Merged timeline of 109 items — blog publish times and listing timestamps, cut at midnight . Page 3 of 3.
From basic prompts to advanced context engineering—learn the proven techniques, patterns, and strategies that make Claude produce exceptional results. Includes real examples, common mistakes, and expert tips for 2026.
In early 2026, Anthropic introduced a groundbreaking new feature in Claude.ai: the Effort parameter. This setting allows users to control how much reasoning Claude applies to each request, offering four levels (Low, Medium, High, Max) that trade off between response thoroughness, speed, and token consumption. Combined with Adaptive Thinking introduced in Claude Sonnet 4.6, the Effort parameter transforms Claude from a one-size-fits-all model into a flexible AI assistant that can be tuned for everything from quick fact lookups to deep analytical work.
The AI agent ecosystem is fragmenting. This guide evaluates the top 10 directories for agent skills, comparing catalog size, CLI support, trust signals, and protocol adherence to help you build faster and more safely.
Separating a viral screenshot from Anthropic’s published rules—conversation-ending for persistent abuse, account actions under the Usage Policy, and why “hurt the AI’s feelings” is the wrong mental model.
Terminal-Bench 2.0 has become the de facto standard for AI agent evaluation since May 2025—used by virtually every frontier lab. This deep dive covers the 89-task benchmark, its evolution from version 1.0, the Harbor framework powering it, and why frontier models still struggle below 65% accuracy on tasks humans complete routinely.
“Interpretability” ranges from feature visualization to high-level logging. For most shipping teams, the honest goal is not ‘open the black box’ but ‘know when it breaks, why it might have broken, and what to do next’—tied to tools, data retention, and governance.
“Aligned” is not a vibe from a good chat. It is a design problem: what we specify, what the system optimizes for, and what actually happens in the world can drift apart. Here is a complete map of that space for people shipping agents and tools.
The product story is 'precision and iteration'; the platform story is gpt-image-2 on the Image and Responses APIs with flexible sizes and quality tiers. Here is a concise map of what OpenAI published and where to read the current limits.
The Caveman skill compresses assistant surface prose (lite, full, ultra) while keeping code intact. Here is 2026 frontier pricing, output-vs-input math, and when brevity helps quality—not only cost.
