What You Will Learn

• Direct an AI dev environment to turn ideas into production-grade web apps
• Use Plan Mode and claude.md to create architecture, file trees, and execution plans
• Integrate MCPs (Context7, Playwright) to ground code in current docs and automate tests
• Set up Supabase, manage .env secrets, and deploy via GitHub → Vercel
• Debug iteratively with reproducible logs, screenshots, and agent-driven fixes

Study Guide

How To Build Anything With No Coding Knowledge

You don't need to become a programmer to build software. You need to learn how to direct one. This course shows you how to use an AI development environment to take an idea from a sentence in your notes app to a live, production-grade web application. You'll learn the exact workflow: plan strategically with the AI, inject the latest documentation so it writes modern code, delegate work to specialized agents, debug through conversation, set up your database, and deploy with one click. By the end, you'll operate like a product director who can ship full-stack apps without writing a single line yourself.

Here's what makes this valuable: the AI now handles the heavy lifting,architecture, code generation, testing, and deployment,while you focus on clarity, decision-making, and iteration. This is a build-anything playbook: a method you can reuse for MVPs, internal tools, prototypes, or full products,fast, affordable, and without hiring a team.

The New Role: From Coder To Director

The old way: wrestle with syntax and package versions. The new way: describe what you want, review a plan, approve, and iterate. Your leverage isn't code,it's clarity. Think in outcomes, user flows, and constraints. Your job is to guide an intelligent system through the software development lifecycle. The system can create thousands of lines of working code while you steer the direction.

Two mindsets unlock this:

1) Always plan before building. 2) Always ground the AI in current documentation and feedback from your live app. When you do both, the AI becomes a reliable, modern, senior developer that never sleeps.

Example:
Instead of "Build me a journaling app," say: "Build a Next.js app with Supabase auth. Users can create, edit, and search journal entries. Use Tailwind for UI. Save entries in Postgres with fields: user_id, title, content, created_at. Include dark mode and mobile-first design."

Example:
Instead of "Fix the error," say: "On submit, I get 401 Unauthorized. Here's the full console output, network request payload, and response headers. I'm logged in; session token looks valid."

Key Concepts & Terminology (So You Speak The Language)

Claude Code: an AI development environment that writes, refactors, and manages code via conversation.

Agent: a specialized AI persona (QA tester, backend engineer, doc writer) with its own role, tools, and memory. You can run them in sequence or in parallel.

MCP (Meta-Circular Prompting server; sometimes called Mentionable Context Provider): an external context source you plug into the AI so it reads the latest docs, runs browser automation, or fetches data on demand. Context7 is a key MCP that aggregates official docs for modern frameworks. Playwright MCP enables automated browser testing.

Plan Mode: a feature that forces the AI to produce a detailed plan (tech stack, file tree, steps) before writing code. You review, refine, and then approve.

/init: a command that bootstraps your project and creates the central project brain file.

claude.md: the brain of the project. Architecture, decisions, constraints, and goals live here. The AI reads and updates it constantly. "The claude.md file provides guidance to the AI when working with code in the repository. You can think of this as the brain of the project."

API: a bridge to external services (text generation, image generation, database calls).

Supabase: managed Postgres, authentication, storage, and APIs.

GitHub: version control and code hosting.

Vercel: modern hosting that auto-builds and deploys from GitHub commits.

.env.local: a local file for secret keys (API keys, database credentials). Never commit this file.

Console Log: the browser's diagnostic output. Your best friend during debugging.

Foundational Setup & Prerequisites

Set these once and you'll be able to spin up unlimited projects.

Core services you'll need:

- AI Development Environment: Use Claude Code (or similar) for a conversational code workflow.

- Version Control: GitHub account to store your code and track changes.

- Hosting & Deployment: Vercel account to auto-build and host your app.

- Database & Authentication: Supabase account for Postgres, auth, and storage.

API keys you'll likely need:

- Anthropic (for text generation)

- Fal.ai (for image generation)

Command Line Interface (CLI): Install GitHub CLI to let the AI initialize repos, commit, and push without manual setup.

Cost structure to understand:

- GitHub, Vercel, Supabase: generous free tiers good for learning and MVPs.

- API usage: metered. Be intentional with model selection, token limits, and caching.

- AI environment: a subscription can be more predictable than pure API billing for heavy builds. "Opus plan mode uses a big, smart model for planning and strategy, and then uses a faster model to write the code. You get the best of both worlds without maxing out your usage limits."

Example:
Minimum viable setup: GitHub (free), Vercel (free), Supabase (free), Anthropic API key with a small starting balance, Fal.ai with a small starting balance.

Example:
Pro setup for speed: Claude Code subscription with Opus planning + Sonnet coding, GitHub CLI installed, Context7 MCP enabled, Playwright MCP installed for automated testing.

