Bleeding-Edge
AI R&D
First Principles
We operate at the intersection of theory and bare metal — where mathematical foundations meet instruction-level hardware control and production systems ship the same week.
Our work spans the full vertical: from hand-tuned GPU assembly and custom compute shaders to novel model architectures and large-scale distributed inference. We do not wait for frameworks to catch up. We write the primitives ourselves.
PTX • SASS • Metal • CUDA • Transformers • MoE • Diffusion
What We Do
We engineer at the lowest levels of the compute stack. While most teams treat GPUs as opaque accelerators, we write directly in PTX, SASS, and Metal shaders — hand-tuning instruction-level behavior to extract performance that higher-level abstractions leave on the table.
A modern flagship GPU is not just an accelerator. It is a massively parallelized supercomputer — tens of thousands of cores, terabytes of bandwidth, its own memory hierarchy and scheduling logic. We treat it as one. Our work pushes hardware into configurations and workloads that the rest of the industry considers impossible or impractical: unlocking compute paths that vendor toolchains never anticipated, bridging execution models across incompatible architectures, and making heterogeneous hardware behave as a single coherent system.
On top of this hardware foundation, we build orchestration infrastructure that runs hundreds of AI agents in parallel against verification-gated pipelines — searching solution spaces at densities that sequential approaches cannot touch.
Bare-Metal GPU Engineering
Hand-written PTX and SASS kernels, Metal compute shaders, and custom instruction scheduling that bypasses driver-level abstractions to hit theoretical hardware limits.
Impossible Hardware Configs
Cross-architecture compute bridging, heterogeneous execution across GPU families, and hardware configurations the vendor documentation says should not work — but do.
Architecture Research
Novel attention mechanisms, mixture-of-experts routing, sparse computation patterns, and hybrid architectures designed from first principles to exploit our low-level stack.
Systems at Scale
Massively parallel agent orchestration, verification-first synthesis pipelines, and infrastructure that treats modern GPUs as the supercomputers they actually are.
Operating Philosophy
We do not believe in credentials. We believe in artifacts. Every claim we make is backed by code you can read, kernels you can benchmark, and models you can run. If it cannot be verified, it does not ship.
The industry treats hardware limits as given. We treat them as suggestions. When the documentation says something cannot be done, that is where our work begins. The result is compute configurations, execution models, and engineering artifacts that redefine what is considered possible on commodity hardware.