Correction for Listing 6.5 in Build a Reasoning Model From Scratch
Short correction note for the random seed in Listing 6.5 on page 198 of Build a Reasoning Model From Scratch.
Sebastian Raschka1 min read
Sebastian Raschka
Stay updated with the latest developments in Artificial Intelligence
Controlling Reasoning Effort in LLMs
How LLMs Learn Low-, Medium-, and High-Effort Reasoning Modes
Sebastian Raschka25 min read
Inkling: A New Open-Weight 975B MoE with a Few Surprises
Architecture and benchmark notes on Thinking Machines Lab's 975B Inkling MoE, including short convolutions, relative-position bias, training, and effort control.
Sebastian Raschka4 min read
200,000 Subscribers
Short note celebrating Ahead of AI reaching 200,000 subscribers.
Sebastian Raschka1 min read
GPT 5.6 Has 72 Possible Configurations. What's A Good Default?
Short note on how GPT 5.6 model and effort choices map onto training-time and inference-time scaling, producing 72 configurations.
Sebastian Raschka1 min read
Build a Reasoning Model From Scratch Is Out
Short note announcing the release of Build a Reasoning Model From Scratch and linking the publisher and Amazon pages.
Sebastian Raschka2 min read
Using Local Coding Agents
Short note linking a new article on setting up local coding agents with open-weight models.
Sebastian Raschka2 min read
Using Local Coding Agents
Using Open-Weight Models in Local Coding Harnesses as an Alternative to Claude Code and Codex Subscriptions
Sebastian Raschka29 min read
Local Open-Weight LLMs in Coding Harnesses
Short note on trying local open-weight LLMs across Qwen-Code, Codex, and Claude Code harnesses.
Sebastian Raschka1 min read
GLM-5.2 and IndexShare for Long-Context Sparse Attention
How GLM-5.2 uses IndexShare to reuse sparse-attention token selections across layers and reduce long-context indexer computation.
Sebastian Raschka4 min read
VibeThinker-3B and the Strength of Post-Training
A closer look at VibeThinker-3B, a Qwen2.5-Coder-based model whose reported reasoning gains come from a detailed post-training pipeline.
Sebastian Raschka5 min read
North Mini Code and Agentic Coding Benchmarks
Architecture and benchmark notes for North Mini Code, Cohere's 30B-A3B MoE trained for repository, terminal, and code-generation tasks.
Sebastian Raschka6 min read
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