The Core Workflow: From Idea To Code

Here's the end-to-end loop you'll use for every feature and every app.

1) Project Initialization

- Create a new empty folder for your project.

- Open the folder in your AI development environment.

- Run /init. The tool sets up basic settings and creates claude.md (your project brain).

- Add a one-paragraph vision to claude.md: the problem, the user, the outcome, and any hard constraints (frameworks, design system, APIs).

Example:
Vision: "Build 'The No Machine' , users paste a situation they want to decline. App returns three responses (light, medium, firm) using Anthropic. Generate a friendly image per response using Fal.ai. Save history per user with Supabase auth."

Example:
Vision: "Build 'Micro CRM' , capture leads, track contact stages, and schedule follow-ups. Basic Kanban board UI, Supabase auth, activity log, and CSV export."

2) Planning Phase (activate Plan Mode)

- Turn on Plan Mode so the AI creates a strategy before writing code.

- Describe your app clearly: features, APIs, pages, user roles, and data structure.

- Expect the plan to include:

* Tech Stack: e.g., Next.js + TypeScript, Tailwind CSS

* Backend & Database: Supabase auth, RPC endpoints, schemas

* Project Structure: folder and file layout

* Implementation Steps: numbered, verifiable checklist

- If available, enable "Opus Plan Mode" for best planning quality and production-ready structure. Code generation can run on a faster model for speed and cost control.

Example:
Plan output includes: app/pages for routes, /lib for API clients, /components for UI, auth with Supabase middleware, database schema with tables and indexes, environment variables list, and end-to-end steps.

Example:
Plan output for an analytics dashboard: ingestion endpoint, cron tasks (edge), charting library, data model for events, RBAC (roles: viewer, admin), caching layer, testing plan, and deployment strategy.

3) Implementation (approve the plan)

- Approve or edit the plan. Then instruct the AI to execute it step-by-step.

- The AI will: create folders, scaffold the app, write components and pages, implement API calls, wire up auth, and set up environment variables.

- Keep claude.md updated with decisions and constraints as you go, so future changes stay coherent.

Example:
The AI creates pages: /login, /dashboard, /history; components: PromptForm, ResponseCard; libs: anthropicClient, falClient, supabaseClient; and utility functions for rate limiting.

Example:
For a habit tracker: creates /habits, /stats; components: HabitList, HabitForm, StreakCard; libs: supabaseClient; and cron job setup for sending reminders.

Advanced Context Engineering: MCPs Done Right

AI models aren't born up-to-date. They need access to current documentation. That's what MCPs provide. They are external servers that feed context into the AI on demand so it writes modern, reliable code and confirms patterns against official docs.

Context7 MCP is the MVP here. It aggregates the latest official docs for frameworks like Next.js and services like Supabase and Vercel. You ask the AI to "review the plan with context7," and it cross-checks your architecture and code against the newest standards.

Best practices:

- Install Context7 MCP and test it once so it's available in every session.

- During planning, explicitly request: "Verify this plan with Context7."

- During code reviews, say: "Audit auth and middleware against current Next.js and Supabase docs via Context7."

- Bake the MCP into your agent prompts so they consistently use it. "Proactively using a specific MCP server in an agent's prompt ensures the agent will use that tool consistently to verify its work."

Example:
Without Context7, the AI might use outdated Next.js routing or Supabase auth methods. With Context7, it updates to the current App Router and proper middleware for protected routes.

Example:
Without Context7, environment variable handling can break in serverless environments. With it, your env strategy matches Vercel's current best practices.

Agent Orchestration: Multiply Yourself

Agents are specialized AI workers with independent context windows, tools, and instructions. You can chain them (sequential) or run them in parallel. They enable complex workflows and higher quality without extra headcount.

Creating and customizing agents:

- Define a clear role: "QA tester that tries to break the app," "Backend engineer optimizing queries," "Technical writer creating developer docs."

- Assign tools: Context7 MCP for docs, Playwright MCP for browser automation, a file system tool for code edits, and a test runner.

- Choose models smartly: Opus for strategic review, faster models for routine tasks.

- Embed constraints: coding standards, security requirements, and "always verify with Context7."

Chaining and parallel execution:

- Chain example: Frontend Developer agent builds the UI → QA Tester agent runs Playwright tests and reports issues → Fixer agent applies code changes → Docs agent updates README and claude.md.

- Parallel example: Three Researcher agents gather docs and patterns for auth, image generation, and caching at the same time → their outputs feed a Planner agent that synthesizes the final approach.

Tool assignment tips:

- Give your QA agent the Playwright MCP so it can automate user flows (login, form submit, error states) and attach traces or screenshots to its reports.

- Give your Planner agent Context7 as a mandatory tool during planning and feature additions.

