If you are learning how to vibe code in 2026, the short version is this: start with something small enough to understand, decide what the first version is supposed to do, give the coding agent enough context to work intelligently, keep the temporary material around the task somewhere you can actually find it, and test what comes back before asking for the next change.
The code is not usually the hard part anymore. Keeping hold of everything around the code is.
That sounds straightforward until you start building.
Vibe coding moved the bottleneck somewhere I did not expect
I have been building much more heavily with AI lately, and one thing surprised me: as the coding agents got better, I spent less time worrying about the code itself and more time trying to keep track of everything around it.
A normal session might leave me with a screenshot showing a UI bug, another screenshot showing what I actually wanted, a useful answer buried halfway up a Claude conversation, a color from a reference site, a documentation link, a generated image in Downloads, and a bit of copy that I wanted to preserve before asking the agent to change anything else.
None of those things felt important enough to organize properly, because most of them would be irrelevant twenty minutes later. But they were important enough that losing one meant interrupting the flow of the work and going hunting for it again.
At first I assumed this was just me having a messy desktop.
Eventually I realized it was a consequence of how my role in the development loop had changed.
The first ten minutes of vibe coding are not the problem
The beginner version of vibe coding is almost magical: describe a small application in ordinary language, watch an interface appear, ask for a different color or another feature, and keep talking until the thing starts behaving vaguely like the product you imagined.
Google Cloud describes the basic shift as moving from writing software line by line toward guiding an AI assistant as it generates, refines and debugs the application, which is why the practice has opened software creation to people who would never have started by learning a programming language from scratch. (Google Cloud)
The tools are getting better at this quickly. Zapier tested more than 50 vibe-coding platforms for its July 2026 roundup and found a category that now stretches from beginner-friendly builders such as Lovable to more flexible or production-oriented environments, while also making the sensible point that none of them is a magic bullet and that testing, technical understanding and judgment still matter once the prototype becomes something you intend other people to use. (Zapier)
A Reddit post that keeps resurfacing in searches for beginner vibe coding advice captures the real problem unusually well: the author wanted to build something real, had no technical background, and ended the question with a wonderfully precise requirement, “I want to vibe, not get lost.” The replies quickly moved from “just ask an AI” to more practical advice: make the project tiny, think through the product requirements first, learn enough fundamentals to understand errors, and expect the difficult part to begin after the first impressive preview appears. (Reddit)
That last part is the bit I think matters most.
Getting something onto the screen has become astonishingly easy. Keeping the project in your head while it changes is another matter.
So the problem is no longer simply, “How do I make software?” Increasingly, it is, “How do I stay oriented while the software is being made this quickly?”
That is a different problem, and I think we have spent less time talking about it because it is not particularly glamorous. There is no benchmark for remembering which screenshot showed the correct spacing. Nobody announces a new frontier model for finding the error message you copied six minutes ago.
Yet this is the layer where a surprising amount of vibe coding actually happens.
The agent has context, but so do I
A lot of the discussion around AI coding is understandably about giving the agent better context: repository structure, specs, project rules, architecture, documentation, previous decisions, and everything else it needs to make sensible changes.
Cursor explicitly warns that irrelevant material can confuse an agent, while OpenAI found that giant instruction files became counterproductive because they crowded out the task and made it harder for the agent to distinguish what actually mattered. (Cursor, OpenAI)
But there seems to be another layer of context that belongs to the human steering the agent.
If I am fixing a UI problem, I might have six pieces of information in front of me, but perhaps only two actually belong in the next prompt. One might eventually become permanent project documentation, another is useful only so I can compare the next build myself, and three can disappear as soon as the bug is fixed.
I do not necessarily want the agent to remember all of that.
I need to remember it long enough to decide what the agent should see.
Call it working context.
Working context is the screenshot showing the bug, the reference image showing how it should look, the hex color you just copied, the error from Terminal, the URL to the documentation, the PDF with the requirement, the previous prompt that produced the better version, the generated image you are comparing against another generated image, and the file sitting in Downloads that you are about to feed back into the next step.
None of those things necessarily belongs permanently in your repository. Most of them probably should not be stuffed into the agent’s context either.
They belong to you, because they are the temporary evidence from which you decide what the agent needs next.
My first solution was to leave everything open
For a while, my system was basically browser tabs, Finder windows, and optimism.
The reference stayed open because I might need it again. The AI conversation stayed open because there was a good answer somewhere above. Screenshots collected on the desktop, useful commands occasionally went into Notes, and Downloads became a temporary database whose indexing system consisted mainly of remembering what time I generated something.
It worked well enough that I never thought seriously about it, but there was a constant tax in the form of tiny retrieval problems: which screenshot was the correct one, where did that error come from, which generated file was the good version, what was the color I copied before copying the URL, and what had I told the agent two iterations ago that produced the better result?
AI reduced the amount of code I had to carry in my head while increasing the amount of working material I was carrying everywhere else.
At no point was the code itself the thing I was struggling to locate. The code was sitting quite happily in the repository.
