# codex

OpenAI wedged a $100/month plan between its $20 Plus and $200 Pro tiers, promising higher Codex limits and explicitly targeting Anthropic’s Claude. But its honeymoon limits expire May 31, so the plan may feel less generous afterward.

Vaibhav Srivastav's Codex prompt is interesting because it describes an agent maintenance loop: look back at recent work, find repeated workflows, and package only high-confidence patterns into Skills, automations, or subagents. It is agent dreaming: turning busy work into capability.

OpenClaw and Hermes are both handing low-level coding-agent execution to Codex app server. This is not just a model switch. It is the agent product stack separating model, execution engine, and chat surface.

OpenAI quietly open-sourced Euphony — a browser-based viewer for Harmony chats and Codex session logs (Apache 2.0). Four telling details buried in the source: a 2-line AGENTS.md, gpt-tokenizer as a runtime dep, translation needing the user's own API key, and a self-written SSRF warning.

@daniel_mac8 shares an open-source Elixir implementation: create a Linear issue and move it to 'in progress,' and Symphony picks it up in a dedicated Codex workspace. Codex even writes status updates back. The author argues this is software development moving up an abstraction layer.

Simon Willison finally defines Agentic Engineering after 11 hands-on chapters: using coding agents to help build software. The interesting part is why he needed the patterns first before the simple definition felt earned.

Karpathy spent a weekend running 4 Claude + 4 Codex agents as an ML research team on GPUs. The result: agents are S-tier at implementation but F-tier at experiment design. His key insight — 'You are now programming an organization' — might define agentic engineering in 2026.

Peter Steinberger shares a high-scale PR triage workflow: run 50 Codex agents in parallel, emit structured JSON for every PR, then consolidate in one large-context session. Clean reports can beat premature vector database architecture.

Drexel/Missouri S&T analyzed 33,596 agent-authored GitHub PRs from 5 coding agents. Overall merge rate: 71%. Codex: 83%, Claude Code: 59%, Copilot: 43%. Rejection cause: no review. LeadDev warns PR flood is crushing monorepos/CI.

GitHub's Agent HQ now offers multi-agent support (Claude, Codex, Copilot) for Copilot Pro+ & Enterprise users. Run multiple AIs simultaneously in GitHub/VS Code to tackle problems from different angles. Outputs become Draft PRs. A paradigm shift for code review.

OpenAI released GPT-5.3-Codex-Spark, its first model on Cerebras chips. It's incredibly fast (>1000 tokens/sec, 80% lower latency), but smaller, no auto-tests, Pro-only. This marks OpenAI's first production deployment on non-Nvidia hardware, redrawing the AI compute landscape.