Practical AI for solving real problems and creating value
The article forecasts eight pivotal AI trends for 2026—including the rise of Generative UI, edge‑deployed agents, smarter home assistants, complex agent harnesses, vibe‑coding, authentic social content, AI‑generated defaults, and biometric social logins—while explaining the reasoning and implications behind each prediction.
The article examines why Anthropic’s Claude Code runs as a terminal‑based CLI rather than a full IDE, outlining the strategic reasons, practical advantages such as parallel development, cross‑device collaboration, lightweight operation, native model integration, flexible vendor switching, and powerful built‑in features, all illustrated with concrete user experiences and tool examples.
This article surveys the most popular large‑language‑model leaderboards—including lmarena, Artificial Analysis, SuperCLUE, and llm‑stats—detailing their evaluation methods, coverage areas, URLs, and practical usage tips, while warning readers that rankings are only a reference and real‑world performance may vary.
Boris Cherny, founder of Claude Code, shares a detailed, step‑by‑step set of 13 practical experiences—from parallel multi‑session handling and model configuration to team collaboration, custom slash commands, sub‑agents, and long‑running task automation—demonstrating how to maximize productivity with Claude Code.
This guide walks you through a two‑step process—first using SlideDeckCleaner to strip watermarks from NotebookLM‑generated PDF PPTs, then employing an AI‑powered PPT conversion service to resolve Chinese garbled text and improve image clarity, with detailed screenshots and tips for handling stubborn elements.
The article shows how to create a simple AI agent that receives a screenshot of an arXiv paper, automatically extracts the paper’s URL and PDF link using a custom prompt, and then lets users view the abstract, download the PDF, or save it to a knowledge base.
This guide shows how to export a tangled Coze workflow, feed its YAML and related JSON files into the Qoder AI coding assistant, let the model analyze the mis‑aligned subtitle timestamps, receive concrete fixes, and even tweak background music through natural‑language prompts.
This guide walks through the entire process of designing, creating, and installing a Claude Skill that automatically optimizes prompts by selecting the best framework, confirming ambiguities with users, and leveraging progressive loading to handle dozens of prompt templates.
This article demystifies Claude's Agent Skills by defining their structure, comparing them with Rules and MCP, illustrating progressive disclosure with concrete examples, and providing step‑by‑step installation and usage instructions for both official and custom skills.
This guide walks through installing the OpenSkills CLI, syncing Claude Skills to AI coding agents such as Qoder, Cursor, Trae, and Windsurf, explains the underlying skill system, compares Claude Code and OpenSkills implementations, and details command‑line options and loading priorities for seamless integration.
