When an LLM serves a 2,000-token response to a prompt of 10,000 tokens, it does something that looks absurd from
Continue readingPagedAttention and the KV Cache: How OS Paging Explains Modern LLM Serving
Every RAG system has two halves: a retriever that decides which text the model sees, and a generator that answers
Continue readingRAG Chunking Strategies in 2026: What the Benchmarks Actually Show
If you run a large language model behind a product, you have probably internalized an uncomfortable trade-off. The big frontier
Continue readingLLM Routing and Cascading: Send Every Query to the Cheapest Model That Can Handle It
Large language model inference has an awkward performance profile: the GPU does enormous math, then waits. Every token requires a
The default answer to “make the agent better” is a better model. But a growing pile of evidence says that
Reinforcement Learning with Verifiable Rewards (RLVR) has become the engine behind modern reasoning models. The recipe is straightforward: let a
AI agents have gotten remarkably good at reasoning, perceiving, and acting. But ask one to remember what you told it
Continue readingMetis: The First Memory Foundation Model That Learns to Remember
Robotics has a data problem. While language and vision models have ridden scaling laws to ever-higher capabilities, robot learning has
Continue readingXiaomi-Robotics-1: When Scaling Laws Finally Arrive in Robotics
The open-weights LLM landscape just gained a significant new entrant. Inkling, released on July 15 by Thinking Machines Lab, is
Continue readingInkling: Thinking Machines Lab’s 975B Open-Weights Multimodal Model
Reinforcement learning has become the defining ingredient of modern LLM post-training. GRPO, PPO, and their variants drive the reasoning capabilities