Arca Opens to Everyone, Paying People for the AI

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Arca Opens to Everyone, Paying People for the AI | Crypto News


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

 

HONG KONG, November 11, 2026: Arca, a new desktop app from the staff behind Brevis, opened to the public today. Arca pays people for the AI classes they’d in any other case throw away. It finds the session historical past their AI instruments already keep on their laptop, lets them select which classes to submit, strips delicate particulars on the gadget before something is uploaded, and pays for the classes that are authorised. Starting today, anybody can signal up with an electronic mail tackle, with no invite and no waitlist.

 

Much of today’s information work runs through AI instruments. People steer a model through a job, correcting it, throwing out what fails and holding what holds up. Every one of those classes leaves a detailed report of real work: the back-and-forth, the lifeless ends, the repair that finally labored. These data pile up by the 1000’s on people’s computer systems, and once the window closes, they’re value nothing to the particular person who did the work. Arca provides that work a worth.

How it really works

Arca works with the AI instruments people already use, including Claude Code, Codex, Cursor and Claude Desktop. After putting in the app, customers see the classes Arca has discovered on their machine, past ones included. They choose a session to add, and Arca anonymises it on their laptop before it leaves, then returns a quote vary. Users either affirm, which sends the session to review, or withdraw, in which case the add is deleted. Approved classes are paid weekly.

 

Arca only reads classes that are already on the person’s laptop, and it lists every thing it finds. Nothing uploads in the background, and nothing leaves the gadget until the person picks a session and confirms. Before a session is uploaded, Arca strips names, file paths, account particulars and keys, and additional anonymising runs on Arca’s facet.

 

“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 discipline

Arca is constructed for anybody 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 fashions in Codex, qualify today, with more to come. Very short classes don’t qualify, and every submission is reviewed before it pays. Users can join GitHub to strengthen their profile, which might raise the worth of their classes, but it isn’t required.

 

Arca carries the precept behind Brevis’s verifiable computing work into a new area: issues you may confirm are value more than issues you’ve gotten to trust.

Availability

Arca first opened on October 13 in invite-only early access, admitting customers in small batches. With today’s launch, the invite requirement is gone. Users signal up with an electronic mail tackle or a Google account and can start instantly. Arca is free to use and out there now for Mac and Windows at arca.market.

 

Through November 30, the Arca Launch Challenge provides $10,000 in prizes on top of common payouts for authorised classes.

About Arca

Arca pays people for the AI classes they’d in any other case throw away. The desktop app finds the classes people’s AI instruments already keep, lets them select what to share, anonymises classes on the gadget before add, and pays for authorised classes. Arca is constructed by the staff 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 functions show the outcomes of complicated computations. Learn more at brevis.community.

Media contact

Nicolas Tang

nicolas@brevis.community

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