Loops explained: Claude, GPT, Mira and what actually works

The big picture

This article's thesis is direct: the gap between mediocre and compounding AI results has nothing to do with which model you chose. It has everything to do with whether you built a loop. Anatoli Kopadze's X post (6 million views, per YouMind's tracking) maps the same iterative cycle across three tools at different complexity tiers, then gives builders a concrete test for when a loop is even worth the setup cost.

Why it matters

The default AI habit is still a vending machine: prompt, receive, close the tab, start from zero next time. That works for one-off tasks. It hits a ceiling fast for anything that repeats. The article cites Andrew Ng's finding (via the LinkedIn summary by Cyril Gupta) that wrapping an LLM in an iterative Plan-Act-Reflect loop can make a weaker model outperform a stronger one on a single shot. Andrej Karpathy's AutoResearch project is named as a frontier example: an autonomous agent edits code, runs GPU experiments, checks metrics, and commits or rolls back changes overnight without human intervention.

How it works

The canonical loop has five stages: DISCOVER (map what needs doing), PLAN (decide the approach), EXECUTE (do the work), VERIFY (check output against a hard condition), ITERATE (feed the result back in and repeat). The article identifies three components builders most commonly get wrong:

• Verify gate: a hard, measurable condition or scored rubric. Without one, the model grades its own homework generously and the loop is not a loop.
• State: a record of what was tried, what failed, and what is next. Without state, the same mistake repeats every session from zero.
• Stop condition: explicit success criteria plus a hard iteration or token cap. Without one, the article calls the result a "Ralph Wiggum loop" that runs until it breaks or drains your account.

Zoom in

The article maps three tools to low, mid, and high complexity tiers. Mira Telegram Skills are the entry point: minimal setup, no code required, good for recurring content or notification tasks. ChatGPT self-check prompts sit in the middle: you instruct the model to score its own output (for example, "Score this output 1-5 against the criterion: does it address the specific objection a skeptical CFO would raise?") and iterate until the score threshold is met. Claude Code /goal and /loop are the high-complexity tier: the model plans its own subtasks, executes them, and verifies against objective criteria with access to external tools and file state.

The four-box test the article prescribes is the sharpest diagnostic here. A loop is worth building only when all four conditions hold: the task repeats at least weekly (so setup cost pays back), something can be auto-verified (not just "does it feel right"), failure has a real cost (so iteration has stakes), and you have a state store to carry context across runs. In practice, the box most builders fail first is auto-verification: they can describe the goal but cannot write a measurable condition an agent can evaluate without a human in the loop.

Yes, but

The article is a synthesis of a viral X post, not a peer-reviewed study. The Andrew Ng loop-beats-stronger-model claim and the Karpathy AutoResearch example are cited by name but without links to primary sources, so treat them as pointers to verify independently. The tier map is also prescriptive: your actual starting point depends on your stack, not just your task complexity. If your task is one-off or its output cannot be auto-verified, skip loops entirely and write a better prompt instead.

The bottom line

Build loops when the task repeats, the output is auto-verifiable, failure has real stakes, and you have a state store. Start with Mira to validate the pattern cheaply, move to ChatGPT self-check scoring when you need a measurable rubric, and reach for Claude Code /goal when the task requires external tool use and multi-step planning. The verify gate is what separates a real loop from a model agreeing with itself in circles.