Practical AI for solving real problems and creating value
Google's Gemini 3.1 Pro preview raises reasoning benchmark scores dramatically, offers new pricing tiers, and is already integrated into Gemini API, CLI, Vertex AI, and consumer apps, while community demos showcase SVG animation, real‑time dashboards, 3D simulations, and heat‑transfer analysis.
This article demystifies short‑term and long‑term memory in LLM‑driven autonomous agents, explaining their mechanisms, limitations, and practical implementations such as sliding windows, summarization, and vector‑based retrieval, while illustrating each concept with concrete Cherry Studio examples and relevant research references.
This article explains how to keep essential information from lengthy AI chat histories by using an intelligent summarization prompt, injecting the summary as a system message, and applying a sliding‑window strategy that retains the last three exchanges, thereby reducing token cost and preserving context continuity.
Addy Osmani’s analysis shows that while AI can generate roughly 70% of code—handling scaffolding and common patterns—the remaining 30% involving edge cases, security, and production integration remains as hard as before, and developers’ trust in AI‑generated code is rapidly declining.
This guide explains how to treat Claude Code as a long‑term partner by configuring lightweight fine‑tuning, organizing reusable skills and slash commands, leveraging event‑driven hooks, deploying focused subagents, managing Model Context Protocol (MCP) and plugins, and applying productivity tips to maximize AI‑assisted coding efficiency.
In the rapidly evolving AI landscape, the author argues that creativity, fast learning, task decomposition, clear communication, critical thinking, awareness of blurred job boundaries, tech‑business integration, and product thinking are the most important capabilities for professionals to stay competitive.
This guide walks through selecting the Gemini NanoBanana Pro model, designing detailed prompt templates, using a prompt‑optimizer skill, handling watermarks, and maintaining visual consistency to generate high‑quality, cartoon‑style images with AI.
The article analyzes Claude Skills' shortcomings—including limited platform support, lack of synchronization, sparse skill libraries, high installation barriers, dependence on precise descriptions, skill conflicts, model capability variance, context‑window consumption, nested reference limits, strict formatting rules, script robustness, unsuitability for simple tasks, and security risks—offering concrete examples and practical guidance.
This article walks through using the lightweight ZCode‑AI editor to run Claude Code for complex tasks such as subtitle correction and bulk file generation, detailing the UI workflow, skill integration, limitations, and future expectations for AI‑driven coding tools.
The article explains how Claude Code’s newly open‑sourced code‑simplifier agent works, detailing its system prompt, five core principles, step‑by‑step workflow, installation commands, language‑specific pitfalls, and a customized version for non‑JavaScript projects, enabling developers to automatically clean and maintain code without losing functionality.
