AI Engineering

Kernel Kombat: How Multi-Agent LLMs Squeeze 1.32× More From Your GPUs GPU bills have a charming way of turning “just one more model deployment” into a finance meeting. For companies running large language model serving stacks, the problem is rarely that nobody knows GPUs matter. Everyone knows. The harder problem is that performance bottlenecks often live inside kernels most executives will never see: attention merges, normalization fusions, activation multiplications, tiny pieces of code called millions or billions of times until “small inefficiency” becomes “why is the infrastructure budget wearing a crown?” ...

TL;DR for operators AgentScope 1.0 is best read as a production-shaping framework for agentic applications, not as a victory lap over rival agent frameworks. Alibaba’s paper describes a developer-centric stack that rebuilds agents around four core abstractions — message, model, memory, and tool — then places a ReAct-style reasoning-and-action loop on top of them.1 ...

TL;DR for operators A paper on a “real-time stock analyst” sounds, at first blush, like another attempt to place a crystal ball inside a chatbot and call it alpha. Fortunately, this one is more useful than that. Taniv Ashraf’s paper, A Serverless Architecture for Real-Time Stock Analysis using Large Language Models, is best read as a build-and-debug case study, not as evidence that Gemini can reliably predict stock prices.1 ...