Sebastian Raschka's Blog

A learning-oriented workflow for understanding new open-weight model releases

How coding agents use tools, memory, and repo context to make LLMs work better in practice

From MHA and GQA to MLA, sparse attention, and hybrid architectures

I put together a new LLM Architecture Gallery that collects the architecture figures from my recent comparison articles in one place, together with compact fact sheets and links.

A Round Up And Comparison of 10 Open-Weight LLM Releases in Spring 2026

I recently sat down with Lex Fridman and Nathan Lambert for a comprehensive 4.5 h interview to discuss the current state of progress of AI, and what the...

Inference scaling has become one of the most effective ways to improve answer quality and accuracy in deployed LLMs. The idea is straightforward. If we are willing to spend a bit more compute, and more time at inference time (when we use the model to generate text), we can get the model to produce better answers.

A 2025 review of large language models, from DeepSeek R1 and RLVR to inference-time scaling, benchmarks, architectures, and predictions for 2026.

A curated list of LLM research papers from July���December 2025, organized by reasoning models, inference-time scaling, architectures, training efficiency, and diffusion.

Two years ago, I posted a list of Hello World examples for machine learning and AI on social. Here, the Hello World means beginner-friendly examples to showcase a method. I set a biennial calendar alert to revisit and append to it. I was thinking pretty hard about what a 2025 example could look like. So, here is a short post with the updated list and some explanations for more context.