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LLMs can learn to operate in low-, medium-, and high-effort reasoning modes that can be controlled.

Open-weight models can replace Claude Code and Codex subscriptions in local coding agent harnesses.

Curated list of notable LLM research papers published from January to May 2026.

Recent open-weight LLMs use KV sharing, mHC, and compressed attention to reduce long-context costs.

Sebastian Raschka describes a workflow for learning new open-weight model architectures.

Coding agents combine tools, memory, and repository context to improve LLM performance.

Guide covers attention variants in LLMs including MHA, GQA, MLA, and sparse attention.

Sebastian Raschka compares ten open-weight LLM architectures released in January-February 2026.

Overview of inference-time scaling techniques for improving large language model reasoning capabilities.

Sebastian Raschka reviewed 2025 LLM progress including DeepSeek R1, inference scaling, and 2026 predictions.

Sebastian Raschka compiled curated research paper lists for LLM work from July to December 2025.

DeepSeek evolved from V3 to V3.2 with architecture, sparse attention, and reinforcement learning updates.

Sebastian Raschka discusses linear attention hybrids, text diffusion, code world models, and small recursive transformers.

Sebastian Raschka explained four main approaches to LLM evaluation with code examples.

Sebastian Raschka published a detailed guide implementing Qwen3, a leading open-source LLM.

Analysis compares architectural advances from GPT-2 to gpt-oss against Qwen3.

Sebastian Raschka compares modern LLM architectures from DeepSeek-V3 to Kimi K2.

Sebastian Raschka compiled 200+ LLM research papers from January to June 2025.

Sebastian Raschka explains KV cache implementation for efficient LLM inference.

Sebastian Raschka offers a course on building LLMs from scratch to understand their mechanics.