Vibe coding for non-programmers

1What vibe coding is

Andrej Karpathy (OpenAI co-founder, former head of AI at Tesla) defined the term on 6 February 2025 in a single post:

“There's a new kind of coding I call “vibe coding”, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. […] I “Accept All” always, I don't read the diffs anymore. When I get error messages I just copy paste them in with no comment, usually that fixes it. […] I'm building a project or webapp, but it's not really coding — I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.”

Simon Willison (co-creator of Django and a long-running blogger on working with LLMs) drew the line between that and real work:

“If an LLM wrote the code for you, and you then reviewed it, tested it thoroughly and made sure you could explain how it works to someone else — that's not vibe coding, it's software development.”

In October 2025 he coined a name for the disciplined version: vibe engineering — “seasoned professionals accelerate their work with LLMs while staying proudly and confidently accountable for the software they produce”. This guide teaches that: the agent writes the code, you stay accountable for the result.

How an agent works

A coding agent is a program that reads your files, edits them, runs commands, and reads its own errors — in a loop, until the task is done:

This loop is what makes the method useful: the model runs the code and sees the result instead of guessing.

2What you can build and what you cannot

The method is reliable where you can check the result yourself. Where you cannot, you need an additional review.

An example that shows the difference

LDFcalc is a recipe calculator for composite filament: from the chosen matrix (PLA, PA6, PC), the fibre type and length, and the machine batch capacity, it computes the mass and volume of every component for a target carbon- or glass-fibre volume fraction. It was built with a single prompt. It saves roughly ten manual calculations per project.

Why it works: the task has a clearly defined input, a known formula, and a verifiable output. No user accounts, no database, no personal data. You can check the result with a pencil. This is the kind of task where the method works best.

The counter-example: the same page with customer logins, saved quotes, and payment would need a security review the model does not reliably perform. The first version would probably work, so the flaws would stay hidden.

Where the limit is in practice

The Stack Overflow 2025 survey names the same problem most often — 66% of developers cite solutions that are “almost right, but not quite”. That is why verification is part of the method, not an add-on. Who verifies determines the scope: for an internal tool it is enough that you check the output; for a public product you need someone who can read the code.

3What people build

Analyses of Reddit posts (r/vibecoding, r/ClaudeAI, r/ChatGPTCoding) show most of the activity around internal tools, prototypes, landing pages, and personal projects — not large products. The most-mentioned tools are Claude Code and Codex; a common route is a fast prototype in a browser tool (Lovable, Bolt), then a move to Claude Code or Codex for serious work (mention analysis).

Karpathy builds a web app

The author of the term used the method to build a real app, MenuGen: “You take a picture of a menu and it generates images for all the menu items.” He is not a web developer; Cursor and Claude wrote the code. His write-up is more useful than the app:

“I didn't even spend all that much work in the code editor itself. I spent most of it in the browser, moving between tabs and settings and configuring.”

Writing code is no longer the bottleneck. The bottleneck is glue work — auth, payments, keys, domains, settings. The model cannot do that for you, because it needs your account and your decision.

Simon Willison's 230+ tools

Willison publicly maintains tools.simonwillison.net: more than 230 small web tools, nearly all built with an LLM. The most instructive part is the colophon: each tool lists its commit messages, and many link to the full transcript of the conversation with the model. The code is open on GitHub. If you want to see how something was actually made, this is the best free material available.

The official use-case catalog

Anthropic’s catalog of sensible use cases for non-coding tasks lists tasks where an LLM helps. It is organised by role (engineering, data, research, marketing, finance, HR, legal). Examples such as “Build a daily briefing across your tools”, “Analyze patterns in user feedback”, and “Forecast & scenario modeling” come with concrete steps.

4The author's projects

I built these projects in my spare time as a mechanical engineer, with no programming background. All links work.