Example:
QA agent with Playwright MCP navigates to /login, tests invalid credentials, verifies error messages, then tests signup, logout, and protected route access. It files a report with failing steps and logs.

Example:
Backend agent reviews Supabase queries, adds indexes, and replaces N+1 patterns with proper joins. It proposes SQL migrations and updates the data-access layer with typed helpers.

The Iterative Debugging Cycle: Conversation As Your IDE

Bugs aren't failures. They're feedback. Here's the loop that turns issues into momentum:

1) Identify the issue: you see a crash, visual glitch, or incorrect behavior.

2) Provide context: report the exact error message, a screenshot, and the full browser console log. Include steps to reproduce (what you clicked, what data you entered).

3) AI diagnosis and repair: the AI traces the error, modifies the right files, and explains the fix.

4) Verification: you retest. If it persists, repeat with updated logs or new screenshots.

Be specific, complete, and neutral in your feedback. You're feeding the AI the raw materials it needs to fix things fast.

Example:
Issue: Image generation fails only on the deployed site. You provide: steps to reproduce, Vercel logs, network request payload, and response code. The AI discovers the missing Fal.ai key in Vercel environment variables, adds it, and redeploys.

Example:
Issue: Authenticated API route returns 401 locally after a page refresh. You provide: console logs, cookies list, and Supabase session object output. The AI updates middleware to refresh sessions on route access and fixes cookie serialization.

Tips for speed:

- Always paste the full console log for the exact user flow.

- If UI is wrong, send a screenshot with annotations (what you expected vs what you saw).

- Include environment differences: "Works locally; fails on Vercel after cold start."

Configuration, Secrets, And API Integration

Your app will talk to external services. Keys and URLs must live in environment variables.

Steps:

- Locate the .env.local file (the AI will scaffold it). If hidden, reveal hidden files via your OS shortcut. This file is ignored by Git by default.

- Supabase setup: grab the Project URL, anon public key, and service_role key (use wisely,server-side only). Paste into .env.local as directed by the AI.

- Run the database schema: the AI generates SQL for tables and indexes. Open Supabase → SQL Editor → paste and run. Confirm tables exist.

- Add AI API keys: generate Anthropic and Fal.ai keys; paste into .env.local. Note exact variable names the AI expects.

- Restart your dev server so new env vars load. The AI can handle the restart.

Security guidelines:

- Never expose secrets to the client; only the anon public key is safe for client-side initialization under controlled scopes.

- Keep the service_role key on server-only routes and environments (never ship it to the browser).

- On Vercel, replicate every env var you used locally into the project's environment settings. Case-sensitive.

Example:
For "The No Machine," env vars include: NEXT_PUBLIC_SUPABASE_URL, NEXT_PUBLIC_SUPABASE_ANON_KEY, SUPABASE_SERVICE_ROLE_KEY (server), ANTHROPIC_API_KEY (server), FAL_API_KEY (server).

Example:
For a task app with email reminders: add EMAIL_PROVIDER_API_KEY (server), NEXT_PUBLIC_APP_URL (for links in emails), and JWT_SECRET (server) for any custom tokens if needed.

Database Integration With Supabase

Supabase gives you Postgres, auth, and APIs without backend boilerplate.

AI-driven schema design:

- Tell the AI your entities, relationships, and access patterns. It proposes tables, indexes, and policies.

- It will generate SQL for you to run in Supabase's SQL Editor.

- It can also write Row Level Security (RLS) policies so users can only access their own data.

Good patterns:

- Always include user_id (uuid) columns for user-specific data.

- Add created_at and updated_at timestamps.

- Use indexes for frequent filters (e.g., user_id + created_at desc).

- Let the AI create typed data-access helpers and a supabaseClient with the correct auth flow.

Example:
Schema for "The No Machine": tables: profiles (user_id, name), boundaries (id, user_id, prompt, tone, response, image_url, created_at). Index on user_id, created_at. Policy: user_id = auth.uid().

Example:
Schema for a micro CRM: leads (id, owner_id, name, email, stage, notes, created_at), activities (id, lead_id, owner_id, type, body, created_at). Index leads by owner_id, stage. Policies constrain reads/writes to owner_id = auth.uid().

From Plan To Execution: Putting It All Together

Let's play out the flow end-to-end with two apps to show repeatability.

App 1: The No Machine (text + image generation, auth, history)

- Plan Mode: define Next.js + TypeScript, Tailwind, Supabase auth, routes (/login, /app, /history), API modules for Anthropic and Fal.ai, and a rate limiter per user.

- Context7 check: update routing to the current Next.js standard; verify server actions and edge compatibility; confirm Supabase auth middleware.

- Implementation: AI scaffolds files and components, adds forms, calls Anthropic for three tones, calls Fal.ai, and saves results.

- Debugging: you test; if an image fails in prod only, provide Vercel logs; AI fixes env var and redeploys.

