Vibe coding is a four-step loop: describe what you want, let the agent generate it, look at what it actually built, and react with feedback, repeated dozens of times a session. The setup that works isn't about a faster model. It's about closing the last two steps, look and react, which carry almost none of the tooling the first two enjoy.
Updated August 2026: that loop hasn't changed shape since vibe coding got its name, but the tooling gap inside it has become the whole story. Everyone building agents raced to make describe and generate better: bigger context windows, faster models, sharper planning. Almost nobody built for the moment you stare at a running app and try to explain, precisely, what's wrong with it.
What is vibe coding, really?
Vibe coding is building software by describing outcomes in plain language and letting an AI agent write the code, rather than typing every line yourself. The term describes a workflow, not a tool: you stay in charge of intent and judgment, the agent handles syntax and boilerplate, and the loop repeats until the result matches what you pictured.
In practice it means running Claude Code, Cursor, Codex or a similar coding agent as the primary way you write software, and treating your own typing as the exception rather than the rule. You describe a feature or a fix in a sentence or two. The agent reads your codebase, writes the change, and often runs it. You look at what came back, in a browser, a simulator, a terminal, and either accept it or tell the agent what's still wrong.
That last part is the whole trick, and it's easy to miss because the first half of the loop is so fast. Describing a feature takes seconds. Generation, for anything the agent has seen thousands of times before, can take less time than it takes to read the diff. The speed is real. What's underrated is that speed doesn't remove the two judgment-heavy steps. It just makes them happen more often, in the same session, back to back.
For a solo builder this matters more than it does on a team, because there's no second person to catch what you missed. On a team, a reviewer who didn't write the code tends to look harder at the result, precisely because they weren't in the room for the generation step. Working alone with an agent, you're both the person who asked for the change and the only one checking it landed, and it's easy to let the second role slide when the first one feels finished.
What does the loop look like?
The loop has four steps that repeat every few minutes during a vibe-coding session: describe what you want, generate the code, look at what actually happened, and react with the next instruction. Each pass is short, but a two-hour session runs the loop dozens of times, so small friction in any one step compounds fast.
Broken into steps:
- Describe: you write a sentence or two of intent, sometimes pointing at a file or two for context.
- Generate: the agent plans, edits files, runs commands, and sometimes tests its own work.
- Look: you check the actual result: a page in a browser, a screen in a simulator, output in a terminal.
- React: you tell the agent what's right, what's wrong, and what to do next.
Only the middle two steps happen without you. Describe and react are typed by a human. Generate is mostly the agent. Look is entirely you, every single pass, because nothing else in the loop has eyes.
Where does the time actually go?
Describe and generate are the tooled half of the loop: editors, agents and context management all target them. Look and react are not. There is no dominant tool for capturing what's on screen or turning a reaction into something an agent can act on, so builders do both by hand, every single pass, and it shows up as time.
Here's the asymmetry in numbers instead of feel. Describing a change costs whatever your sentence costs, typically a few dozen tokens. Generation's cost is real but tooled: Claude Code and its peers manage context, batch tool calls, and are the most actively optimized part of the whole pipeline. Look is the outlier. On Claude 4.7 and later, showing an agent a single 1080p screenshot costs 2,691 tokens by Anthropic's published patch formula, ceil(width / 28) × ceil(height / 28), before you've said a word about what's wrong with it (Source: Anthropic, 2026). Older, standard-tier models cap out lower and downscale the same screenshot to 1,560 tokens instead. Record the same 30 seconds and frame-dump it to your agent, one frame per second, and the high-res bill is 80,730 tokens: 30 frames at 2,691 tokens apiece, calculated the same way. A narrated walkthrough of the identical screen runs about 150 tokens typical, measured rather than calculated, because speech compresses to text far better than pixels do.
| Metric | Tokens |
|---|---|
| One 1080p screenshot on Claude 4.7 and later | 2,691 (exact) |
| 30 seconds frame-dumped at 1 fps | 80,730 (calculated) |
| A spoken walkthrough of the same 30 seconds | ~150 (measured, typical) |
None of that is really about tokens. Tokens are just the measurable proxy for the actual cost, which is attention. Somebody still has to look at the running app, notice what's wrong, and put it into words the agent can use. That's a human step no amount of context-window growth removes, and outside a handful of small, recent tools, it's still mostly bring-your-own-screenshots-and-adjectives.
It's worth being honest about why that gap exists rather than treating it as an oversight. Describe and generate are problems every agent vendor already had to solve to ship a product at all, so they got years of investment by default. Look and react were nobody's core problem until agentic coding got fast enough that they became the visible bottleneck, which is recent. The tooling is thin because the need is new, not because it's hard to build.
What tools does each stage need?
Describe and generate are served by the coding agent itself and by MCP servers that give it more to work with. Look needs a cheap way to capture and show the screen. React needs a structured way to turn what you saw into something the agent can act on, not a video link, not a paragraph of prose.
Describe and generate. This half runs on the agent itself: Claude Code, Cursor, Codex, Copilot, plus whatever it can reach. The reach matters. MCP is the open standard that lets an agent call out to other tools and data sources instead of guessing, and it's the plumbing under most of what makes 2026's agents more capable than 2024's. If your agent can only read code and never touch anything else, describe and generate are still where you'd start improving it.
