AI coding stack: Codex local, Cursor cloud

Most teams lose time because their AI coding stack trusts an agent's "all done" message. The fix in this workflow is reusable verification skills: written instructions that tell any future agent how to exercise a feature and prove it works before a pull request gets merged.

How this AI coding stack splits Codex and Cursor

OpenAI, the company behind ChatGPT and Codex, owns the local half of this AI coding stack, and Cursor, an AI-first code editor built on VS Code, owns the cloud half. Codex runs on the laptop for frontend and pixel-level work; Cursor cloud agents run in a hosted virtual machine for implementation and testing.

The reason for the split is desktop performance under parallel work. Running several local worktrees at once in Cursor slowed the editor noticeably, while the Codex desktop app stayed responsive with multiple worktrees open, according to the builder's first-hand account in the source video.

Frontend work went to Codex for a second reason: annotation and quick visual iteration felt faster there. Small conveniences such as a device toolbar that changes viewport resolution while the chat history stays open matter more over a long session than they look on paper.

Cursor's paid role is different. Its cloud agents get a full desktop virtual machine where the agent can OpenAI browser, exercise the running app, and capture screenshots of what it did.

What Pstack adds on top of Cursor cloud agents

Pstack is an engineering skill set for coding agents, created by an engineer on the team behind Grok, xAI's assistant, and designed to run inside Cursor cloud agents. It bundles reusable instructions that change how an agent plans, implements, and tests a feature.

The planning half is multi-model. Several models draft an implementation plan, then a judge and orchestrator model reviews those drafts and produces one merged plan. Cheaper models then write the code, since raw implementation does not require the most expensive model available.

The verification half matters more. Pstack includes skills for creating and maintaining verification routines, so when an agent finishes a feature it also records exactly how to test that feature next time. Cursor's cloud agents can execute those routines against the app.

Treat the multi-model claim as the builder's reasoning from published research directions rather than a measured result in the video. No benchmark comparing Pstack's judge-and-orchestrator pattern against a single frontier model appears in the source material.

Verification skills replace manual click-testing

Verification skills are written instructions that tell a coding agent how to exercise a specific feature and confirm it works before reporting success. Each skill is reusable, so an agent that has never touched a surface can still follow a tested procedure for logging in, exercising the flow, and checking the output.

The practical effect is that the human stops clicking through the app after every change. The agent runs the routine inside the cloud virtual machine and returns screenshots as evidence. The builder reviews code and architecture choices rather than hunting for broken buttons.

This is where the AI coding stack earns its cost. Before verification skills, an agent assumed its own code worked and the human confirmed it. After, the confirmation is a recorded artifact that the next agent can repeat.

A named example from the source video: verification skills named for Realtime Lambda, Resend, TikTok Connect, and the web app's core flows. Each one encodes how to reach that integration and what a passing result looks like.

Splitting agents like a small engineering team

Each major product surface gets its own agent with an owner role. The agents named in the video cover the UGC studio, account spy, analytics, content coach, script writer, and a content calendar still in progress. A given agent explored its feature, used the tool itself, and built a knowledge base about how that surface works.

Ownership creates a repeatable loop after every merge: the owning agent reviews the changes, updates its knowledge base, and extends or maintains the verification skills for that surface. Over time the agent's context about its own feature grows instead of resetting with each task.

The workflow starts in Grok, which the builder uses to invoke and coordinate Cursor cloud agents. A bug report written conversationally, with a request for evidence, becomes a cloud agent task that appears back inside Cursor.

Cross-surface work has a planned next step: a group chat where several owning agents discuss a shared feature and check each other's work. The builder says that has not been run yet, so treat it as an intention rather than a working practice.

The spec is where the engineering happens now

Writing the task takes 10 to 15 minutes, and that time is the engineering. The spec names what changes, which surfaces are touched, which verification scenarios must pass, and which behaviors must not regress, which forces design decisions before any code exists.

A complete spec lets a capable agent stack run with little intervention: implement, verify, review against standards, then return for approval. The human decision shrinks to accepting the change or asking for revisions.

