Comparison : GPT-5-Codex V.S. Claude Code

1. Overview and Background GPT-5-Codex: Background, objectives, and architecture (as known) Release & positioning Architecture & training methodology (public hints and inferences) Training data scale / types (publicly known or inferred) In short, GPT-5-Codex is a specialized, engineering-focused spin of…

【HRM】How a Tiny Hierarchical Reasoning Model Outperformed GPT-Scale Systems: A Clear Explanation of the Hierarchical Reasoning Model

This article summarizes and expands on a recent presentation by Singular Radio that explained the Hierarchical Reasoning Model (HRM) — an architecture that recently attracted attention for solving benchmark reasoning tasks with an extremely small parameter count. Singular Radio’s hosts…

GPT‑5‑Codex: OpenAI’s Agentic Coding Model

Introduction OpenAI’s GPT‑5‑Codex is a domain‑specific variant of GPT‑5 designed to act as an autonomous software‑engineering assistant. OpenAI introduced the GPT‑5 family in August 2025 and described it as a unified system that routes requests among different model variants (the standard…

AI Adoption Slowdown: Data Analysis and Implications

Introduction The recent U.S. Census Bureau data revealed an unexpected dip in AI tool usage among large enterprises. Specifically, the biweekly Business Trends and Outlook Survey (BTOS) found that the share of companies with over 250 employees using AI dropped…

Grokking in Large Language Models: Concepts, Models, and Applications

Basic Concepts and Historical Background Definition of Grokking: Grokking refers to a surprising phenomenon of delayed generalization in neural network training. A model will perfectly fit the training data (near-100% training accuracy) yet remain at chance-level on the test set…

AI Development — August 2025

Executive summary (as of Sept 2025) 1) Major model releases & technical announcements 1.1 OpenAI GPT-5 (released Aug 7) What shippedOpenAI introduced GPT-5 as a unified system: a fast “smart” model for most queries, a deeper reasoning model for hard…

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