Tools You Can Use Right Away

• LDFcalcComposite filament recipe: carbon or glass fibre in a polymer matrix, scaled to the machine capacity.One prompt · ~10 manual calcs saved
• Ljubljana BulletinA tourist bulletin that scrapes events, curates them, and publishes daily with no manual step.Runs daily on its own
• ScalePrint ARPlaces an STL or 3MF file in the room at true scale through WebXR.WebXR in the browser
• Board Game RulesBilingual rules for 24 remade board games; every box carries a QR code.QR code on the box

Physical and Interactive Projects

• Clay StudioParametric vessel design that emits G-code for the Delta WASP 40100 clay printer.G-code for clay
• TileGrooveA camera tracks printed tiles; position and rotation drive sound in the browser.Camera and sound
• XIANOA synthesizer with a custom PCB and capacitive keyboard; firmware on a Seeed XIAO.Real-time audio, kilobytes of memory
• DK LabEight small tools: phone sensors, image transfer over WebRTC, language utilities.One afternoon each

More Ambitious Experiments

• Nightona US&AArcade racing on Three.js: a banked oval, procedural tracks, AI drivers.Three.js
• TriviadorPub quiz with a question bank and synchronized sub-quizzes — no server, no database.Static hosting
• Marine AidA Raspberry Pi watches a vessel's surroundings with one camera, using image segmentation.Hardware + vision + alerts

5At work and for your career

The chapter starts with a portfolio and CV because they make a good first project: the result is immediately visible and useful. Everyday work tasks and examples by profession follow.

Portfolio and CV

You give the agent your references, a few images and an example page you like. After a few rounds of corrections you have a web portfolio and a PDF CV from the same source. You see the result in the browser immediately, a correction is one sentence, and the finished thing is a link you can send to an employer or a client.

Why it suits a first project:

• The result is visible and verifiable. You open the page and know within a second whether it is good. You can check a result like that yourself (chapter 2).
• You already have the content. An old CV, LinkedIn, project descriptions, photos of your work. You invent nothing — the agent rearranges and typesets.
• You can paste a visual reference. A screenshot of a page you like, or a link to it. “Make it more like this” works far better than “make it prettier”.
• One source, two products. The same data gives you a web page with a public link, and printing that page to PDF gives you a one-page CV.
• Maintenance is one sentence. Six months later: “add this project and update the date.” No Word, no re-breaking the layout.

I have an old CV, project descriptions and a few photos in this
folder. Build me a simple personal portfolio from this that works
well on a phone and that I can also print as a one-page A4 CV.

Do not invent any data - use only what is in the folder. First review
the material and pick the 3 to 6 best projects, then show me the
proposed structure before you start writing code.

For the look, use the attached reference (a link or a screenshot) -
take the proportions and the calm, not the content. One typeface,
one accent colour, light and dark theme.

After every version, take a screenshot at 390 px and 1440 px wide and
show it to me. At the end, add @media print so the page prints on one
A4 sheet with no navigation.

After two or three rounds of corrections (“the heading is too big”, “put the projects in a grid”, “the print breaks onto a second page”) it is done. Deploying is covered in chapter 15 and is free.

Seven typical tasks

• Building other people’s software. You find an open-source tool that almost does what you need. The agent downloads it, compiles it (turns the source code into a program the computer can actually run), adds your patch, and writes down what it changed. This used to mean a week of reading someone else’s build instructions.
• Programs for small devices. Firmware is a program that runs directly on a device with no operating system, the same way the program in a washing machine or a thermostat does. An ESP32 is a cheap microcontroller, a computer the size of a postage stamp costing about €5, with Wi-Fi built in. Example task: “Have an ESP32 read a temperature sensor every 10 s and save the readings to CSV.” The agent writes the program and tells you which sensor wire goes to which pin (the pinout).
• Literature review. Selecting papers, extracts of methods and results, comparison tables with citations. Open and check the citations, because the model can get them wrong.
• Measurements and reports. A script reads the raw exports from an instrument, removes errors and duplicate rows, plots charts, and drafts a commentary. You approve the conclusions.
• Scripts for small chores. Renaming 400 files by pattern, merging PDFs, Excel formulas and macros, converting between formats. Chores that were never worth looking for a tool for.
• Single-purpose apps. A tally counter with big buttons for counting events during a measurement, a sample-entry form, a protocol timer. Each takes about half an hour.
• Metrics. A script reads the weekly export and plots the key indicators (KPIs) on one page that management can read too.

