Arca Opens to Everyone, Paying People for the AI Sessions They’d Otherwise Throw Away

Source Newsbtc

After invite-only early access, the desktop app from the team behind Brevis is now open to anyone on Mac and Windows.

 

HONG KONG, November 11, 2026: Arca, a new desktop app from the team behind Brevis, opened to the public today. Arca pays people for the AI sessions they’d otherwise throw away. It finds the session history their AI tools already keep on their computer, lets them choose which sessions to submit, strips sensitive details on the device before anything is uploaded, and pays for the sessions that are approved. Starting today, anyone can sign up with an email address, with no invite and no waitlist.

 

Much of today’s knowledge work runs through AI tools. People steer a model through a task, correcting it, throwing out what fails and keeping what holds up. Every one of those sessions leaves a detailed record of real work: the back-and-forth, the dead ends, the fix that finally worked. These records pile up by the thousands on people’s computers, and once the window closes, they’re worth nothing to the person who did the work. Arca gives that work a value.

How it works

Arca works with the AI tools people already use, including Claude Code, Codex, Cursor and Claude Desktop. After installing the app, users see the sessions Arca has found on their machine, past ones included. They pick a session to upload, and Arca anonymises it on their computer before it leaves, then returns a quote range. Users either confirm, which sends the session to review, or withdraw, in which case the upload is deleted. Approved sessions are paid weekly.

 

Arca only reads sessions that are already on the user’s computer, and it lists everything it finds. Nothing uploads in the background, and nothing leaves the device until the user picks a session and confirms. Before a session is uploaded, Arca strips names, file paths, account details and keys, and further anonymising runs on Arca’s side.

 

“Every AI session is a record of real thinking and real work, and until now the person who did that work got nothing for it,” said Michael, co-founder and CEO of Brevis. “Arca lets people see what their sessions are worth and decide for themselves what to share. We opened it in small batches first to get the experience right. Today it’s open to everyone.”

Built for real work in any field

Arca is built for anyone who does real work with AI, from code to contract reviews, research and long working conversations. Sessions with Claude Opus and Fable, and with the latest GPT models in Codex, qualify today, with more to come. Very short sessions don’t qualify, and every submission is reviewed before it pays. Users can connect GitHub to strengthen their profile, which can raise the value of their sessions, but it isn’t required.

 

Arca carries the principle behind Brevis’s verifiable computing work into a new domain: things you can verify are worth more than things you have to trust.

Availability

Arca first opened on October 13 in invite-only early access, admitting users in small batches. With today’s launch, the invite requirement is gone. Users sign up with an email address or a Google account and can start right away. Arca is free to use and available now for Mac and Windows at arca.market.

 

Through November 30, the Arca Launch Challenge adds $10,000 in prizes on top of regular payouts for approved sessions.

About Arca

Arca pays people for the AI sessions they’d otherwise throw away. The desktop app finds the sessions people’s AI tools already keep, lets them choose what to share, anonymises sessions on the device before upload, and pays for approved sessions. Arca is built by the team behind Brevis. Learn more at arca.market.

About Brevis

Brevis builds verifiable computing infrastructure. Its zero-knowledge proving technology, including the open-source Pico zkVM, lets applications prove the results of complex computations. Learn more at brevis.network.

Media contact

Nicolas Tang

nicolas@brevis.network

Disclaimer: For information purposes only. Past performance is not indicative of future results.
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