A tool you stare at for ten hours a day deserves to look good. This guide covers two levels of Codex desktop customization: first, use the official Appearance settings to tune the theme, colors, and fonts; then use the open-source Codex Dream Skin to add a custom background. It explains both script and Skill installation, image specifications and composition, macOS and Windows differences, recovery options, security boundaries, and common pitfalls.
AI-generated code is unusually good at looking correct, which is precisely what makes it dangerous. This review checklist covers the places AI code fails most often, the right order for review, and how to use AI to review AI without turning approval into a rubber stamp.
Perfect LeetCode results and high benchmark scores do not prove that a model can improve your codebase. Learn the limits of coding-puzzle benchmarks, the dimensions that matter in real development, and a practical process for building an evaluation set from your own repository.
When AI breaks a project, it's not due to incompetence but rather its lack of global context, confident hallucinations, tendency toward over-correction, and human users' excessive trust. This article dissects the root causes behind these failures and offers a practical framework—from task decomposition to validation safeguards—to reduce risk.
When AI edits code in your main workspace, its changes become hard to separate from your own. Git Worktree gives each AI task an independent directory and branch in the same repository, producing clean diffs, easy disposal, and safe parallel work. This guide provides the complete workflow and scripts.
AI coding tools shine on toy projects but can stumble in a real codebase with tens of thousands of lines, established conventions, and years of technical debt. Learn how to provide project context, set permission boundaries, and choose the right integration approach for Claude Code, Cursor, and Codex.
From DeepSeek and Tongyi to vLLM and Ollama, nearly everyone offers an 'OpenAI-compatible' interface. This isn't an official standard from OpenAI but a de facto convention that has emerged across the industry. This article clarifies exactly what it supports, how seamlessly you can switch between providers, and where its limitations lie.
When a third team starts requesting its own OpenAI key, the company probably needs an AI gateway. This guide explains what an AI gateway adds beyond a traditional API gateway, the six practical problems it solves, and how to choose among open-source, managed, and custom options.
Without relying on off-the-shelf solutions, build your own AI gateway: how to normalize requests, consolidate key management, measure token usage, implement rate limiting and billing within streaming responses, and deploy model routing. This article thoroughly explains the core pipeline, offering actionable module breakdowns and data structures.
Repeatedly sending identical system prompts and knowledge base prefixes is a hidden driver of inflated AI bills. Prompt caching stores these segments; once cached, subsequent hits trigger significant price reductions. This article clarifies the differences between OpenAI's and Claude's caching mechanisms, quantifies potential savings, and explains how to structure prompts for higher cache hit rates.
The MCP Server ecosystem is exploding, but only a handful of categories are useful to developers every day. This guide explains 10 essential types—from files, GitHub, and databases to retrieval, browsers, and documents—and what to consider before using each one.
Build a file-search-capable MCP Server in roughly 100 lines of TypeScript, ready for immediate use in Claude Code and Cursor. This end-to-end guide covers tool definition, parameter validation, path security, integration configuration, and debugging techniques.
