Parallel computing

Oct 16, 2026

Decision models take off & Anthropic bans cruelty to Claude

The panel examines the rise of decision models, a family of LLM-based systems that return a single, scored classification in one forward pass, and asks whether they are genuinely new or a smarter use of existing architectures. It then weighs the cost and benefit of multi-agent teams, drawing on a study that found agent teams cost two to five times more without consistently better results, and closes with Anthropic's new policy against sustained cruelty toward its models and what it says about AI safety, ethics, and human behavior.

Sep 19, 2026

Weight Folding, CUDA Streams, and the Bug That Made My Model Speak Backwards — Filip Makraduli

This talk introduces FlashNorm, an optimization for transformer RMS norm layers. It details how algebraic tricks like weight folding, deferred division, and dropped pre-normalization can significantly speed up inference by mitigating GPU inefficiencies. The speaker also shares critical insights from implementing parallel CUDA streams, including debugging a subtle race condition caused by implicit stream joins, and advocates for open inference engines for deploying kernel-level research.

Sep 18, 2026

Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon

Stefano Ermon, co-founder and CEO of Inception, discusses his company's radical bet on diffusion models to overcome the hardware and latency bottlenecks of autoregressive LLMs. He explains how parallel token generation in diffusion offers superior inference scaling and GPU utilization, expanding beyond images to discrete text and code generation. Ermon shares insights into Inception’s Mercury models, real-world applications like voice agents, the custom software stack required, and why efficiency will define the next era of AI competition.