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Applied Compute
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Applied Compute
@appliedcompute
We build Specific Intelligence for enterprises.
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- 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
- Replying to @appliedcomputeWhy route at all? Because each model has its own strengths, and some models are overkill depending on the difficulty of the task. The best constant policy hits 0.834. The oracle router (i.e. picking the ground truth model per task) pushes the ceiling to 0.890 at roughly half the
- 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 at
00:00 - 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 model
00:00 - 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 @rhythmrg recently stopped by @southpkcommons to share how
00:00 - Applied Compute reposted
The Redpoint InfraRed 100 is now live. These are the companies building the infrastructure that powers everything happening in AI right now, from world models and agent runtimes to the sandboxes, databases, and security tools agents depend on. Congratulations to this year's
- Applied Compute has been named to @Redpoint’s 2026 InfraRed 100, recognizing the companies shaping the future of infrastructure and AI. Congratulations to all featured this year!
- Replying to @appliedcomputeFurthermore, we find that the token-level granularity in self-distillation is both a strength and a weakness – it's dense, but there is often a great deal of noise in the updates because the teacher and student may disagree on tokens for reasons unrelated to the desired behavior.
