Apple's Illusion of Thinking Paper Explores Limits of Large Reasoning Models
Agentic LLM systems have moved from linear prompt chains to graph-based workflows and, more recently, code-executing harnesses. Chains are simple but fragile, graphs add branching, retries, and validation, and code harnesses verify outputs through execution. The article advises validating model outputs and choosing the least complex design that meets reliability, speed, and cost needs. Khawaja Shams shares a distributed systems perspective on scaling LLM inference stacks efficiently. He discusses practical strategies for optimizing GPU utilization, managing KV cache memory cliffs, leveraging tiered storage, reducing tail latency, and implementing disaggregated prefill and decode architectures to improve overall fleet performance, cost economics, and availability. Nathen Harvey shares insights from DORA research on AI adoption in software delivery. He explains why AI acts as an…