- Deployment: push to GitHub; connect to Vercel; set env vars; deploy; test live; iterate.

Example:
User enters: "Decline a last-minute meeting on a weekend." App returns: Light: "Hey! I'm unplugging this weekend,can we do Monday morning?" Medium: "I'm not available this weekend. Happy to regroup during work hours." Firm: "I don't take meetings on weekends. Please propose weekday times." Each with a friendly image.

Example:
History view shows timestamped entries with filters by tone and a one-click copy-to-clipboard button. Users can delete items or re-generate better images.

App 2: Micro CRM (data model + CRUD + analytics)

- Plan Mode: define Next.js + Tailwind + Supabase. Routes: /leads, /pipeline, /reports. Components: LeadForm, LeadCard, PipelineBoard. Features: CSV import/export, activity logs, stage transitions with drag-and-drop.

- Context7 check: verify drag-and-drop library setup with Next.js and correct SSR/CSR handling; confirm Supabase RLS for multi-user isolation.

- Implementation: AI builds the UI, DB schema, and migrations. It adds indexing and a report page with simple charts.

- Debugging: deal with a stage update race condition; provide logs; AI adds optimistic UI updates with server reconciliation.

- Deployment: standard GitHub → Vercel flow with env vars replicated.

Example:
Pipeline board has columns: New, Contacted, Qualified, Won, Lost. Drag a card to update stage and log an activity (with timestamp and user ID).

Example:
Reports page shows "Leads by stage" and "Won ratio over time." AI wires a chart library and caches queries for performance.

Quality Assurance With Automated Testing

Manual testing doesn't scale. Use agents and Playwright MCP to simulate user flows:

- The QA agent navigates the live preview, signs up, logs in, triggers form submissions, and asserts expected UI and network responses.

- Failures trigger a bug report with steps, screenshots, and logs. A Fixer agent can auto-apply code changes and re-run tests.

Example:
QA agent test suite: "Sign up → Email validation error → Correct input → Successful login → Visit protected route → Logout → Access protected route (should redirect)."

Example:
Performance test: QA agent measures page load and LCP on /app; flags heavy image sizes; AI adds image optimization and lazy loading.

Continuous Deployment: GitHub + Vercel

Make shipping a non-event. Every push redeploys automatically.

Steps:

- Push to GitHub: the AI initializes a repo, commits your files, and pushes to your account.

- Connect to Vercel: import the repo. Vercel detects the framework and sets a build command.

- Configure environment variables: replicate every .env.local variable here. This one step breaks most first deployments if skipped.

- Deploy: Vercel builds and hosts your app at a public URL. Future commits trigger automatic rebuilds and deployments.

Example:
Bug fix: you report a 500 error with stack trace; AI patches the API handler; it commits "Fix: handle missing user session in API"; push → Vercel redeploys → issue resolved live within minutes.

Example:
Feature rollout: add "Export as PDF" to history. AI implements server-side PDF generation, updates UI, adds tests; you push; Vercel deploys; users have it instantly.

Security, Reliability, And Cost Control

Ship fast, but protect your users and your wallet.

Security:

- Keep secrets server-side. Public runtime only gets safe public keys.

- Enforce RLS in Supabase from day one.

- Validate inputs on server routes even if you already validate on the client.

- Use HTTPS-only cookies for sessions.

Reliability:

- Add basic error boundaries on UI and retry logic for flaky network calls.

- Log server errors; surface them in the Vercel dashboard for quick diagnosis.

- Use health checks and simple circuit breakers if integrating with unstable APIs.

Cost control:

- Prefer smaller/faster models for routine generation; reserve premium models for planning and user-facing high-stakes content.

- Cache AI responses where feasible (e.g., identical prompts from the same user within a time window).

- Rate limit API endpoints by user_id to prevent abuse.

Example:
For "The No Machine," add per-user daily generation limits and store hashes of prompts to de-duplicate identical requests.

Example:
For a research tool, route simple Q&A to a smaller model and escalate complex requests to a larger model only when confidence is low.

Best Practices You Shouldn't Skip

- Start simple: build the minimum version that delivers the core outcome. Complexity compounds bugs and costs.

- Always plan: turn on Plan Mode for every new project and every major feature. Architecture first, then code.

- Leverage context: request a Context7 review during planning and before merging big changes.

- Debug with evidence: share screenshots, exact errors, and full console logs. Vague inputs slow everything down.

- Document decisions in claude.md: state why you chose a pattern so future you (and the AI) can reason consistently.

- Commit small, test often: short feedback loops reduce rework.

Example:
Feature addition: "Add Google OAuth." You run Plan Mode for the feature, request a Context7 audit, implement, test with QA agent, then deploy.

Example:
Refactor: "Replace CSS modules with Tailwind." Plan Mode outlines a file-by-file migration strategy; QA agent checks UI regressions; you ship safely.