The trade is explicit. Autonomy rises only as verification skills and code standards become rigid enough that an agent's "done" is trustworthy. Loosen those and the same workflow hands you unreviewed breakage instead.

The builder also states the honest limit: bugs will happen. Agent teams produce defects the same way human teams do, and the verification layer changes how quickly defects surface, not whether they exist.

Bug fixing and support agents beyond coding

An error-fixing agent runs continuously against errors captured by PostHog, the product analytics platform used here for error tracking. It triages each error to the surface owner, packages the context, and hands it over to the agent that owns that feature.

Unroutable errors land on the same agent as a general fixer. It does not merge by default. The prompt grants merge permission only for a widespread outage affecting most users, and the builder's stated bar for that is a severe incident, not a routine bug.

A support agent monitors a dedicated inbox after the team moved off Intercom. Known scenarios get answered automatically; unknown ones get escalated to the two founders, and the resolution is written back into the agent's instructions for next time.

The merge permission is the part worth copying carefully. It rests on verification skills, which means the trust boundary is the test coverage, not the model's confidence.

Cost, honest limits, and when to copy this stack

The stack costs $400 per month for the two top-tier plans, paid personally, with a third subscription under consideration. That figure is the builder's own arrangement in September 2026 and not a recommendation for everyone.

Copy the verification discipline before the tooling. Verification skills and written code standards are what allow an agent to merge a fix overnight; without them, cloud agents add review burden rather than removing it.

Expect the first weeks to be slow. Writing reusable skills and detailed specs costs time before it saves any, and the payoff depends on surfaces that get touched repeatedly.

Keep the model layer replaceable. Pstack and the verification skills are the durable assets here, and both survive a switch of editor or model provider.

FAQ

• What is an AI coding stack? An AI coding stack is the set of coding agents, editors, models, and verification routines a developer uses together. In this workflow it combines Codex for local frontend work, Cursor cloud agents for implementation, and Pstack skills for planning and testing.

• Why pay for Codex and Cursor at the same time? Each tool covers what the other does poorly. The builder reports that Codex handles multiple local worktrees without slowdown and that Cursor cloud agents provide a full desktop virtual machine for testing with recorded evidence.

• What are verification skills? Verification skills are written instructions that tell an agent how to exercise a feature and confirm it works, stored so future agents can reuse them. They convert manual click-testing into a routine the agent executes inside its own virtual machine.

• Can a coding agent merge code without human review? In this setup only one agent can, and only for widespread outages affecting most users. The permission rests on maintained verification skills, so the guardrail is test coverage rather than model confidence.

• Does Pstack only work with Cursor? The source video describes Pstack as optimized for Cursor cloud agents and used there. Skill files are usually portable instruction sets, but reuse in other tools is a claim to test on your own stack.

• How long does writing a feature spec take now? The builder reports 10 to 15 minutes per feature spec, spent before implementation begins, because the spec forces architectural and verification decisions that previously happened during coding.

• Is this stack suitable for regulated or sensitive environments? Local execution and allowlist filtering can support an air-gapped design, but they do not by themselves establish HIPAA, banking, or other compliance. Compliance depends on the application and the organization's controls.

• What should a small team copy first? Start with verification skills and written code standards for your most-touched surfaces. Tool choice matters less than having a routine that proves a change works before anyone trusts an agent's completion message.

• How current is this workflow? It reflects the builder's setup as of September 2026, when the source video was published. Agent tools change quickly, so re-check the vendor documentation for current plans, features, and pricing before adopting it.

Turn a recorded walkthrough into a written article

The most reusable part of this AI coding stack is not a model or a subscription; it is the writing. Specs and verification skills are documents that make agent work trustworthy, and most builders already have that kind of knowledge recorded somewhere, usually in a long video where they explain the setup while showing it.

If that sounds like your channel or your team's recorded walkthroughs, Skalablog turns the video into a draft article: paste a YouTube URL, transcribe it, and generate a structured write-up you can edit and publish.

That draft is useful for the reader who wants a verification checklist rather than a 20-minute video, and for you it means the explanation you already recorded does a second job.

Skala blog

Source video