The single biggest programming need for AI is a universal, high-performance computation graph framework that eliminates framework fragmentation & manual optimization overhead.
AI development is stuck in the stone age of tooling – every framework (PyTorch, JAX, TensorFlow, TVM, Claude COde, etc.) forces devs to rewrite models, debug backends & hand-tune kernels even if they can auto-correct protocols.
This wastes 80% of engineering time on infrastructure instead of innovation.
Here’s a concrete programming
example of the gap:
# Today: 5+ frameworks.
manual optimization
import torch
x=torch.randn(1024, 1024, device=’cuda’)
for _ in range(1000):
x = x @ x + torch.sin(x) #
Seprate kernel launches!
#Tomorrow: ONE declarative graph
@compute_graph
def model(x):
return x @ x + sin(x)
result = model(x, backend=’auto’,
optimize=’max’) #Auto-fuses
to 1 kernel
We’ve been developing this universal framework to function much like a single, declarative graph
IR (like a supercharged MLIR) with:
+ Automatic fusion & kernel gen -> no manual Triton/CUDA
+ Universal backend dispatch -> GPU/TPU/NPU auto-optimized
+ Zero-copy Python binding -> write once, run everywhere
+ Built-in distributed -> native sharding + pipeline parallelism
+ Bebud/visualization -> live graph inspection at any granularity
A development guide with cybersystems frameworks that have been used successfully in many places around the globe.
