Claude Code BR
The free guide to building real software with Claude Code and AI agents — from concept to deploy.
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Join the free groupThe Logic Developer's Philosophy
The fundamental premise: you don't need to know how to implement it. You need to know what you want to happen. AI takes care of the rest.
Solution Logic
You know the flow: input A comes in, transformation X happens, result B comes out. That is what matters.
Technical Implementation
Which library to use? Which API to call? Which syntax? AI works that out with deep research.
Result
You become an Architect of Intent: you describe the project with surgical precision and AI builds it.
Infrastructure: Your AI Tools
Before you start, you need to understand the tools. The context window sets the limit of what the AI can "keep in its head". Claude Code with a Max subscription is our main recommendation.
| Tool | Plan | Context | Highlight |
|---|---|---|---|
| Claude Code (Max) | Max subscription (5x US$ 100/month · 20x US$ 200/month) | Up to 1M tokens (Opus 5.5, Sonnet 5.5, Fable) | Full terminal agent, file reading/writing, command execution, deep reasoning |
| Claude Code (Pro) | Pro subscription (US$ 20/month) | Up to 1M tokens with the current models | Same agent, lower usage limits |
| Gemini (Google AI Pro) | Google AI Pro (US$ 19.99/month · R$ 96.99 in Brazil) | 1 million tokens | Deep Research, Google Workspace integration |
| Gemini Free | Free | 32,000 tokens | Deep Research with a variable limit (may be unavailable at peak hours), good for simple projects |
Prices, limits and models checked on September 30, 2026 on each company's official pages.
CLAUDE.md. For deep research and for generating the Master Document, Gemini Deep Research remains an excellent complementary option.
The Genesis: Deep Research
This is the most important part of the whole process. You will create the "Master Document" (the "Project Bible") — a technical report of 15 to 25 pages that details every component of your application.
What the deep research prompt must contain:
Total Intent
A detailed explanation of the end goal. What does the software do? For whom? What problem does it solve? What value does it deliver?
Exploring Possibilities
A direct order for the AI to explore ALL the most effective technologies, languages and approaches on the market today.
Selecting APIs and Libraries
A request to identify the best APIs for each function, ensuring modern and stable technology.
Workflow Deduction
An instruction for the AI to fill in the gaps in the workflow that you may not have seen.
Download format: Markdown > PDF
Always download as .md (Markdown) or .txt. Markdown preserves the structure of headings, lists and code blocks in "raw" form, which makes it easier for AI agents to read and to bring into IDEs such as VS Code.
Prompts Ready to Copy and Use
These are tested templates for each stage of the process. Copy them, adapt the parts in [brackets] and paste them into Claude Code or Gemini Deep Research.
Prompt 1 — Deep Research (Master Document)
You are a Senior Software Engineer and Cloud Architecture Specialist. Your task is to create an exhaustive technical document of 15 to 25 pages about the following project: ## PROJECT: [Name of your project] ### TOTAL INTENT: I want to build [describe in detail what the software does, for whom, and what problem it solves]. The end user will [describe the complete user flow: how they get in, what they do, what they see, what they receive]. ### DATA AND FLOW: - The system receives [type of input data] - It processes it using [describe the desired logic/transformation] - It produces as output [expected result] - The data is stored in [where/how] ### MANDATORY REQUIREMENTS: 1. [Functional requirement 1] 2. [Functional requirement 2] 3. [Functional requirement 3] 4. [web/mobile/desktop] interface with a [modern/minimalist/etc] design 5. User authentication [if applicable] ### RESEARCH INSTRUCTIONS: - Explore ALL the most effective and modern technologies, frameworks and programming languages on the market today (2025-2026) for each component - Identify the best APIs for each specific function, comparing at least 3 options for each one - Create a DECISION MATRIX comparing each option in terms of: cost, latency, ease of implementation, documentation and stability - Deduce and fill in any gaps in my workflow that I may not have seen - Include security, scalability and performance considerations - Use clear Markdown headings (## and ###) and comparison tables, without exception - Cite all the sources consulted ### OUTPUT FORMAT: A structured Markdown document with: 1. Executive Summary 2. System Architecture (with a text diagram) 3. Recommended Tech Stack (with justifications) 4. External APIs and Services (with a decision matrix) 5. Data Model 6. Screen/Interface Flow 7. Implementation Plan (step by step) 8. Infrastructure Cost Estimate 9. Risks and Mitigations 10. References
Prompt 2 — Refine the Research Plan (before confirming)
Before starting the research, edit the plan to also include: - A comparison with the following competitors: [name1, name2, name3] - A specific security analysis for [LGPD/GDPR/OAuth authentication] - A compatibility check with [specific platform/service] - An exploration of free or low-cost deploy options (Vercel, Railway, Supabase, etc.) - A section on automated tests and CI/CD
Prompt 3 — Context Injection into Claude Code / IDE
Read the file PROJETO.md in this folder. This is the complete specification document for my project. Before you start coding, explain to me: 1. How this project will work in practice (overall architecture) 2. What the initial implementation steps are 3. Which dependencies we will need to install 4. What folder structure you recommend 5. What the most critical/complex points of the project are After explaining, wait for my confirmation before you start generating code.
