How to Start Vibe Coding — Expanded & Detailed Guide
Welcome to the slightly chaotic, tremendously useful world of vibe coding—where you spend more time talking to an AI and less time wrestling with boilerplate.
If you've ever wanted to spin up a working prototype in an afternoon, or turn an idea into a demo without building a full team, this guide is for you.
What is vibe coding?
Vibe coding is an LLM‑first, conversational approach to writing software. Instead of typing every line of code, you describe the behavior you want to an AI assistant or agent and let it geVibe codingnerate the scaffolding, logic, tests, and sometimes even deployment steps.
The power of vibe coding is the loop: craft a clear spec, let the model produce code, run it, feed the errors back into the model, and repeat until the feature works.
The model acts like an energetic junior developer who can do repetitive plumbing instantly but still needs human direction, tests, and vetting. The secret sauce is discipline in the loop: always require unit tests, insist on plain‑English explanations for changes, and never auto‑merge an AI’s PR without a human check.
Who is vibe coding for?
Vibe coding is made for makers: solo founders, product designers, product‑led growth folks, indie hackers, and curious learners who want to move from idea → prototype quickly.
It’s particularly powerful when you want to validate assumptions (does this feature solve a problem?), build internal automation (expense reconciliation, simple admin tools), or ship a demo that proves product‑market fit.
You don’t need to be a senior engineer to benefit, but you do need practical judgment—being able to run a server, read a stack trace, and write or run a simple test will make the difference between a neat demo and a catastrophic merge.
Not for: large enterprise systems, safety‑critical infrastructure, regulated financial systems, or anything that requires formal security audits—unless you add proper human review, testing, and security hardening afterward.
Ideas for vibe‑coding projects (real, tiny, delightful)
The best vibe‑coding projects are small, self‑contained, and valuable. A few low‑friction ideas with quick outcomes:
CSV Summarizer — upload a bank export or expense sheet and get categorized totals and flagged anomalies. (Use: Pandas/Node CSV parser → small web UI.)
One‑page TODO app with local auth — useful as a learning project and a deployable demo. Includes create/read/update/delete, local storage or SQLite, and a small CSS framework.
RSS → Notion importer — fetch articles and append summaries to your Notion database with a simple webhook flow.
Invoice reconcile admin — upload two CSVs and let the app match line items (fuzzy matching), surface mismatches, and export reconciliation reports.
Landing + payment proof — a marketing page with a test Stripe webhook that records demo purchases into a DB.
Each of these has clear inputs and outputs, limited edge cases, and gives you a concrete artefact to test prompts and agent behavior.
Tech expertise: how much do you really need?
Short version: not much to start, but more to ship safely. If you can git clone, npm install/pip install, run a script, and paste logs into a chat, you’ll make good progress.
The next level of competence—reading stack traces, adding a unit test, and inspecting the dependency list—lets you catch hallucinations, insecure defaults, or license issues.
If you plan to run an app externally or for multiple users, add a developer or ask a contractor to audit the AI‑generated code before production.
Vibe Coding Tools to consider
Tools split into two categories: chat→product builders (fast, opinionated, hosted) and agentic developer tools (deep repo access and fine‑grained control).
Lovable.dev — one‑click prototypes with a friendly interface
What it is: A chat-driven product builder that generates full stacks from prompts.
Good for: founders who want a quick demo with a hosted preview link.
Caveats: opinionated output—extract the code if you intend to maintain it; review dependencies and auth flows before exposing real data.
Example prompt:
"Build a small TODO web app with local auth, using Express and React, persist to SQLite, and include two unit tests: one for creating a TODO and one for listing TODOs."
Bolt.new — speed and polish for landing pages and simple apps
What it is: Rapid generator optimized for UX and deployable demos.
Good for: landing pages, simple SaaS prototypes, marketing proof‑of‑concepts.
Caveats: less flexible once you want custom server logic; best used for idea validation.
Replit — the friendly cloud IDE with AI agents
What it is: Cloud IDE + runtime + AI helpers.
Good for: sharing live prototypes, iterating fast in a sandboxed environment, or running demos for non‑technical stakeholders.
Caveats: production readiness requires export and audit; not ideal for scaling.
Cursor — the developer’s companion editor
What it is: Code editor with deep AI assistance: refactors, context‑aware completions, and PR help.
Good for: developers who want an AI that helps in‑editor without replacing control.
Caveats: better when you already have dev chops and a codebase to improve.
Windsurf — live previews and single‑prompt scaffolds
What it is: Agentic IDE designed for minimal friction from idea → running app.
Good for: end‑to‑end scaffolds, UI iterations, and preview‑first workflows.
Caveats: still an evolving UX; vet generated server code before shipping.
Claude Code — safety‑focused agentic coding
What it is: Anthropic’s agentic tooling that prioritizes long context and safer reasoning across codebases.
Good for: teams needing auditability and alignment in automated coding tasks.
Caveats: access and workflow integration may vary by plan.
OpenAI Codex & agent tools — automation for engineering chores
What it is: Powerful agents for repo tasks: propose PRs, fix tests, and automate repetitive dev tasks.
Good for: accelerating engineering workflows, generating patches, or writing unit tests.
Caveats: treat outputs as proposed changes—human review is non‑negotiable.
When you hit a wall during the agent loop, it helps to have a human‑in‑the‑loop fallback.
Services like Perfect.Codes connect you with experienced developers who can join a short session to patch bugs, clean up AI‑generated code, and explain fixes in plain English.
Treat this as a safety valve: keep iterating with your AI assistant, and if the issue involves authentication, complex dependency conflicts, or production‑sensitive concerns, book a quick session to get a fast, tested fix.
How to Fix Errors, Bugs and have a debug loop (practical steps)
The agent debug loop is: run → capture error → feed the context → request a minimal patch + test → review. When an error appears, copy the failing stack trace and the smallest relevant files into your prompt.
Use diff‑aware prompts: "Here’s the failing test and todo.js— propose a minimal patch and add one unit test that reproduces the bug."
Always ask the agent for a plain‑English summary of the bug and why the patch fixes it.
That explanation is typically a better signal of correctness than the code itself.
Common pitfalls & guardrails (concrete advice)
Hallucinated APIs: always verify third‑party calls and check the docs for endpoints.
Insecure defaults: ensure secrets aren't hardcoded and that auth flows are sane.
Dependency & license risk: pin versions, run npm audit/pip-audit, and use SCA tooling.
Messy agent commits: require PRs, linters, and a pre‑merge checklist.
Vendor lock: keep local copies and export generated code regularly.
Suggested guardrails: Git hooks that run linters/tests, CI that fails on missing tests, SCA scans, and manual approvals for production merges.
Mini prompt cookbook (copy‑ready templates)
Spec prompt:
"Build a small web app that does X. Tech stack: Y. Inputs: A. Outputs: B. Constraints: C. Provide file list, minimal tests, and a README to run locally."
Debug prompt:
"Failing test attached. Files: list. Stack trace: attach. Please propose minimal patch and one unit test that reproduces the bug. Explain the bug in 2 lines."
Refactor prompt:
"Refactor this module to follow pattern X (e.g., Repository pattern). Return a patch in unified diff format and explain changes in bullet points."
Conclusion — start small, ship safely
Vibe coding gives you a superpower: extraordinary speed for prototyping and internal tooling. But with that speed comes responsibility: tests, reviews, and guardrails.
Start with a tiny, well‑scoped project, pick the tool that matches your goal (demo vs production), and practice a disciplined loop of spec → run → test → review.