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Applied Compute
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Applied Compute
@appliedcompute
The Best AI is Built Not Bought
- Applied Compute repostedModel strategy for @harvey: We are working on the first model in our legal foundation model series, inspired by @cursor_ai's Composer. Two goals: 1. Allow us to serve frontier intelligence across our product surface areas at an affordable price and a strong security posture.
- Preserving entropy is critical for continued training; in modern post-training recipes, entropy is often a fixed resource that gets exhausted over the course of a training run, making it difficult for the model to improve and learn on new tasks. Adaptive entropy control methodsReplying to @appliedcomputeThe collapse also shows up in the answers themselves. Under various metrics of intra-prompt diversity, a policy trained with GRPO leads to less diverse responses than a trained with adaptive entropy control. Moreover, we observe that entropy allows response diversity to be tuned,
- The workflows that make you different shouldn't run on the same general models everyone else rents. Our co-founder @rhythmrg on when to train your own.
- Applied Compute repostedWhen we started Applied Compute this was our thesis in a nutshell. "Companies need to turn their workflows, domain knowledge, and accumulated judgment into AI systems that improve with each use. Private evals should capture whether a model is actually improving against outcomes
- "A great eval needs to understand every correct answer, and every way one can go catastrophically wrong." @BrendanFoody from @mercor_ai shared with our CEO @ypatil125 how evals are deceptively the hardest part of post-training. Our team at Applied Compute solves this by00:00
- “RL is remarkably data efficient. You can specialize a model on exactly what your business needs, with surprisingly little data.” @BrendanFoody sat with our CEO @ypatil125 to discuss how RL flipped the equation from quantity to quality, so the proprietary data only you have can00:00
- After working with both frontier labs and enterprises across industries, @mercor_ai CEO @BrendanFoody joined our CEO @ypatil125 to discuss why proprietary data and custom models are what keep a company competitive at the frontier.00:00
- @nvidia’s Nemotron 3 Ultra handles software-engineering tasks at a fraction of the per-task cost of frontier models. So we trained a router to send each coding task to the cheapest model that can successfully solve it, cutting inference cost while holding frontier-level quality.Replying to @appliedcomputeThe models are complementary. The trained router sends 73% of tasks to @NVIDIAAI's efficient Nemotron 3 Ultra and routes the long tail to GPT 5.5 and Opus 4.7 on tasks where frontier performance at a premium is worth the tradeoff. Since the router is agentic, it can call tools
- No two companies are the same, even within the same vertical. That's why generic models fall short and Specific Intelligence wins: custom models you fully own, post-trained on your data, so they get very good at the exact task you need. Our co-founder @rhythmrg at00:00
- 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 model00:00