Find your profession

The professions below have little to do with computing at first glance. The same rule applies to each: take a task you currently do by hand that has a verifiable result. Hover over a tile or tap it to reveal the description.

• Engineer / designerTurns a bill of materials into a purchase order automatically, and flags any line that costs more than the last order.
• Researcher / labTurns raw readings into a control chart and automatically flags a value that falls outside the allowed limits.
• Analytics / financeEvery Monday reads the system export and plots the key indicators on one page, ready for the meeting.
• Office / adminPulls dates, amounts and orders out of emails and attachments automatically and lines them up in one spreadsheet.
• Sales / marketingBuilds a customer-specific quote from a price list and template in seconds, instead of writing one by hand every time.
• Project managerTurns meeting notes into a list of decisions with owners and dates, instead of digging them back out of a recording.
• Teacher / lecturerTurns one topic into a worksheet with answer keys at three difficulty tiers.
• Tradesperson / repairTurns a photo of finished work and a few details into an invoice and a job sheet for the customer.
• HealthcareAdministrative work only: scheduling, tidying exports, preparing an anonymised research set — never diagnosis or dosing.

Everyday life and hobbies

The method does not distinguish between work and free time — the seven ideas above apply just as well to hobbies. Here are a few life situations they didn't cover.

• Running and cycling

  Turning GPX files into a weekly summary: distance, elevation, pace by segment, compared with the same route last month.

• Home and bills

  An energy-use tracker from monthly readings; service reminders; a payback calculator for a heat pump; a contractor-quote comparator.

• Parent

  A feed and sleep log with big buttons, no login; a clothing-size tracker; appointment reminders; a monthly album from a photo folder.

• Hobbies and workshop

  A schedule for a tournament or club; a materials-stock tracker for the workshop; a timer and log for a personal project built in stages.

Tools the agent already knows: DuckDB (analytical SQL over local files, no server), Polars or pandas for tables, Jupyter for exploration, Streamlit for a colleague-facing interface.

6Getting started: three decisions

Three steps to a first working result. The following chapters explain them in more detail.

1. Choose a provider

   Test ChatGPT, Claude, and Gemini on three or four of your own tasks: one writing task, one data task, one document or screenshot, and one small coding task. Use the free tiers.

   Pick the one whose answers you most often keep without major repair. Do not decide by leaderboards — see Choosing a Model.

2. Choose a way of working

   For questions and short transformations, use chat. For a task across several files, Work/Cowork. For a project that must be edited and run, a coding agent. For recurring chores, a schedule.

   Detail in Ways of Working and Tools.

3. Start working

   Write clearly what you want and how. A reliable result also needs a check. A good first request answers four questions:

   • What it should do — the function, in one sentence.
   • What goes in and what comes out — inputs, outputs, units, formats.
   • How it should look and behave — no login, works on a phone, works offline.
   • What it must not do — no database, no external libraries, no user accounts.

   An example of such a request — this is how LDFcalc was made:

   I work out recipes for composite filament and want to stop doing the
   maths by hand every time. Build me one simple web page where I enter
   the matrix (PLA, PA6 or PC), the fibre type (carbon or glass), its
   length and the machine batch capacity; the target fibre fraction
   should recalculate the mass and volume of every component live, with
   no "calculate" button.
   
   Make it one page, no login and no database, and it should work on a
   phone and offline. Keep the material densities visible and let me
   edit them if I need to.

   Then verify the result. For a calculator, work one case out by hand. For data processing, compare a few rows against a known answer. If you cannot verify the output, the task is not specified correctly yet.

Free official courses, if you prefer guided teaching: Claude Academy (Claude 101 → Claude Code in Action), OpenAI Academy, Google AI Essentials, GitHub Skills.

7Ways of working and tools

You can use the same model in four ways. Product names change, the ways stay. The way you work also affects the result: which tools the model has and how much of your project it can see.

What a coding agent can do

You use Claude Code and Codex in a terminal, a code editor or a desktop app. Both also have a web mode where the code runs at the provider. When the agent works in a folder on your machine:

• It reads your files. It sees what you have instead of guessing from a description.
• It runs commands (builds, tests, scripts) and reads their output.
• It fixes its own errors until the test passes.
• It works with Git: it sees history, saves and compares versions.
• The project can be larger than the context, because the agent reads files as needed. You can also put manuals as PDFs, old scripts and templates in the folder; the agent finds what it needs.
• You see every change as a diff and can reject or revert it, instead of copying code out of a chat.
• It is included in the subscription (chapter 9).