Applications And Opportunities

Entrepreneurship: launch MVPs without a dev team. Validate offers, gather feedback, and iterate rapidly.

Education: non-technical students can build real tools, not just mockups. Teach product thinking and software literacy.

Enterprise/internal tools: unlock frontline teams (ops, finance, marketing) to create their own dashboards and automations.

Rapid prototyping: turn Figma ideas into interactive apps for stakeholder demos and user testing.

Example:
A solo founder builds a waitlist landing page with an AI-powered concierge chatbot, logs sign-ups in Supabase, and tests pricing with a Stripe checkout in a weekend.

Example:
An HR team builds an internal mobility tool: employees browse open roles, match skills, and request referrals. Supabase stores profiles; Vercel hosts; QA agent tests flows.

Authoritative Concepts To Anchor Your Workflow

"The claude.md file provides guidance to the AI when working with code in the repository. You can think of this as the brain of the project." Treat it as the single source of truth for architecture, constraints, and goals.

"Opus plan mode uses a big, smart model for planning and strategy, and then uses a faster model to write the code. You get the best of both worlds without maxing out your usage limits." Use this to balance quality and cost.

"Proactively using a specific MCP server in an agent's prompt ensures the agent will use that tool consistently to verify its work." Bake Context7 into agent prompts by default.

"You can build apps that are thousands, if not tens of thousands, of lines of code that actually work because the system has task management and planning features built-in." Scale is no longer the bottleneck,clarity is.

Capstone Walkthrough: Build "The No Machine" From Zero To Live

1) Set up accounts: GitHub, Vercel, Supabase, Anthropic, Fal.ai. Install GitHub CLI. Ensure Claude Code is ready.

2) Initialize project: create folder the-no-machine. Open in Claude Code. Run /init. In claude.md, write the vision and constraints (frameworks, APIs, UI style).

3) Plan Mode: describe features precisely. Ask the AI to propose:

- Tech stack: Next.js, TypeScript, Tailwind

- Auth: Supabase

- Data: boundaries table (user_id, prompt, tone, response, image_url, created_at)

- Routes: /login, /app (prompt form + responses), /history (saved results)

- API modules: anthropicClient, falClient

- Rate limiting: per-user daily cap

4) Context7 refinement: "Review this plan with Context7 MCP." The AI updates patterns to the latest docs, confirms auth middleware, and env variable handling for Vercel.

5) Implement: approve the plan; the AI scaffolds files, writes components, connects APIs, and inserts helpful comments. It adds .env.local template keys.

6) Configure secrets: paste Supabase URL, anon, service_role; Anthropic and Fal.ai keys into .env.local. Restart dev server.

7) Database schema: the AI provides SQL; run it in Supabase SQL Editor; verify tables and RLS policies.

8) Test locally: submit prompts, verify three tones, confirm images, check database writes, sign out/in, and confirm history loads per user.

9) Debug loop (if needed): share console logs, screenshots, and repro steps. The AI patches issues and explains changes. Repeat until clean.

10) QA automation: use a QA agent with Playwright MCP to run auth and generation flows. Fix any fail cases.

11) Deploy: AI pushes to GitHub; you import the repo into Vercel; set env vars; deploy. Visit live URL and run end-to-end tests again.

12) Iterate: add a "tone intensity slider," "copy all," or "export history." For each feature, use Plan Mode, Context7 verification, agent tests, and small commits. Ship continuously.

Example:
Feature: "Favorites." Plan: add favorite boolean to boundaries table, toggle in UI, filter on /history. AI adds migration SQL, updates UI, writes tests; you deploy.

Example:
Feature: "Team sharing." Plan: add teams table, many-to-many user_team, and sharing policies. AI revises RLS, adds team switcher UI, and updates all queries to scope by team_id.

Common Pitfalls And How To Avoid Them

- Missing environment variables in Vercel: app works locally but fails live. Fix: replicate every key from .env.local exactly, then redeploy.

- Outdated code patterns: model suggests deprecated routing or auth flows. Fix: always request a Context7 review.

- Vague bug reports: "It doesn't work." Fix: provide exact errors, steps to reproduce, and full console logs.

- Overbuilding: too many features at once. Fix: ship the minimal version that delivers the core outcome, then layer on features with Plan Mode.

- Security mistakes: exposing service_role to the client. Fix: keep service_role server-only; rely on anon key client-side with strict RLS.

Example:
After deploy, 500 error on /api/generate. Vercel logs show missing ANTHROPIC_API_KEY. You add it to Vercel env, redeploy, and the route stabilizes.

Example:
Login loop on protected route. AI updates middleware to correctly check session presence and redirect. QA agent confirms the fix.

Scaling Your Process With Confidence

Once you have one app live, you can copy the process for anything else:

- MVPs: validate offers quickly.

