Every modern large language model you use is almost certainly a Mixture-of-Experts model. Mixtral, DeepSeek-V3, GPT-OSS, the latest Kimi and
Continue readingInside Mixture-of-Experts: Routing, Load Balancing, and Why 671B Parameters Cost 37B
Most teams treat “thinking mode” as a switch. Turn it on and the model reasons through the problem step by
Continue readingThinking Inertia: Why LLMs Don’t Stop Reasoning When You Tell Them To
Every RAG pipeline starts with the same piece of code: split documents into chunks, embed them, store them. It looks
Continue readingRAG Chunking Is Not Settled: What Token-Level Evaluations Actually Show
Every LLM application has two knobs that get blamed for everything: temperature and top-p. When outputs are boring, someone raises
Continue readingTemperature, Top-p, Top-k, and Min-p: How the LLM Sampler Actually Works
Ask someone to sketch a transformer on a whiteboard and they’ll draw the 2017 version: softmax attention over every pair
Continue readingMHA, MQA, GQA, MLA: The KV-Cache Trade-offs Behind Every Modern Transformer
Most teams that ship a retrieval-augmented generation feature never measure it. The demo looks good, a few hand-picked questions return
Continue readingStop Vibing Your RAG Pipeline: Recall@k, MRR, and NDCG in an Afternoon
Run two unrelated documents through a large language model at the same time, and you assume the model keeps them
Continue readingYour Transformer Can Hold Two Thoughts at Once: The New Linear Superposition Result
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