Anthropic recommends giving the agent a way to check its own work: a test, a build or a screenshot to compare. “It's the difference between a session you watch and one you walk away from.” (Claude Code best practices)

Listing finder

Every day at 8.00 check bolha.com for: [WHAT YOU WANT].

HARD FILTERS (no exceptions)
- price: [FROM]-[TO] EUR
- location: within [X] km of [PLACE], or shipped
- age / year / condition: [...]
- exclude listings containing: faulty, for parts, damaged,
  replica, not working, [YOUR OWN WORDS]

WHAT COUNTS AS A GOOD DEAL
- compare against other active listings for the same model
- rate the price as: below market / market / above market, and by how many %
- account for condition, age, included accessories and distance
- if the price is far below market, flag that explicitly as suspicious

THE REPORT
- only listings that are NEW since the last check, never repeat ones I saw
- at most 5, ranked by value for money
- for each: title, price, location, how old the listing is, link
- one sentence: why it stands out
- one sentence: what to watch out for (missing photos, vague
  description, a common fault on this model, price too low)
- at the end: how today compares with last week

RULES
- if there is nothing new worth seeing, just write "nothing new"
- never invent a price or a detail that is not in the listing
- always include the original link

Clear limits keep the number of matches down, and asking only for new listings stops repeats. The most important line is the ban on inventing details: without it the model tends to fill gaps. For the first few days compare the report with the original listings and adjust the criteria.

every day at 8.00 search bolha for e-mtb and give me a curated list
of current best offers, what makes them stand out and what I should
be careful about and how they compare to other current and past offers

create a retro-looking ljubljana bulletin for tourist in style of
new york times that scrapes events from xx.dsd, yy.com, ztz.com websites,
curates them and publishes them daily

8Choosing a model

Public benchmarks say little about your work. Take three or four tasks you actually do and give them to several models. Keep the one whose answers you most often use without edits.

ChatGPT, Claude and Gemini

The table summarises published comparisons and user experience, not a test of my own. Use it as a starting point.

Which model tier

Every provider has models in three tiers. The names change; the price gap between tiers is often fivefold or more.

• Small model (Haiku, mini): summaries, sorting, simple conversions and short, well-scoped coding tasks. Not the first choice for hard debugging.
• Default model (Sonnet, main GPT, Gemini Pro): where most users get their work done. Start here.
• Most capable model (Fable/Opus, Deep Think): large rewrites, hard reasoning, tasks where the default fails repeatedly. Slower, and it uses up limits faster; your plan says by how much.

Other useful tools: Perplexity (cited search; open and check the citations), NotebookLM (answers strictly from uploaded sources), Elicit and Consensus (academic literature), Lovable and Bolt (browser prototypes, nothing to install).

9Subscription vs API

Beginners often pay API rates for work their subscription already covers. The difference is in what you pay for.

A subscription does not cover the API. ChatGPT Plus and the OpenAI API are separate accounts with separate billing; the same holds for Claude.

The coding agents, however, are included in the subscription:

• Codex is included in ChatGPT plans, with limits that differ between plans. Access on Free and Go is currently time-limited; check when you sign up (OpenAI: Using Codex with your ChatGPT plan).
• Claude Code is included in the relevant Claude plans (Pro, Max and business plans); check the current limits with Anthropic.

10The context window

Most good practices for working with an agent follow from one constraint:

“Most best practices are based on one constraint: Claude's context window fills up fast, and performance degrades as it fills.”

The context window is everything the model considers at once for the current task: your messages, its replies, the contents of files it read, and command output. It is measured in tokens — chunks of text, roughly three quarters of a word.

During a longer session the amount of history grows until the system compacts, summarises or truncates it. A single debugging session can use tens of thousands of tokens. Compaction loses detail, so the model later forgets early instructions. So keep unrelated tasks apart and write important rules into project files and tests.

11Workflow

Ten steps from idea to a published tool. The first three are preparation and have the most influence on the result.

