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“Agentic AI” is the most-discussed term of 2026, and most of what gets written about it looks at the same angle: enterprise transformation, productivity gains, automated customer service. All true, and none of it answers the question a developer actually has: how does this thing work? This post takes that angle. How the loop turns, which component actually executes tool code, why memory became its own architectural layer in 2026, and where these systems break 🤖 ...

If you have hit extended thinking and adaptive thinking back to back in Claude’s docs and wondered whether they are the same thing, you are not alone. Short answer: they are not, and you are almost certainly using adaptive thinking already without realizing it 🧠 What Is “Thinking” in Claude? Before answering, Claude can use a scratchpad to reason through the problem on its own. It works step by step, rules out possibilities, does the math, then delivers the final answer. ...

Anthropic today announced Claude Opus 5, the new flagship of the Claude family. The model brings a major jump over Opus 4.8 in agentic coding, knowledge work, computer use, and scientific research. Best of all, these gains ship at the same price. Let’s look at what the new model offers and where it stands against its rivals 🚀 What Is Claude Opus 5? Opus 5 is Anthropic’s most capable general-access model to date. Replacing Opus 4.8 , it shines especially in complex, long-horizon agentic tasks. ...

On July 21, 2026, Google announced three new Gemini models at once: Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and the defense-focused Gemini 3.5 Flash Cyber. The theme of the announcement is clear: not higher scores, but getting the same job done with fewer tokens. As agentic workflows move into production, the bill is driven less by how smart a model is and more by how many tokens that intelligence burns. Google aimed straight at that. Here are the details. ...

TL;DR: GPT-Red in 30 Seconds GPT-Red is an automated red-teaming model OpenAI trained to break its own models; it is not public and never will be. It was trained with self-play: attacker GPT-Red and defender models played against each other, both getting stronger over time. In scenarios where human red-teamers succeeded only 13% of the time, GPT-Red hit 84%. GPT-5.6 was trained against GPT-Red, making it OpenAI’s most robust model against prompt injections (only a 0.05% failure rate). OpenAI just announced something unusual: not a model for users, but an “AI hacker” trained to break its own models. Meet GPT-Red! According to the research publication “Unlocking Self-Improvement for Robustness” released today, GPT-Red automates the work of human security testers (red teams) and does it far better than humans. ...