102 related articles

A deep dive into Harness Engineering — the third phase of AI coding. Based on research across 2,853 GitHub repos, explore agents.md, Skills, MCP, and eight configuration mechanisms to control your AI coding assistant.
行业洞察SQLite adds AGENTS.md explicitly rejecting AI-generated code and opens a dedicated Bug forum to handle the flood of AI bug reports. A look at its AI governance strategy.

Bun runtime now generates a CLAUDE.md file by default with bun init, signaling AI assistants becoming first-class citizens in the development toolchain.

Complete guide to Pi coding agent: design philosophy, installation, shortcuts, session management, and 7-layer customization architecture. How this 45K-star minimalist terminal tool redefines AI coding workflows.

Deep dive into OpenAI GPT-5.6 Value Maxing strategies covering Sol/Terra/Luna model selection, KV cache optimization, Prompt compression, and programmatic tool calling to help developers achieve more output with fewer Tokens.

Deep dive into running OpenAI GPT-5.6 inside Claude Code: comparing Codex vs Claude Code on subagent orchestration, workflow design, and system prompt quality, revealing how harness engineering determines model output.

Claude Code creator Boris argues top engineers should embrace AI-era automation leverage. By encoding domain knowledge into infrastructure, preview environments, and lint rules, engineers multiply output—the core path to Staff Engineer.

Veteran AI practitioner Remy breaks down the leap from chat models to AI agents: how agents work, the three pillars of context, tools, and skills, MCP connections, and hands-on architecture to make you a 100x employee.

An open-source automated news briefing system. No coding needed—just let an AI read the project docs to complete the entire deployment. Six-stage pipeline, four-channel search covering 16+ platforms, smart classification and dedup, daily auto-push to Feishu, completely free.

An open-source newsletter auto-generation system requiring no coding—just have AI read the project docs to complete deployment. Six-stage pipeline, four-channel search covering 16+ platforms, smart classification & deduplication. Completely free.

Google's official hands-on: how to go from idea to production fast with AI Studio and build AI Agents using the now-GA Interactions API. The core idea—Agents are just combinations of files.

Official Google hands-on: go from idea to production fast with AI Studio, and build AI Agents with the now-GA Interactions API. The core idea: an Agent is just a composition of files—Markdown plus a few scripts, no complex Python loops needed.

Tech blogger Theo found GPT-5.6 runs better in Claude Code than in OpenAI's own Codex. A deep dive into their differences in system prompt quality and subagent orchestration.

Claude Code creator Boris and developer Theo reveal: in the AI Agent era, tinkering habits like automation, building small tools, and writing CLAUDE.md are becoming the core edge for reaching Staff engineer level.

OpenCode has become the world's most popular open-source coding agent—8M monthly active developers, 75+ model providers, and custom sub-agent routing. This deep dive covers its core features, config tips, and business model.

A data-deletion disaster reveals the biggest AI Agent risk: the problem isn't the model, it's Harness design. Learn context management, process standards, and permission isolation.

OpenAI Codex is more than a coding tool—it's an AI agent that writes code, makes slides, and operates browsers. This guide covers its four versions, permission modes, plugins, automation, and six best practices.

Model performance gaps are closing. Real competitive advantage lies in portable AI agent architecture. Learn how to build a workspace that works across Claude Code, Codex, and beyond — no vendor lock-in.

A deep dive into the 7 core components for building long-running AI Agents: Goal, Evaluator, Verifier, Loop, Orchestration, Observability, and Memory.

Chrome's Modern Web Guidance offers 100+ expert-reviewed guides to help AI Agents adopt modern web practices, fixing training data lag and legacy pattern recommendations with semantic search and Baseline compatibility checks.