- Internal tools: dashboards, trackers, approval flows.

- Content apps: AI-assisted writing, summarization, and research tools.

- Education apps: flashcards, adaptive quizzes, interactive tutorials.

Example:
"Idea Validator" app: accept a short pitch, generate a positioning statement, ICP outline, and landing page copy. Save versions per user in Supabase; deploy in a day.

Example:
"Ops Checklist" app: teams create checklists with required evidence before completing a task. Store artifacts in Supabase storage; audit logs for compliance.

What Mastery Looks Like

Mastery isn't memorizing syntax; it's mastering the loop:

- Describe outcomes precisely.

- Force planning first.

- Inject current docs and constraints.

- Delegate to the right agents.

- Debug with evidence.

- Deploy constantly.

When you operate this way, you'll ship reliable software faster than most teams. The system works because it removes guesswork and replaces it with repeatable, auditable steps guided by an intelligent, tireless assistant.

Example:
You estimate features in hours, not weeks: plan in minutes, build in an hour, test in minutes, deploy immediately.

Example:
You maintain quality under pressure: agents catch regressions, Context7 keeps patterns modern, and your claude.md decisions keep the whole codebase coherent.

Conclusion: The New Default For Building

You can build anything without writing code the traditional way. Your edge is clear thinking, strong prompts, and disciplined process. Use Plan Mode to architect before you execute. Use Context7 to keep the AI current and reliable. Use agents to test, refactor, and document while you sleep. Use the debugging loop to turn errors into fuel. Use GitHub and Vercel to make shipping continuous and safe.

The path is simple: Plan → Build → Configure → Debug → Deploy → Repeat. Your job is to hold the vision, define the outcome, and feed the system the right context. The AI will do the rest. Start with a small app. Get it live. Then iterate. You'll surprise yourself with how fast real products appear when you direct the right system with clarity and conviction.

Frequently Asked Questions

This FAQ is a practical reference for building real applications without writing code by hand. It answers the questions people actually ask before, during, and after creating a project with AI assistance. Use it to plan your build, avoid common mistakes, and ship faster with fewer surprises. Key points:
You'll find step-by-step guidance, tool choices, troubleshooting flows, and concrete examples for business-ready apps.

Section 1: Getting Started with Claude Code

What is Claude Code?

Claude Code is an AI-powered development environment that turns plain-language instructions into working software. It can plan your app, create files, write code, set up frameworks, and fix bugs while you steer. Key points:
It behaves like a diligent teammate that understands goals, reasons about tradeoffs, and executes tasks across the full stack. You stay focused on outcomes instead of syntax.
Example:
"Create a simple web app with login that generates three professional ways to decline a meeting and saves them for later." Claude proposes a stack (e.g., Next.js, Supabase, Vercel), scaffolds the project, writes the UI and API routes, configures auth, and iterates based on your feedback.

What foundational tools and accounts are required to build and deploy an application?

You'll need a few services (most have generous free tiers) to cover development, hosting, and AI features. Key points:
* Claude Code (Anthropic account) * GitHub + GitHub CLI * Vercel for deploys * Supabase for database and auth * AI API keys (e.g., Anthropic for text, Fal.ai for images).
Example:
A simple boundary-setting app: Claude Code to build, GitHub to store code, Vercel to host, Supabase for users/history, Anthropic for replies, Fal.ai for images.

How do I start a new project in Claude Code?

1) Create an empty folder for your app. 2) Open a terminal inside it. 3) Run the "claude" command to launch the interface in that directory. From there, you'll initialize, plan, and execute. Key points:
Start in an empty folder, use /init, then Plan Mode before writing code. This keeps the project coherent and reduces rework.
Example:
Folder "my-first-app" → open terminal → type "claude" → run /init → describe the app in Plan Mode → approve the plan → Claude scaffolds your files.

What is the /init command and why is it important?

/init creates a claude.md file that acts as your project's living brief. It captures goals, tech stack, file structure, and decisions, so Claude always has accurate context. Key points:
Run /init at the start of every new project. It aligns future actions and reduces drift as the codebase grows.
Example:
After /init, your claude.md will summarize "App purpose, stack: Next.js + TypeScript + Supabase, architecture decisions, file map." Claude updates it as features ship.

What is the claude.md file?

claude.md is your project's source of truth. Claude reads and updates it to stay aligned with your intent. It includes a description, tech choices, file structure, and key implementation notes. Key points:
Treat claude.md like a strategy doc,approve changes and keep it clean. It prevents "why did we choose this?" moments later.
Example:
"Use Supabase for auth and data; store user replies in 'responses' table; Next.js App Router; Tailwind for styling; image generation via Fal.ai."

Section 2: Core Development Concepts

What is Plan Mode and why is it recommended for new projects?

