Applied Compute
@appliedcompute • 4,544 subscribers
The Best AI is Built Not Bought
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Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by South Park Commons to share how companies are owning their intelligence with Applied Compute.
Applied Compute118,922 次观看 • 1 个月前
"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN
Applied Compute64,144 次观看 • 4 个月前
"Our approach is to build products and conduct research that are in service of accelerated AI deployments. Our platform team builds tools and context primitives that enable faster deployment. Our research team builds frontier systems, including a state-of-the-art RL stack. We then take that research and product and forward-deploy with our customers to help deliver real value." Thanks Founders You Should Know for having us. Open roles at:
Applied Compute45,197 次观看 • 3 个月前
"Every company is going to build their own frontier AI unique to their secret sauce. That's exactly what the top law firms do. We're starting to talk with a law firm and their client and ask: how could we build you a joint model?" Thanks Gabe Pereyra and Harvey for joining us at our Private Frontier all-hands to share how Harvey is defining the future of Specific Intelligence for legal agents.
Applied Compute19,710 次观看 • 1 个月前
Enterprise AI deployments today are frozen in time. Model capabilities stagnate in production. The problem compounds because companies aren’t static either. Every time your company improves, the model falls further behind. The bottleneck is continual learning. How does a model do something once and improve from feedback? The future of enterprise AI is Specific Intelligence: custom models teams own, trained on a company’s choices, interaction by interaction, using internal knowledge general models cannot access. Applied Compute helps companies train, serve, own, and improve custom models. Thanks Apoorv Agrawal for having Yash Patil at MS&E 435 to talk about the future of model training.
Applied Compute12,513 次观看 • 1 个月前
RL is a powerful mechanism for training company-specific models on their unique work and data. This is what we do at Applied Compute. A key challenge is how to make RL efficient, because we need runs to be fast (delivered in days), cheap (scalable unit economics), and predictable (not just fast, but reliably fast). Here are some takeaways: • Synchronous RL is wasteful with time and compute. • Asynchronous RL is more efficient but introduces staleness, which causes learning instabilities. • Modeling and simulations can help analytically solve for what configuration leads to optimal efficiency. This allows us to rapidly prototype training configurations, without burning expensive compute cycles on trial runs. Two of our co-founders, Rhythm Garg 🚂 and Linden Li, discussed some of this research at AI Engineer recently, with a focus on the following subproblem: what is the highest throughput way to do RL given a maximum staleness and compute budget?
Applied Compute45,480 次观看 • 7 个月前
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