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
llm
280 stories
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 readUsing Local Coding Agents
Short note linking a new article on setting up local coding agents with open-weight models.
Sebastian Raschka2 min readLocal 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 readQuantization vs Distillation: How Big AI Models Get Small
Frontier AI models are almost too big to use — a 70B model needs ~140 GB of memory just to hold its weights. So how do these giants end up running on a single GPU, or even on a laptop?
ScrollypediaGLM-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 readVibeThinker-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 readNorth 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 readLLMs Count Digits, Not Store a Number Line
Constraint tests on how language models represent numerical magnitude.
oioi Blog5 min readAI Paper Review: Training Language Models to Follow Instructions with Human Feedback (InstructGPT)
GPT-3 was a major breakthrough in natural language processing. With 175 billion parameters, it demonstrated remarkable few-shot learning abilities and showed that scaling large language models could u
freeCodeCamp — ChatGPT38 min readAI Evaluation is Becoming an Exciting Standalone Discipline
Having worked on robustness problems during my PhD, I see many of the characteristics appearing in the evaluation of LLMs and AI systems. Adversarial attacks such as jailbreaks are becoming more relevant, edge cases finally become relevant, test sets and benchmarks are not static anymore but…
David Stutz - Paper Reviews7 min readBuild an LLM Pipeline with Structured JSON Output — No Python Needed
Learn how to build an LLM pipeline that returns structured JSON output using AI-Flow's GPT Structured Output node — no Python, no parsing code.
AI-Flow Blog5 min readn8n vs AI-Flow for AI Model Pipelines
A direct comparison of n8n and AI-Flow for building AI model pipelines — where each tool fits, and where it doesn't.
AI-Flow Blog5 min readSecret Messages for LLMs
Hidden context messages keep user-facing LLM chats clean while giving the model rich, per-message instructions—turning conversations into a predictable, tool‑driven workflow engine with a tiny amount of code.
Oats Blog4 min readTokenizers Are All We Need
A grounded walkthrough of tokenization and its impact on model behavior.
oioi Blog8 min readWhat is LLaMA Factory? LLM Fine-Tuning
A technical look at LLaMA Factory—a feature-rich toolkit for tuning large language models—and the latest updates through 2025.
VoltAgent Blog6 min readWhat is Vercel AI SDK?
A technical look at Vercel AI SDK 5, a toolkit for building AI-powered applications, and how it integrates with VoltAgent. Updated for AI SDK 5.0 (July 2025).
VoltAgent Blog5 min read
Showing 252 of 280 stories
Load more storiesAiByts Weekly Brief
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news
Join 15,000+ AI enthusiasts