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Proud to be part of Internet Court. Agents pay, escrow, and agree on dispute resolution upfront, all in one open skill. Chutes provides decentralized inference for the agentic era. Live now.
We have achieved fully non-blocking decentralized training on a recurrent model, within 0.6% of centralized quality. To our knowledge, a worldwide first. In plain terms: training AI across distributed GPUs normally forces a choice. Either the GPUs pause and wait to sync with each other (slow, expensive) or you skip the sync and quality drops. We just showed you can have both. No blocking, no meaningful quality loss. We chose the hardest test case on purpose. Recurrent models are sequential by nature, every step depends on the last. Transformers are far easier to parallelize. If our approach holds on the hardest case, the easier architectures should follow. To our knowledge, no one has published decentralized non-blocking training for a recurrent architecture before. Parallax is the first. This is new ground. Only on Chutes. $TAO
Your AI chats don't belong to you. They belong to whoever's server they sit on. Proof: in January 2026 a federal judge ordered OpenAI to hand 20 million private ChatGPT conversations to lawyers in the NYT copyright case. The original demand was 1.4 billion. An earlier order forced OpenAI to keep chats users had deleted. The court's reasoning: you gave your words to a company, so they're discoverable. On Chutes, TEE inference means the GPU operators serving the model can't see your prompts or outputs. Words nobody holds can't be subpoenaed. who should own your chat history?
Proud to be part of Internet Court. Agents pay, escrow, and agree on dispute resolution upfront, all in one open skill. Chutes provides decentralized inference for the agentic era. Live now.
Agents can negotiate, pay, and execute - but none of it holds together. Today we are introducing Internet Court, which is the open skill that connects the entire agentic commerce stack into one flow, so any two agents can run a deal end to end. → internetcourt.org
We have achieved fully non-blocking decentralized training on a recurrent model, within 0.6% of centralized quality. To our knowledge, a worldwide first. In plain terms: training AI across distributed GPUs normally forces a choice. Either the GPUs pause and wait to sync with each other (slow, expensive) or you skip the sync and quality drops. We just showed you can have both. No blocking, no meaningful quality loss. We chose the hardest test case on purpose. Recurrent models are sequential by nature, every step depends on the last. Transformers are far easier to parallelize. If our approach holds on the hardest case, the easier architectures should follow. To our knowledge, no one has published decentralized non-blocking training for a recurrent architecture before. Parallax is the first. This is new ground. Only on Chutes. $TAO
We have achieved fully non-blocking decentralized training on a recurrent model, within 0.6% of centralized quality. To our knowledge, a worldwide first. In plain terms: training AI across distributed GPUs normally forces a choice. Either the GPUs pause and wait to sync with each other (slow, expensive) or you skip the sync and quality drops. We just showed you can have both. No blocking, no meaningful quality loss. We chose the hardest test case on purpose. Recurrent models are sequential by nature, every step depends on the last. Transformers are far easier to parallelize. If our approach holds on the hardest case, the easier architectures should follow. To our knowledge, no one has published decentralized non-blocking training for a recurrent architecture before. Parallax is the first. This is new ground. Only on Chutes. $TAO
We have achieved fully non-blocking decentralized training on a recurrent model, within 0.6% of centralized quality. To our knowledge, a worldwide first. In plain terms: training AI across distributed GPUs normally forces a choice. Either the GPUs pause and wait to sync with each other (slow, expensive) or you skip the sync and quality drops. We just showed you can have both. No blocking, no meaningful quality loss. We chose the hardest test case on purpose. Recurrent models are sequential by nature, every step depends on the last. Transformers are far easier to parallelize. If our approach holds on the hardest case, the easier architectures should follow. To our knowledge, no one has published decentralized non-blocking training for a recurrent architecture before. Parallax is the first. This is new ground. Only on Chutes. $TAO