12Prompting and good practices

No special phrases are needed. Explain the task as you would to a colleague: what you want, what you already have, what must not change and how you will both know the result is good.

Example: "The pricing table on this page is unreadable on a phone — rows squeeze together and the numbers get cut off. Keep every price and description, and change only the table layout below 480 px. Don't touch anything else. When you're done, show me a screenshot at 375 px."

Keep all text and links. Remove cards and gradients.
One 760 px column, white background, blue only for links.
No horizontal overflow at 390 px (exception: the table).
Run it and show the changed files.

Which language to write in

Models understand Slovenian and answer fluently. Even so, the language you write in matters, for two reasons.

1. A smaller language costs far more tokens. The tokenizer was built mostly on English text. An English word is often one token; the same word in another language is often three or four, and accented letters are frequently tokens of their own. Oxford research found the same text can be up to 15 times longer in tokens across languages — considerably more expensive for the same content in some languages (Petrov et al., 2023). Your context window therefore fills faster and your limits run out sooner.

2. The model saw technical material almost exclusively in English. Documentation, library names, error messages and developer discussions are English. Written about in another language, the model translates as it goes and more often invents terms or gets grammar wrong.

13Five common failures

Condensed from Avoid common failure patterns in Claude Code best practices. Each has a simple fix.

14Git

Git records project history locally. GitHub keeps a remote copy. Git is not required, but it is the fastest way back to a working version when an agent breaks several files at once.

A commit is a saved snapshot with a description. A branch is a parallel line of work. Push and pull synchronize with the remote copy (more on branches).

Graphical client: GitHub Desktop. Learning: Learn Git Branching, GitHub Skills.

15Publishing to the web

This is how this page and every project above is published: push the repository to GitHub (it can be private) and, in Cloudflare Pages, connect the repository. Each push publishes a new version and each branch gets its own preview address. The *.pages.dev address works immediately, a custom domain is optional.

Build settings:

An agent is a program with permissions

A coding agent can read files, run commands and open web pages. What it is allowed to do matters more for security than which model you use.

• Give the agent only the permissions the task needs.
• Read commands before you approve them.
• Block reading of secrets: .env, private keys, certificates and credential files.
• Review an unfamiliar repository first. Installing packages runs someone else's code.
• What the agent reads is not a command. A web page or document can contain hidden instructions for the agent (prompt injection).
• Run risky things separately: in a sandbox, a container or under a separate user.

Permission settings in Claude Code: security, permissions, configuration.

16Sensitive data

Before you put personal, business or health data into a chat or project:

• Check your organisation's rules and the provider's terms, including whether data is used to train models.
• Where you can, anonymise the data or use a made-up sample with the same structure.
• Do not send more than the task needs.

The strongest protection is a local model with no cloud connection. Run one with Ollama or LM Studio. Lower level: llama.cpp. Browser interface: Open WebUI.

A small local model is enough for classification, extraction, and summaries. For harder reasoning and long agent work, cloud models are still considerably better. Do not take the word “local” on trust: a local interface can still call web search, cloud embeddings, or telemetry. Check the settings and the network traffic.

17Glossary

LLM (large language model)

A model that builds text from learned patterns, not from a database of facts, so check facts.

Token

A chunk of text, roughly three quarters of an English word. Usage is measured in tokens.

Context window

Everything the model currently considers: messages, files, command output. It accumulates during a session until the system compacts or truncates it. See the section.

Coding agent

A program that reads and edits files locally, runs commands, and fixes its own errors — Claude Code, Codex.

Skill

A saved procedure in a SKILL.md file that loads only when used.

Subagent

A separate session with its own context that does the research and returns only a summary.

Prompt

The instruction you give the model. A good prompt clearly describes the task, the input and the result you want.

Diff

The list of changes between two versions of a file. Review it before you accept a change.

Multimodality

Input and output beyond text: images, PDFs, audio, video.

Commit

A saved snapshot of the project in Git, with a description of the changes. See the section.

Subscription vs API

A subscription pays for a person in the app; the API pays for calls from your code, by usage. See the section.

18Sources and further reading

Sources are also linked in the text. Checked September 2026. For prices and names, the provider's page applies.