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Today, we're introducing AC2, the Applied Compute Agent Cloud, to enable every team to train, serve, and improve their own frontier models.

Applied Compute

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206,702 次观看 • 1 个月前 •via X (Twitter)

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33 条评论

Applied Compute 的头像

Applied Compute1 个月前

AC2 turns post-training into a repeatable, engineered process. Use your own harness to train up to multi-trillion-parameter models with all the latest research techniques.

Applied Compute 的头像

Applied Compute1 个月前

Ari, our research agent, monitors training, reads traces to find failure modes, analyzes results, and turns insights into better data, graders, and experiments.

Applied Compute 的头像

Applied Compute1 个月前

When you have the right checkpoint, deploy it to production with AC2 inference: dedicated, low latency, and 99.9% uptime all while preserving the configuration used during training.

Applied Compute 的头像

Applied Compute1 个月前

The most useful data comes from real user interactions, feedback, and traces. With self-distillation, your model can learn from production traces even when the original environment can’t be replayed.

Applied Compute 的头像

Applied Compute1 个月前

Over the last six months, AC2 has powered our Applied AI team’s work for Microsoft, NVIDIA, Cognition, Harvey, DoorDash, and others. Now, we’re excited to empower every AI team with their own model factory. AC2 is available in private beta. Reach out to learn more.

Ishan Mukherjee 的头像

Ishan Mukherjee1 个月前

Congrats @rhythmrg @ypatil125 @lindensli @michaelzchen5 and team! Looking forward to working as we post train our @rox_ai models.

John Schulman 的头像

John Schulman1 个月前

congrats!

vincent sunn chen 的头像

vincent sunn chen1 个月前

congrats @ypatil125 @lindensli @michaelzchen5 and team!!

Apoorv Agrawal 的头像

Apoorv Agrawal1 个月前

Let's go!! @ypatil125 @lindensli Agastya and team!

Dan Biderman 的头像

Dan Biderman1 个月前

congrats guys!

Jonathan Jacobi 的头像

Jonathan Jacobi1 个月前

👑

Francesca LaBianca 的头像

Francesca LaBianca1 个月前

Congrats team!

andrew 的头像

andrew1 个月前

congrats on the launch!

Jared Rosner 的头像

Jared Rosner1 个月前

🔥

kevin 的头像

kevin1 个月前

congrats on the launch AC team!

Maanav Khaitan 的头像

Maanav Khaitan1 个月前

congrats on the launch!

Lina T 的头像

Lina T1 个月前

the team is cooooking 🧑‍🍳

Zongheng Yang 的头像

Zongheng Yang1 个月前

Congrats @ypatil125 @lindensli and team, cooking 🔥

Ramin 的头像

Ramin1 个月前

great work! can I get access?

Neeraj Kumar 的头像

Neeraj Kumar1 个月前

Improvement loops need reversible deployment.

Shriram Sridharan 的头像

Shriram Sridharan1 个月前

Congrats on the launch !!

Praneet 的头像

Praneet1 个月前

Incredible launch, congrats @rhythmrg and team!

angela zhang 的头像

angela zhang1 个月前

So exciting!! Let’s go!!

Omeed Tavakoli 的头像

Omeed Tavakoli1 个月前

Wow

Ishan Gupta 的头像

Ishan Gupta1 个月前

Congrats on the launch!

Alex 的头像

Alex1 个月前

Awesome !! This gives companies more control over their models. Something awesome is happening ! We just need to combine it all...!!

angel hernandez 的头像

angel hernandez1 个月前

What happens when the us government regulates frontier models and we are only allowed to use claude

Sebastian Tan 的头像

Sebastian Tan1 个月前

insane

Sebastian Buzdugan 的头像

Sebastian Buzdugan1 个月前

most teams lack enough clean data for frontier training, how does ac2 prevent garbage feedback loops

jian 的头像

jian1 个月前

It’s like Tinker

Rompel 的头像

Rompel1 个月前

"Enable every team to train frontier models" is the pitch every MLOps startup makes right before the pricing page shows you still need a stack of H100s and an RL infra team to use it. What's actually managed here — the training loop, or just the serving layer around someone else's weights?

Robby Manihani 的头像

Robby Manihani1 个月前

congrats @ypatil125 and team!

刘朝 Zhao Liu 的头像

刘朝 Zhao Liu1 个月前

The trace-to-checkpoint loop is the real product: research agents find failure modes, graders turn them into evidence, and deployment preserves the tested configuration. Account and session state belong in that trace too:

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