Oct 08, 2026

How Standard Kernel uses compound AI systems to optimize code and extract maximum GPU performance

Chris from Standard Kernel explains how the company uses two autonomous optimization systems, one for whole workloads and one for individual CUDA kernels, to speed up AI inference. He walks through a roughly 2x vLLM speedup on Qwen with a CoreWeave team, details the kernel generation search and the correctness checks needed for frontier-model-written kernels, and presents case studies including 11x faster world models, faster time-series and LLM serving, and customer-driven requirements.

Jul 26, 2026

The Messy Reality of Scale: Synthetic Data and Pre-Training — Marah Abdin & Robert McHardy, poolside

Poolside discusses their innovative approaches to synthetic data generation, pre-training validation, and distributed training challenges. They highlight how modular data pipelines, rigorous replica hash checks, and numerical stability fixes enabled them to scale their LLMs, culminating in the 118B parameter Laguna S model designed for agentic coding, which shows strong early results against leading open-weight models.