The evidence around the code was everywhere.
The AI does more of the execution, which leaves you more of the steering
Anthropic published one of the clearest pictures of this shift in June 2026 after analyzing roughly 400,000 Claude Code sessions. In the typical session they studied, the human made about 70% of the planning decisions, while Claude made about 80% of the execution decisions, meaning the person was still largely deciding what should happen, what mattered and what counted as correct even as the agent handled much more of the actual implementation. (Anthropic)
OpenAI describes a similar division from its own agent-first engineering work: humans steer while agents execute, with the engineering role moving toward specifying intent, designing the environment and creating feedback loops that let the agent work reliably. In OpenAI’s experiment, the bottleneck eventually became human attention and quality assurance rather than the model’s ability to generate code. (OpenAI)
Cursor’s advice lands in almost exactly the same place. Its 2026 guide says that once you become comfortable letting agents write code, your job increasingly becomes giving the agent the context it needs, and it recommends planning before implementation, keeping conversations scoped, starting fresh when a thread becomes noisy, and avoiding irrelevant context that can distract the model. (Cursor)
Put those ideas together and vibe coding starts to look less like dictating code and more like directing a very fast collaborator.
Which means you need to know what you are directing.
Context engineering has a human side
There is a lot of good advice now about writing specs, keeping agent conversations focused, maintaining project rules, and not throwing irrelevant information into a model’s context window. But before I can give an agent clean context, I still have to keep hold of the messy material from which I decide what “clean context” actually is.
That material tends to be temporary by nature. Screenshots, copied errors, prompts, URLs, reference images, files, and little fragments of code might matter intensely for the next fifteen minutes and then have almost no value once the task is finished.
This is why I think context engineering is a useful idea but an incomplete description of the human experience of vibe coding. The work had become fluid while the desktop was still pretending every object belonged somewhere permanent. (More on what building this taught me)
This is where Tansei fits
Tansei is a shelf at the edge of your Mac for the things that are still active in the work.
Copy something with ⌘C and it can land on the Shelf automatically; drag in a file or image and it can live beside the copied material; text, links, images, files, colors and code can sit together even though they originated in different applications, and the Shelf stays docked while you move between an editor, browser and chat. (Tansei)
That makes it useful for vibe coding in a way that has almost nothing to do with writing code.
The screenshot can stay there. The error can stay there. The reference URL can stay there. The color can stay there. The generated file can stay there. The line of copy you do not want the agent “helpfully” improving can stay there.
Then, when the next prompt needs three of those six things, you choose the three that matter and give them to the agent.
Tansei does not solve the coding agent’s context window. It gives the human steering the coding agent somewhere to keep their working context.
Those are different jobs. The distinction is small enough to sound pedantic and large enough to change how I work.
Do not give the AI everything
There is a tempting response to this problem, which is simply to throw every screenshot, every reference, every conversation and every requirement into the AI and let the context window sort it out.
The people building these systems increasingly recommend the opposite.
Cursor advises giving agents relevant context rather than manually tagging everything, and notes that long conversations accumulate noise and can eventually make an agent less effective. OpenAI reached a similar conclusion while building with Codex, where its team abandoned the idea of one enormous instruction manual in favor of a small map that pointed the agent toward deeper sources of truth when they were actually needed. (Cursor, OpenAI)
That gives the human a useful responsibility: curation.
Your permanent project context should become structured and durable. If a decision is going to matter next month, put it somewhere the agent can reliably discover next month.
Your temporary context does not need that ceremony. A screenshot can remain a screenshot until the problem disappears. A copied error can vanish once the fix works. A reference image might matter for one feature and never again.
A shelf is useful precisely because it does not pretend every temporary object deserves promotion into institutional knowledge.
The vibe-coding workflow I would use in 2026
If I were starting from zero today, I would keep the process deliberately boring:
- Start painfully small. Decide on one useful thing the first version should do, rather than prompting an entire startup into existence before lunch. That aligns with the most repeated beginner advice in the Reddit thread, where experienced users consistently pushed newcomers toward tiny first projects and clear requirements. (Reddit)
- Write the idea down before asking for code. Describe who it is for, what the first version does, what it deliberately does not do, and what would make you consider it successful; planning before generation is also one of Cursor’s strongest recommendations for agentic coding. (Cursor)
- Pick one coding environment and learn its rhythm. The major tools now solve different versions of the problem, from highly accessible end-to-end builders to IDEs and coding agents that expose much more control, so the right choice depends on how technical you want the workflow to become rather than which logo currently has the most enthusiastic Reddit thread. (Zapier)
- Keep your working context somewhere outside the agent. On my Mac, this is where Tansei sits: screenshots, copied errors, URLs, colors, code fragments, files and references stay visible until I know whether they belong in the next prompt, the permanent project, or nowhere at all. (Tansei)
- Test the result as a user rather than admiring it as a creator. The first version working once is not the same as the product working reliably, and even the current generation of vibe-coding platforms still requires testing and judgment before something serious should be shipped. (Zapier)
- Promote decisions, not debris. If something becomes a lasting requirement, architecture decision or project rule, move it into the durable context your coding agent can discover later; if it was merely useful while fixing today’s problem, let it disappear when the problem does. OpenAI’s agent-first engineering work makes the same distinction by treating versioned repository knowledge as the system of record rather than relying on scattered external information. (OpenAI)
That is a considerably less exciting workflow than “build your SaaS in one prompt.”
