Read source code together, write articles together, and enjoy spicy hot pot together.
This guide explains how to install and use the OpenAI Codex plugin for Claude Code, covering setup commands, core review functions, optional review gates, and practical scenarios where the plugin provides a seamless AI‑powered second opinion within your coding workflow.
This article explains why the traditional 100vh unit fails on mobile browsers, introduces the three new CSS viewport unit families (large, small, and dynamic), and provides practical code examples and guidance for choosing the right unit in responsive web designs.
This article explains a structured workflow that uses git worktree to run multiple independent worktrees for parallel agent execution, detailing the three‑step process, bottlenecks, and a bash orchestration script for managing front‑end and API worktrees in a multi‑repo environment.
Harness Engineering is the emerging discipline that unifies Prompt Engineering, Context Engineering, and system-level controls to create robust, maintainable AI agent pipelines, illustrated with real-world performance gains, architectural patterns, and practical guidelines for building scalable AI‑driven workflows.
MiniMax M2.7 is a self‑evolving LLM that combines a persistent Agent Harness, multi‑level memory, and autonomous improvement cycles to reach SOTA benchmark scores, cost efficiency, and real‑world software‑engineering capabilities, illustrating the emerging skill‑economy of agent ecosystems.
This article evaluates Claude's ability to translate isolated Linux kernel modules from C to Rust, presenting a detailed analysis of translation granularity, token costs, experimental results on drivers, networking, and file‑system modules, and discussing the technical and economic challenges of a full kernel rewrite.
Harness Engineering is the emerging discipline that integrates Prompt Engineering, Context Engineering, and system-level controls to create reliable, maintainable AI‑generated code, and the article analyzes its origins, key components, real‑world performance data, and practical guidelines for building effective AI harnesses.
The article reveals that the perceived AI‑GPU shortage stems from misleading utilization metrics and wasted capacity, not actual supply constraints, and argues that better measurement and orchestration—not buying more hardware—will determine competitive advantage in the emerging AI infrastructure market.
The article walks through creating a compact 250‑line Rust gzip decompressor from scratch, explaining gzip’s header, DEFLATE block types, bit‑reading logic, Huffman coding, and LZ77 back‑references, and shows how to test it with real compressed data, highlighting key implementation challenges and trade‑offs.
This article compares Anthropic’s Claude Code skill and OpenAI’s Codex skill, detailing their design philosophies, structural methodologies, typography, motion strategies, content rules, anti‑patterns, and recommended use cases, and provides direct links to the open‑source repositories and blogs.
