Ahead of AI Sebastian Raschka, PhD
The article explains how KV caches work in large language models and provides a from-scratch code implementation. A KV cache stores previously computed key and value vectors during text generation, eliminating redundant recomputation—for example, when generating
KV caches are one of the most critical techniques for efficient inference in LLMs in production.
Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention
Ahead of AI · 2 months ago
Coding LLMs from the Ground Up: A Complete Course
Ahead of AI · 1 year ago
Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
Ahead of AI · 9 months ago
How to evaluate and benchmark Large Language Models (LLMs)
Together AI · 8 months ago
The daily briefing
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TLDRocket
21 July 2026
The open-weight AI model market hit an inflection point today as Alibaba and Kimi released trillion-parameter competitors that challenge the closed-model orthodoxy—just as Washington moved to restrict them. Alibaba's Qwen 3.8 Max (2.4 trillion parameters) and Kimi K3 (2.8T) are going open-weight, with K3 ranking first on Frontend Web App Arena and fourth on agentic benchmarks, signaling that scale and transparency can coexist. The timing matters: while U.S. policymakers weigh layered restrictions on Chinese open models, technical experts are pushing back hard, arguing that closed gardens actually hurt security and competition more than they help. The real fragmentation risk isn't technical—it's regulatory.
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