Reading up on codex-cli

The CLI coding agent market in mid-2026 is crowded with 35+ tools, and this piece systematically compares them across model labs, platform CLIs, and open-source harnesses.

Hasp is a local secret broker that injects credentials into coding agent processes without the agent ever reading the plaintext value.

A developer describes how AI coding agents have transformed his workflow, enabling him to ship 3x more code at higher quality and shifting his bottleneck from writing code to exercising judgment.

Codex CLI v0.128.0 introduces /goal for persistent, auto-resuming AI sessions that survived a 6.75-hour run with 41 minutes compute.

Skills are loader specifications with three execution stages, not static prompts, and understanding this runtime architecture is the key to writing efficient, portable agent skills.

The post collects expert perspectives on AI-assisted development, emphasizing verification over generation, harness engineering, function structure, and the cultural divide between software thinking and human experience.

Claude-replay converts AI coding agent session transcripts into self-contained, interactive HTML replays for sharing and embedding.

OpenAI's Codex prompting guide details how to optimize the model's performance through autonomy, tool use, and compaction features.

System prompts shape coding agent behavior as much as the underlying model, with swaps between agents like Claude and Codex producing distinctly different workflows.

Aight is an iOS app that connects to self-hosted AI agents like OpenClaw, Claude Code, and Codex CLI, offering sessions, voice mode, group chats, and direct control without cloud middlemen.

Solo is a native terminal workspace that runs AI coding agents like Claude Code and Codex alongside a developer's full stack, providing unified visibility, auto-restart, and MCP-based agent coordination.

A single Codex CLI agent built a basic HTML+CSS browser in three days with 20,000 lines of Rust, challenging the need for large multi-agent projects.

OpenAI explains the architecture of Codex CLI's agent loop, detailing how it orchestrates model inference, tool calls, and context management using the Responses API.

OpenAI releases GPT-5.2-Codex, an agentic coding model with improved long-context understanding, Windows support, and cybersecurity capabilities, including discovery of a React vulnerability.

Software engineers must prove code works through manual and automated testing, shifting accountability away from AI agents to the human submitting the PR.

AI's primary value lies in consuming and synthesizing personal data rather than generating novel content.

OpenAI is adopting Anthropic's 'skills' approach into ChatGPT and Codex CLI, using markdown files for context management and subagent orchestration.

OpenAI has quietly adopted Anthropic's skills format, now available in ChatGPT's Code Interpreter and Codex CLI, using markdown-based skill folders for file operations and code generation.

OpenAI recommends migrating to the Codex-Max agentic model using the codex-cli prompt, compacted context, and specific tools for best performance on complex coding tasks.