Explains reasoning models vs LLMs, philosophizes about building from scratch, and shows Python/PyTorch setup with uv.
Explains common failures when switching to reasoning-tier models in Azure OpenAI and how to avoid them.
Analysis of a system where AI agent failures silently triggered template fallbacks, hiding three critical bugs that made the system appear functional while broken.
Explains how LLMs learn low, medium, and high-effort reasoning modes, covering training and inference techniques for controlling reasoning effort.
Explains how LLMs learn low, medium, and high-effort reasoning modes, covering training techniques and effort settings.
Analysis of GPT 5.6's 72 configurations, mapping model choices and reasoning effort to performance and cost.
Announcement of the release of 'Build a Reasoning Model (From Scratch)', a book on implementing modern AI reasoning techniques.
A curated list of LLM research papers from January to May 2026, covering architecture, reasoning, RL, agents, and more.
A curated list of LLM research papers from January to May 2026, covering architecture, reasoning, agents, and more.
Explains Recursive Language Models (RLMs), which are LLMs that call themselves to break complex tasks into structured, reusable steps.
A review of key trends and developments in Large Language Models (LLMs) throughout 2025, focusing on reasoning models, agents, and industry shifts.
A 2025 year-in-review analysis of large language models (LLMs), covering key developments in reasoning, architecture, costs, and predictions for 2026.
A 2025 year-in-review of Large Language Models, covering major developments in reasoning, architecture, costs, and predictions for 2026.
A month-by-month recap of 2025's AI landscape, focusing on reasoning models, agents, efficiency breakthroughs, and industry shifts.
Analysis of China's Kimi K2 Thinking AI model, a low-cost, open-weight model challenging US dominance in reasoning and agentic tasks.
A guide to the four main methods for evaluating Large Language Models, including code examples and practical implementation details.
Explores four main methods for evaluating Large Language Models (LLMs), including code examples for implementing each approach from scratch.
Explores the shift from RLHF to RLVR for training LLMs, focusing on using objective, verifiable rewards to improve reasoning and accuracy.
A curated list of key LLM research papers from Jan-June 2025, organized by topic including reasoning models, RL methods, and efficient training.
A curated list of key LLM research papers from the first half of 2025, organized by topic such as reasoning models and reinforcement learning.
