Technical analysis, systems engineering perspectives, and research updates from our team.
An analysis of current scaling trends in large language models — where we see diminishing returns, and where unexplored approaches may unlock new capabilities.
How we architect model serving infrastructure to handle millions of requests with sub-50ms latency — lessons from production deployments.
Novel techniques for training large models on limited hardware. Our approach to memory optimization that reduces VRAM requirements by 40% without accuracy loss.
Most AI failures are not model failures — they are systems failures. An examination of why robust infrastructure is the critical differentiator in production AI.
Technical challenges and solutions for deploying capable AI models on resource-constrained edge devices — from model compression to hardware-software co-design.
What breaks in production AI systems and how to build pipelines that self-heal. A practical guide to observability, fault tolerance, and automated recovery.
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