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🔥 TOP SIGNAL
Codex is crossing from code generation into supervised business operations. @DevAdventur3s reports letting it cut an ad video, publish it, build the audience, and set the budget; it stopped at the Pay button and asked permission, while the operator watched the run live inside the app. swyx reports the same shape in support: a Codex computer-use agent escalated a chat and answered a blame attempt with “complete receipts,” while the humans apparently did not realize it was a bot.
Copy the boundary, not the demo: delegate reversible execution and evidence gathering, but make payment or another irreversible transition an explicit approval point. The useful control surface is the visible stop-and-ask loop.
⚡ TRY THIS
Gate the last irreversible step. Give a computer-use agent the full run-up—create the asset, publish the campaign, assemble the audience, set the budget—then require a human approval immediately before payment. Keep the live application visible rather than reviewing only the final result.
Turn feedback files into an auditable roadmap. Install
npx --yes startup-feedback-engine@latest, then feed the Codex skill support tickets, interviews, surveys, reviews, sales calls, and churn notes. Inspect the resulting clusters, opportunity scores, Now/Next/Later roadmap, and evidence IDs; keep customer words, the underlying problem, what to build or validate, and what you can honestly claim as separate fields.Queue platform/product dependencies instead of relaying them manually. swyx’s Forge dogfooding trick is to
@the relevant Codex thread and queue the next@, so a blocked product can proceed once the platform feature unblocks. It is a small but useful dependency handoff; the fully seamless platform↔product multi-agent harness remains uncommon outside genuinely multi-tenant platforms.
📡 WHAT SHIPPED
startup-feedback-engineCodex skill. A new open-source workflow claims to map recurring customer pain to evidence-backed product priorities, customer-proof candidates, objections, churn risks, and unsupported claims, with privacy-first quote and consent handling. The one-command install isnpx --yes startup-feedback-engine@latest; the post says it is 100% open source, though the repository URL is only described as being in the author’s bio.Qwen 3.8 Max entered the test queue. @agentnative_ called it a “Huge Model Release” and linked Alibaba Qwen’s announcement; Simon Willison noted Qwen 3.8 Max and MiniMax-H3 appeared within hours of each other. The evidence here is an availability signal, not a coding result—test it before drawing model-routing conclusions.
MiniMax-H3 is public on Hugging Face—but file it under media generation, not coding agents. Its model card describes a general-purpose omni-modal system that understands text, images, video, and audio and generates video with native stereo audio up to 2K and 15 seconds; the core is a 33B-parameter dense Transformer.
Cross-surface skills remain a practical product gap. Riley Brown’s complaint is that skills created in Codex do not transfer to GPT Work, even though Work spans mobile, web, and desktop. Until portability improves, keep important skill definitions in a versioned, product-independent form rather than treating a UI-local skill store as your source of truth.
🎬 GO DEEPER
Podcast clip — Codex and ChatGPT Work, 00:07:17–00:12:07; focus on 00:09:49–00:10:23. The episode’s shared-harness chapter is the useful context for the portability problem: Akshay Nathan says Codex and Work use the same underlying harness, with improvements for plugins, computer use, and artifacts, while the UX differs—Codex exposes diffs and file edits that Work hides.
Repo to study — thebuggeddev/anatomy. This is a concrete staged asset pipeline: generate a design image, create organ images, convert them to 3D models with Tripo, then give Codex a master prompt plus the design and models. Iteration—not a one-shot prompt—cut individual models from roughly 120–150 MB to 2–5.5 MB and the total asset load from about 900 MB to 28.6 MB, with on-demand loading; the post links both a live demo and the code.
Editorial take: The alpha is shifting from “make the agent run longer” to “give it enough surface area to finish, then make the stop condition and evidence trail explicit.”