507 related articles

Master four efficient Claude Code commands: Compact for context compression, WIT for local file import, precise targeting for fixes, and custom Commands to boost AI programming productivity.

5 advanced Claude Code Skill techniques — Prompt Optimizer, Deep Interview, Real Plan, Code Simplifier, and Skill Creator — to build reusable AI workflows that reduce rework and boost precision.

Deep dive into the Skill mechanism in AI coding: definition, purpose, and usage. Learn how Skills differ from Agent Constitutions and enable AI to follow professional workflows for tasks like debugging, code review, and requirements analysis.

Deep dive into OpenAI Codex Agent's core features, Skill ecosystem, context compression, and project-level Harness management tips from 660M tokens of real-world usage.

Anthropic reveals Claude is accelerating AI development, potentially enabling recursive self-improvement. A deep dive into its implications for safety, competition, and humanity's future.

Exploring the "Magic Fatigue" effect in AI products: why users feel AI is getting dumber, how to distinguish real degradation from rising expectations, and strategies for managing user expectations.

Anthropic releases Claude Opus 4.8 with three core upgrades: sharper judgment, more honest self-awareness, and longer independent work duration — all at the same price.
TutorialsA complete methodology for open-source project customization based on real-world experience, detailing the Cursor+Codex dual-IDE workflow, seven-stage process, MVP validation, and AI source code reading techniques.
TutorialsFirebase Functions now supports Dart, enabling shared code and business logic across frontend and backend. Learn about full-stack Dart, AOT compilation, deployment workflows, and current status.
TutorialsLearn LangChain FewShotPromptTemplate: core parameters, implementations for text completion and chat models, and practical use cases like batch file renaming to reduce LLM hallucinations.
Deep DivesDeep analysis of NousResearch's Hermes Agent Self Evolution project: GIPA genetic Pareto prompt evolution algorithm, six-step optimization loop, and five guardrail mechanisms for real-world Agent self-evolution.
Industry InsightsDeep dive into five core design patterns for long-running AI Agents from Google Cloud Next 26: checkpoint recovery, delegated approval, hierarchical memory, ambient processing, and cluster orchestration.
Product ReviewsDeep dive into JCode, an open-source Coding Agent Harness designed for multi-Agent collaboration. Features Agent Memory, Swarm collaboration, multi-Provider access, and self-evolution with just 14ms first-frame latency and 117MB for 10 sessions.
TutorialsA methodology for rhythm control in AI programming: use a five-step strategy (identify, plan, minimal change, test, risk assessment) to prevent AI from going off-track and causing rework.
TutorialsA detailed guide for Claude Code users to quickly get started with OpenAI Codex, covering desktop and CLI setup, pricing comparison, seamless project migration, plugin configuration, and context management differences.
TutorialsLearn how to integrate OpenAI Codex into your dev workflow alongside Claude Code. Covers pricing comparison, desktop setup, one-click migration, context management differences, and unique visualization features.
Industry InsightsWhy does Apple Intelligence keep getting delayed? From Siri's acquisition to AI team infighting, a deep dive into the organizational failures behind Apple's AI struggles.
TutorialsComplete guide to Hermes Agent's five core pillars: Memory, Skills, Soul, Crons & self-evolution. Covers VPS deployment, Telegram setup, security management & best practices for building an AI assistant that grows stronger over time.
TutorialsDeep dive into Harness Engineering: how engineers shift from coders to AI supervisors. Learn to solve agent drift, feedback optimization, and build future-proof engineering skills.
TutorialsConfused learning AI from scratch? This guide breaks down why fragmented learning fails and provides a complete path from Python to deep learning with practical tips.