Plan Mode makes Claude propose a detailed plan before writing code. You toggle it with Shift + Tab. For big features, planning first prevents fragile or misaligned builds. Key points:
Use Plan Mode for new projects, major refactors, or integrations. Approve the plan, then let Claude implement.
Example:
"Add email login, history view, and image generation." Plan Mode outlines stack, routes, database tables, states, and tests,then executes once you confirm.

How should I describe my application idea to Claude Code?

Describe the core outcome, must-have features, and any preferred tools. Natural language is enough. Prioritize clarity over jargon. Key points:
Include: what it does, key user flows, AI providers, and constraints (budget, speed, design style).
Example:
"Build a 'No Machine' web app. Users paste a scenario, get three 'no' responses (light/medium/firm) via Anthropic, plus a humorous image via Fal.ai. Use Supabase for login and to save results."

What is the benefit of setting the model to Opus Plan Mode?

Opus Plan Mode uses a strategic model for planning and a faster model for execution. You get high-quality architecture with efficient code generation. Key points:
Better plans, fewer mistakes, lower cost than using a single top-tier model for everything.
Example:
Use /model to set Opus for planning the app, Sonnet for writing routes, components, and tests.

Section 3: Advanced Tools , MCPs and Agents

What are MCPs (Mentionable Context Providers)?

MCPs feed Claude verified, current information and tooling. They reduce outdated assumptions and improve accuracy. Popular MCPs include Context 7 (docs access) and Playwright (browser automation for testing). Key points:
Think of MCPs as smart add-ons that ground or extend Claude's capabilities.
Example:
"Review our plan with the context7 mcp" makes Claude cross-check Next.js, Vercel, and Supabase steps against official docs before coding.

How do I install and use an MCP like Context 7?

Install via a single terminal command from the MCP's site, restart Claude Code, then call it in your prompts. You can also bake MCP usage into an agent's system prompt. Key points:
Use MCPs to verify plans, generate correct configs, and reference current APIs.
Example:
"Use the context7 mcp to confirm environment variable names for Supabase on Vercel, then proceed."

What are Agents in Claude Code?

Agents are specialized Claude instances with their own role, tools, and model. Create agents for QA, docs, frontend, research, or migrations to keep tasks focused. Key points:
Specialization improves outcomes. Each agent keeps independent context and passes outputs to the next step.
Example:
A "QA Tester" agent tries to break auth flows; a "Docs Writer" agent updates claude.md and README with new endpoints and settings.

How do agents improve the development workflow?

They enable specialization, task chaining, and parallel execution. You can run multiple focused agents, then synthesize their outputs. Key points:
Less context overload, faster iteration, clearer accountability.
Example:
"Frontend Developer builds the login page → QA Tester simulates failed logins → Docs Writer updates setup steps."

Section 4: Practical Implementation and Troubleshooting

What are environment variables and how are they managed?

Environment variables store secrets and config values so they're not hardcoded. Locally, use .env.local; on Vercel, add them in Project → Settings → Environment Variables. Key points:
Never commit secrets to Git. Keep local and production values in sync. Restart the dev server after changes.
Example:
SUPABASE_URL, SUPABASE_ANON_KEY, ANTHROPIC_API_KEY, FAL_KEY in .env.local; mirror them in Vercel project settings.

What is the recommended workflow for debugging an application?

Be specific. Share what you did, what happened, exact error messages, and console logs. Screenshots help for layout issues. Key points:
Evidence speeds diagnosis. The trio,steps to reproduce, error text, and full console log,lets Claude pinpoint root causes quickly.
Example:
"Clicked 'Generate', got 500. Console shows 'Invalid API key'. Here's the full log." Claude will trace the failing call, update code or env vars, and retest.

How do I set up a database schema with Supabase?

Ask Claude to generate SQL for your tables, paste it into Supabase's SQL Editor, and run. Keep schemas versioned and documented. Key points:
Start with minimal tables, then iterate with migrations. Protect data with Row Level Security (RLS).
Example:
A "responses" table with user_id, scenario, three_text_variants, image_urls, created_at; RLS policy restricts rows to the owning user.

Section 5: Deployment

What is the process for deploying an application to the internet?

1) Push code to GitHub. 2) Import the repo into Vercel. 3) Add environment variables. 4) Deploy. Future commits auto-deploy. Key points:
Keep main stable; use branches for features. Treat Vercel as your production pipeline.
Example:
"commit to git" from Claude → GitHub updates → Vercel builds Next.js → live at yourapp.vercel.app.

My Vercel deployment shows an error. What should I do?

Open the Vercel deployment logs, copy the exact error, and share it with Claude along with recent changes. Fix locally, commit, and push to trigger a new deploy. Key points:
Many failures are missing env vars, wrong Node version, or build script issues. Logs are your best friend.
Example:
Error "Environment variable not found: SUPABASE_URL." Add it to Vercel settings, redeploy, confirm the fix.

