonomous interaction.

I posted a short video showing the experiment here:

Kanverse projects are starting to get picked up outside Product Hunt

Nice to see Kanverse projects starting to be discovered independently beyond Product Hunt.

A few external sites have now written about or indexed Kanverse GPU Borrow and Kanverse ProofStamp thanks to Product Hunt

The part of GPU Borrow that matters most to me is the control model:

Discover Ask Permission Borrow Execute Verify Release

Kanverse GPU Borrow - Borrow GPU compute from another device, with permission

Kanverse GPU Borrow demonstrates how a device can temporarily use GPU compute exposed by another machine through Blink Bridge. GPT-6 Astra acts as the orchestrator: it discovers the capability, inspects its state, requests explicit user authorization, invokes one bounded NVIDIA GPU workload, verifies the result, and confirms the GPU is released. The demo uses a real NVIDIA RTX 3050 Laptop GPU and a live cross-device connection—not a simulated GPU.