It is also much closer to what building something real actually feels like.
Tansei is deliberately not another AI coding tool
There is already an abundance of software willing to write code for you, and the current market has become crowded enough that one major roundup can test more than 50 different vibe-coding platforms in a single update. (Zapier)
I do not think the answer to that abundance is another chat box.
Tansei does not generate code, choose your architecture, debug your repository or remember a technical decision that should have been written into the project documentation.
It does something smaller. It keeps the pieces of active work close while you move between the tools that do those things.
That is why the Shelf can hold a screenshot beside a color, a copied command, a link and a file; it is organized around what you are using now, rather than where each object came from. Tansei also provides search, groups, tags and pins when the Shelf grows, while active material is stored locally on the Mac and remains available offline without requiring an account.
I increasingly think this kind of software becomes more useful as AI becomes more capable rather than less useful, because the faster the implementation moves, the easier it becomes for the human steering it to become the slowest part of the loop. (The output is easier, the carrying is harder)
The best vibe coders are not the people with the cleverest prompts
Anthropic’s data suggests that people with stronger domain knowledge tend to get better results from coding agents, even when traditional coding expertise is not the deciding factor; what matters is understanding the problem well enough to frame it precisely, notice when the result is wrong and steer the system back toward the outcome you actually wanted. (Anthropic)
That rings true to me.
The skill is increasingly not remembering syntax. It is remembering what you meant.
What was the requirement? Which version looked right? What did the user actually complain about? Which screenshot showed the bug? What did we already try? What belongs in the next prompt? What should become permanent project knowledge, and what can disappear once this particular problem has been solved?
AI can take an astonishing amount of execution off your hands. It cannot decide which details in your day deserve your attention unless you make that judgment first.
The faster coding agents become, the more I suspect the human bottleneck shifts from producing the implementation toward maintaining orientation while the implementation changes underneath you.
That is why my answer to “how do I vibe code in 2026 without getting lost?” is not another model recommendation.
Let the coding agent carry more of the implementation. Keep your working context somewhere you can see it.
On my Mac, that place is Tansei.
Common questions
What is vibe coding?
Vibe coding is describing the software you want in ordinary language and letting an AI coding agent generate, refine and debug it, while you steer the direction rather than write most of the lines yourself. The term describes a change in where human effort goes: less time typing syntax, more time deciding what should be built and judging whether the result is correct.
Do you need to know how to code to vibe code?
Not to start. You can get a working prototype with no programming background at all. You do need enough understanding to read an error message, judge whether the output is actually right, and know when something is unsafe to ship. Most beginner advice converges on the same point: the first preview is easy, and the real work starts after it appears.
What is the difference between the agent’s context and your own working context?
The agent’s context is durable project knowledge: the repository, specs, architecture, project rules and past decisions it needs to make sensible changes. Your working context is the temporary evidence you are reasoning from right now: screenshots, copied errors, reference links, colors, prompts and generated files. The first belongs in the project. The second belongs to you, and most of it stops mattering within the hour.
Which vibe coding tool should you start with?
Pick one and learn its rhythm rather than comparing tools for three days. Beginner-friendly builders such as Lovable get you to a working screen fastest, while coding agents and AI-native IDEs such as Claude Code and Cursor give you far more control over a real codebase. The right choice depends on how technical you want the workflow to become, not on which tool is currently most discussed.
Where should you keep screenshots, errors and references while vibe coding?
Somewhere visible and outside the agent. Notes apps and folders assume the material is worth filing permanently, and most of it is not. A shelf that sits at the edge of the screen suits it better, because the material stays reachable while the task is live and can be cleared when the task is done. On a Mac, that is what Tansei is for.
Does vibe coding still need testing?
Yes. A version that worked once is not the same as a product that works reliably. Test the result as a user rather than admiring it as a creator, and expect testing and judgment to matter more, not less, as the generation step gets faster.
How are you handling this in your own AI coding workflow?
If you are using Claude Code, Cursor, Codex, Replit, Lovable, or something else, where do you keep the screenshots, errors, references, prompts and files that matter for the current task but probably will not matter tomorrow?
Do you put everything into the agent, keep a scratchpad, use a dedicated tool, or are browser tabs and the desktop still doing most of the work?
The discussion is happening over on Indie Hackers →
I’m building Tansei, a Mac app for keeping the things you’re actively copying and using within reach while you work.
Tansei is available for Apple Silicon Macs running macOS 15 or later, and for Windows 11, as a $7 one-time purchase per platform — no subscription and no account required.
The AI can write the code. You still need to know where you put the screenshot.
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