Understanding and Implementing Qwen3 From Scratch

From GPT-2 to gpt-oss: Analyzing the Architectural Advances

And How They Stack Up Against Qwen3

Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

Open-source LLMs as LangChain Agents

Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models

QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming

arXiv:2508. 20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration.

Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

Understanding and Coding the KV Cache in LLMs from Scratch

KV caches are one of the most critical techniques for efficient inference in LLMs in production.

Introducing Agents.js: Give tools to your LLMs using JavaScript

Mastering Long Contexts in LLMs with KVPress

Rethinking LLM Evaluation with 3C3H: AraGen Benchmark and Leaderboard

LLM Inference on Edge: A Fun and Easy Guide to run LLMs via React Native on your Phone!

How To Build Your Own LLM Runtime From Scratch

If you have ever wanted to actually build an LLM inference runtime yourself — pack your own weights, own every barrier, capture your own CUDA graphs — this is what that journey looks like on an H100. A step-by-step tour of a small runtime called annotated-llm-runtime, and the three bugs that produced most of the annotations.