In just two weeks: some of the largest supply-chain attacks in history, a major commercial LLM sabotaging legitimate ML research requests, and a US export ban kneecapping the world's access to a major model release. More than ever, we need Open, Verifiable Inference.
Today we start shipping direct responses. A🧵:
4/ We already had Owncast integration, and now we've added PeerTube, with more integrations on the way.
Step by step, we're proving that Arc-Cashier can become the go-to solution for streaming payments across open-source platforms.
Repo: https://t.co/aUUcvZeUTL
1/ ArcOSS Update: After a lot of hard work, we've released the PeerTube connector for Arc-Cashier V1
This is our submission for the @thecanteenapp Call for Proposals, built as a reusable primitive that other developers can leverage and extend within the @arc ecosystem 🧵👇
4/ Pinpoint accuracy.
Pause the video? The charges stop instantly.
Close the tab? Any unused balance is automatically refunded directly to your wallet.
You only pay for the exact amount of content you consume, down to the second.
Building SpreadHunter for the Agora Agents Hackathon by @thecanteenapp × @arc
A fully autonomous agent that runs 100% in your browser. It monitors two DEXes on Arc testnet (@Xylonet_ ↔ @synthra_finance), detects price gaps between stablecoin pairs
Building SpreadHunter for the Agora Agents Hackathon by @thecanteenapp × @arc
A fully autonomous agent that runs 100% in your browser. It monitors two DEXes on Arc testnet (@Xylonet_ ↔ @synthra_finance), detects price gaps between stablecoin pairs
"Spread covers fees. Liquidity sufficient. Net profit: 0.12%. Executing."
Two transactions on Arc testnet. Sub-second finality. Done before a human finishes reading the first price.
Real example: the agent reads EURC/USDC on both DEXes.
Xylot: 0.9637. Synthra: 0.9606. Spread: 0.32%.
The agent simulates the full swap via QuoterV2, adds fees (0.3% + 0.3%), calculates real slippage from pool liquidity, and decides:
@lablabai@AIatAMD Powered by the AMD Instinct, MI300X and Qwen3-VL-32B-Instruct, the system uses a dual-agent architecture to not only detect manufacturing defects via natural language, but also autonomously diagnose the root cause of the failure.
When a human operator sees something wrong, they immediately think: what's missing? why did it fail? how do I fix it?
That's the exact reasoning process we are replicating in AsBuilt Lens with a dual agent architecture.
@AIatAMD@lablabai
When a human operator sees something wrong, they immediately think: what's missing? why did it fail? how do I fix it?
That's the exact reasoning process we are replicating in AsBuilt Lens with a dual agent architecture.
@AIatAMD@lablabai