Look. This is the stage without a default. Most people paste a screenshot, which costs 2,691 tokens on Claude 4.7 and later and still needs a caption to mean anything. A smaller group tries screen recording built specifically for an agent: raw video, frame extraction, or narrated bundles, each with a different cost and a different amount of meaning per token. Reaching for something like Loom is a reasonable instinct, since it already sits in most people's toolbars. But Loom's AI reads the transcript of what you said, not the pixels of what you showed it, so the visual half of the review never actually reaches the agent.
React. Once you've looked, you have to say something. A wall of prose works but costs tokens and attention on both sides. A growing number of tools compress a review into a fixed, agent-readable format, often a markdown file structured for an LLM to scan in one pass rather than a video it has to sit through. Worth knowing before you pick one: feedback tools for Claude Code, Cursor and Codex now range from free browser extensions to $39-and-done apps, and the right one depends on whether you're reviewing a browser tab, a native app, or a whole desktop.
| Loop stage | Default today | Typical cost | Dedicated tooling |
|---|---|---|---|
| Describe | Typed prompt | Tens of tokens | Mature, built into every agent's UI |
| Generate | Agent + MCP tools | Varies, tooled | Mature: Claude Code, Cursor, Codex |
| Look | Pasted screenshot | 2,691 tokens per image (Claude 4.7+) | Thin: a handful of 2026 entrants |
| React | Typed follow-up message | Tens to hundreds of tokens | Thin: structured formats are new |
Every tool named in the look and react rows is new and single-vendor; none has the years of adoption data Claude Code or Cursor already have. Judge each on what it costs and what it actually does, not on reputation, because there isn't one yet, Walkie included.
How do you keep quality up as speed rises?
Quality holds up when the react step gets more precise as the loop speeds up, not less. That means naming the exact location of a problem, saying what's wrong and what you expected instead of just what's wrong, and checking the fix in the same place you found the bug, not trusting the agent's own description of what it changed.
Speed is the whole appeal of vibe coding, and it's also the thing that erodes review discipline first. When a fix takes eight seconds instead of eight minutes, it's tempting to skim the result instead of actually looking at it, and to accept "done" at face value instead of checking. Neither habit costs you anything on the first ten passes. Both compound by pass fifty.
- Be precise in what you say back: location, expectation, and the gap between them, not a vague "this still looks off"
- Keep the look step honest even on the fifth pass this hour: actually render the page, actually run the flow, rather than reading the agent's summary of what it did
An agent's account of its own work is text about a claim it can't verify by looking. You're the only part of the loop that can.
What breaks at scale?
Two things break as a vibe-coded project grows: context, because each new file adds to what the agent re-reads every turn, and review bandwidth, because more surface area means more to look at per pass while your session time stays fixed. Both show up as the same symptom: slower turns that feel like the agent got worse.
On a small project, one review pass covers most of what changed. Past a few dozen files, it doesn't. A fix in one place can move something two screens away, and a single "looks good" from the agent covers less and less of what actually shipped. The context problem compounds it: agents don't remember previous turns for free, so as CLAUDE.md files, prior decisions and file counts grow, more of every turn's budget goes to re-reading state instead of doing new work. Image-heavy review makes that worse fastest, since a handful of screenshots per pass can outweigh the code itself in tokens.
None of this is a reason to slow down the generate step. It's a reason to make the look and react steps more efficient as the project grows, not less: narrower reviews, more specific feedback, and a habit of checking the parts most likely to have moved rather than everything at once.
The practical version of this is scoping each review pass to what actually changed instead of re-checking the whole app from the top every time. A layout tweak in a shared component might touch five pages; a fix to one form's validation almost never does. Knowing which kind of change you just asked for, and looking there first, is what keeps a review pass short even as the project it covers gets bigger.
What does a good setup look like end to end?
A working setup pairs a capable coding agent for describe and generate with a deliberate choice for look and react: a fast capture method, a compact format the agent can read cheaply, and feedback specific enough to act on the first try. No single tool covers all four steps yet; most builders assemble two or three.
Here's what that looks like assembled, stage by stage:
- Describe and generate: a coding agent you already trust, Claude Code, Cursor or Codex, with an MCP server or two connected for anything outside the codebase itself.
- Look: a capture method sized to the bug. A single screenshot for something contained to one screen. A short narrated recording when the problem is a sequence, a state change, or something easier to point at than to type.
- React: feedback in a format the agent can scan in one pass rather than a paragraph it has to parse: a location, what's wrong, what you expected.
- Verify: the same look step, run again, against the actual output, not the agent's account of what it changed.
None of these choices is fixed, and the honest state of the market in mid-2026 is that no single vendor has solved all four steps. For the fuller list of what's actually available at each stage, with real prices, see the current stack of vibe coding tools. Walkie is one entry in the look-and-react half, a screen-and-voice recorder that turns a review into a compact bundle a coding agent can read for around 3,500 tokens instead of a video it can't open. It's a recent, single-vendor answer to a two-year-old problem, not the only one, and it won't be the right fit for every stage or every project.
What to do next
Pick one loop pass from this week that took longer than it should have, and figure out which step it actually stalled on. If it stalled on describe or generate, that's a prompting or agent-configuration problem, and plenty is already written about it. If it stalled on look or react, rewriting the same bug report three times, or pasting five screenshots to cover one page, that's the half of the loop this article is about. Try writing a rough structured review by hand for a week (location, issue, expectation, one screenshot) before deciding whether a dedicated tool is worth paying for. The habit matters more than which tool you pick.