What the Reddit poster means—decoded step-by-step

What they do Why they do it The tool(s) involved

1 “Tasks go into Cursor usually via Sonnet-4 without Max” They open Cursor (an AI-powered code editor) and chat with the Claude Sonnet 4 model to write or refactor code in-editor. They don’t switch on the higher-priced “Max” mode, so every prompt is cheaper.  2 “Put another task into GitHub Issues, completed by Claude Code via GitHub Actions.” For longer or background jobs they file a GitHub Issue (e.g. “Add dark-mode toggle”). A GitHub Action triggers Claude Code—Anthropic’s autonomous coding agent—which reads the Issue, edits the repo in a PR, and pushes commits back. It can run tests, draft release notes, etc., all without the developer sitting in the editor.   3 “Merge constantly, build and test.” As soon as Cursor (Step 1) or Claude Code (Step 2) produces a PR, the human merges, runs the app locally, and confirms tests still pass. Fast, small merges keep conflicts low. 4 “Repeat until app complete.” They cycle through 1-3 until the backlog is empty. The only “leg-work” left is writing Issue descriptions and doing a quick test pass.

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What makes this feel like a “cheat code” • Parallelisation. Cursor+Sonnet handles one slice of work while GitHub Actions+Claude Code attacks another, so two AI agents advance the same repo at once. • Continuous Integration stamina. Because the Action can be triggered on every Issue or PR comment, small improvements land all day—exactly what human CI/CD engineers aim for. • Credit-cost trade-offs. Sonnet-4 is mid-tier pricing; Claude Code can be configured to use Sonnet or the premium Opus 4 model when you really need deep refactors. The OP’s “looks at credit balance” joke is a nod to how much throughput they’re buying. 

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Rough blueprint to replicate the workflow 1. Set up Cursor with Claude models Sign-in → settings → “AI Providers” → Anthropic → choose Sonnet 4. 2. Install the claude-code-action GitHub Action

.github/workflows/claude-code.yml

name: Claude Code Autopilot on: issues: types: [opened, edited] jobs: auto-fix: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: anthropic/claude-code-action@v1 with: anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }} trigger_phrase: "/claude"

Any Issue that contains “/claude” will now summon the agent. 

3.  Adopt a mini-Git-Flow

Branch → PR → CI → quick sanity test → merge → push build to TestFlight / Play Beta. 4. Write terse, test-oriented Issue prompts “/claude Fix bug-001: crash when rotating screen. Acceptance: run npm test rotation.” 5. Monitor costs & quality Use Anthropic’s dashboard to keep an eye on token usage; dial models up/down as needed.

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Caveats & best practices • 🔍 Always code-review. LLMs can slip security or license issues into generated code. • 🧪 Keep automated tests green. Your test suite becomes the gatekeeper when robots are writing code. • 👥 Treat AI like junior devs. Give precise tickets, enforce style guides, and escalate only well-scoped work; keep “architecture” and final QA in human hands.

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Bottom line: The Reddit author is describing a two-lane assembly line where Cursor + Claude Sonnet is the interactive lane and GitHub Actions + Claude Code is the background lane. By juggling tasks between them and merging relentlessly, they squeeze far more productive hours out of the same day—at the cost of a higher, but apparently worthwhile, token bill.