Section 6: Additional FAQs for No-Code Builders

Do I need coding experience to build with Claude Code?

No. You bring the problem, requirements, and feedback loop; Claude writes and edits the code. You'll learn concepts as you go. Key points:
Focus on clear prompts, small iterations, and testing real user flows. Over time, patterns will become intuitive.
Example:
"Add a history page showing past responses." Claude scaffolds a route, queries Supabase, renders a list, and styles it. You review and refine the UX.

What kinds of apps can I realistically build without code?

Launch-ready web apps: internal tools, content assistants, lead qualifiers, lightweight CRMs, dashboards, marketing sites with AI features, and simple marketplaces. Key points:
Aim for clear value, not endless features. Integrate APIs where it makes sense.
Example:
A client-intake tool: form → enrich with Anthropic → store in Supabase → notify sales via email → view pipeline on a dashboard.

How do I choose features for my first version (MVP)?

Pick the smallest set that proves value. Defer anything you can until after users try it. Key points:
One core flow, one persona, one success metric. Add polish after signal.
Example:
"No Machine" MVP: login, input scenario, generate 3 responses + 1 image, save history. Defer sharing, tags, and bulk export to later.

How do I write better prompts for building features?

State the goal, constraints, and acceptance criteria. Reference files, data models, and UI states. Ask for a plan before code for complex changes. Key points:
Avoid vague verbs; specify outcomes, edge cases, and performance targets.
Example:
"Create a POST /api/generate route. Input: scenario text. Output: {light, medium, firm, imageUrl}. Validate empty input; handle API timeouts with retries; log errors to console with request ID."

What are common misconceptions about AI-assisted development?

Myth: "AI builds perfect code in one shot." Reality: it accelerates iteration; you still direct strategy and review outputs. Myth: "You must understand every line." Reality: understand flows and risks; inspect key parts and tests. Key points:
Think product manager, not typist. The quality of your guidance and feedback determines outcomes.
Example:
Ask for tests or QA steps: "Write Playwright tests for login, invalid input on generate, and history pagination."

What's the difference between GitHub and Vercel?

GitHub stores and versions your code; Vercel builds and hosts your app. Together, they enable continuous deployment. Key points:
Git is change history; Vercel is runtime. Keep main stable; use PRs to review.
Example:
Merge a PR on GitHub → Vercel auto-builds → new version goes live with a unique URL to verify before promoting.

How do I keep secrets safe (and avoid leaking service_role keys)?

Never commit secrets. Store them in .env.local and Vercel's environment settings. Avoid using service_role on the client; it's for secure server-side tasks only. Key points:
Use Supabase anon key in the browser; keep service_role to server-only API routes.
Example:
Server route performs admin inserts with service_role; the client calls that route, not Supabase directly.

How do I manage API costs and rate limits?

Control inputs, cache results, batch operations, and set per-user quotas. Add retries with backoff for transient failures. Key points:
Track usage by user and feature. Provide fallbacks for degraded modes.
Example:
Cache Anthropic outputs per scenario hash; if a user asks the same scenario, serve from cache to save tokens.

How can I test my app automatically?

Use Playwright (via MCP) for end-to-end flows: login, submit forms, see results, handle errors. Add unit tests for utility functions. Key points:
Test the critical path first; expand as features grow.
Example:
A Playwright test signs up a user, generates responses, checks three outputs, and verifies they appear in history after refresh.

What branching strategy should I use with Git?

Keep main production-ready. Create feature branches, open pull requests, and merge after review and deployment previews pass. Key points:
Smaller PRs ship faster and are easier to roll back.
Example:
feature/add-history-page → PR → Vercel preview URL → manual QA → merge to main → auto-deploy.

How do I secure data with Supabase Auth and Row Level Security (RLS)?

Enable RLS and write policies that restrict rows to the authenticated user. Use server-side checks for sensitive actions. Key points:
Test with a non-admin user. Log policy failures to debug.
Example:
"Users can select rows where user_id = auth.uid()" policy on the responses table prevents cross-account access.

How do I add analytics and error tracking?

Instrument key events (signup, generate, save) and add error tracking to catch failures in production. Tools like PostHog (analytics) and Sentry (errors) integrate quickly. Key points:
Track outcomes, not vanity metrics. Alert on error spikes.
Example:
Log "generate_clicked" with scenario length; capture API timeout errors with request IDs for rapid triage.

How do I add payments and handle webhooks on Vercel?

Use a provider like Stripe. Create server-only webhook routes to verify signatures and update user status in your database. Key points:
Never process payments in the browser. Secure your webhook secrets in Vercel.
Example:
/api/stripe/webhook verifies signature → on "checkout.session.completed" set plan=pro in Supabase.