Prompt 4 — Break the Plan into Sprints (with a Theoretical and Practical Deep Dive)
Before we start coding, let's plan in incremental SPRINTS. Take PROJETO.md and break the implementation into incremental sprints, in the order in which they should be executed. Each sprint is a coherent slice of the product that can be built and validated independently. For EACH sprint, generate: 1. **Sprint goal** — which CONCRETE, demonstrable delivery of value comes out of this sprint (a working feature, a live endpoint, a testable flow). 2. **Theoretical deep dive** — which concepts I need to understand BEFORE coding. For each concept, write 1-2 paragraphs explaining what it is, why it matters in this context and which common mistake happens when it is ignored. 3. **Code deep dive** — a MINIMAL working example of the sprint's key concept. A snippet that really runs, not pseudocode. Comment every non-obvious line. 4. **Technical tasks** — a numbered list of what will be built, in order of dependency. Each task is a small, self-contained unit. 5. **Definition of Done** — a verifiable checklist: tests that must pass, behavior that must work, expected output. No ambiguity. 6. **Risks / gotchas** — what usually goes wrong at this specific stage and how to avoid it (e.g. race conditions, API limits, edge cases). 7. **Next step** — what comes next and why THIS sprint needs to be done first. Save the result to SPRINTS.md in the project root. Do NOT start coding yet — I want to review the plan sprint by sprint, adjust whatever makes sense, and then execute one at a time. When executing each sprint, update SPRINTS.md marking the sprint as done and record any learnings or deviations from the original plan.
Prompt 5 — Start the Assisted Build
Great, I understand the architecture. Now let's start building. Follow exactly the Implementation Plan in the PROJETO.md document. Start with Step 1: [describe the first step of the plan]. Rules: - Create the complete folder structure first - Install all the necessary dependencies - Generate the configuration files (package.json, .env.example, etc.) - Implement the main logic following the document - Add explanatory comments to the code so that I can learn - After each completed step, tell me what was done and what the next step is - If you find any ambiguity in the document, ask before assuming
Prompt 6 — Create a Persistent Context File (CLAUDE.md)
/init. It reads the project and generates a starter CLAUDE.md. Then use /memory or the prompt below to complete it with decisions, business rules and the current status.Create a file called CLAUDE.md in the project root with the following information to keep consistency between sessions: # Project Context: [Name] ## Architectural Decisions - [List the decisions already made] ## Defined Stack - Frontend: [technology] - Backend: [technology] - Database: [technology] - Deploy: [platform] ## Business Rules - [Rule 1] - [Rule 2] ## Constraints - [Constraint 1] - [Constraint 2] ## Current Status - [x] Completed phase - [ ] Phase in progress - [ ] Next phase ## Important Notes - [Anything the AI must not forget between sessions]
Prompt 7 — Debugging
The following error appeared when running [command]: ``` [Paste the complete error message here] ``` Context: - Affected file: [file name] - What I was trying to do: [describe the action] - Last piece of code changed: [describe or paste] Analyze the error, explain the root cause in plain language, and fix the code. Show the before and after of the fix.
Prompt 8 — Turn the Document into a Web Page
Convert the content of the PROJETO.md file into a modern, professional web page using HTML + Tailwind CSS. Requirements: - Dark, modern design - Responsive (mobile-first) - Side navigation or navigation by sections - Styled tables for comparisons - Code blocks with syntax highlighting - Optimized print mode via CSS @media print - A single self-contained HTML file (inline CSS or via CDN)
Implementation with AI Agents
With the Master Document in hand, you now use Claude Code to turn the text into real code.
Step-by-Step Workflow
Set Up the Environment
Create a local folder for the project. Put the specification file (PROJETO.md) inside it. Open the folder in your IDE or terminal.
Inject Context
Use Prompt 3 (above) so that the agent reads and understands the complete document BEFORE it starts coding.
RequiredActive Learning
The agent explains the architecture. You learn while the project is being built. Ask questions. Understand every decision.
Learning phaseAssisted Build
The agent generates the folder structure, the configuration files and the main logic, installs dependencies and runs tests. All under your supervision.
AutomatedPersistent Context File
Run /init to generate CLAUDE.md in the project root and complete it with Prompt 6. This ensures the AI never loses context between sessions.
Agent Tools
The agent has access to powerful tools that eliminate "copy-and-paste" (these are the official names in Claude Code):
Read
Reads any file in the project to understand the context.
Write and Edit
Write creates or rewrites a file; Edit changes only the right part. You don't copy anything.
Bash
Runs commands in the terminal: installing packages, running tests, starting servers.
Plan mode and subagents
Two Claude Code features that make a difference from the very first project:
Plan mode
Press Shift+Tab until the status bar shows plan mode on (or type /plan). Claude reads the project and proposes a plan, but does not change any file until you approve. Use it before every big change.
Subagents
Ask: "use a subagent to investigate how authentication works". It reads the files in its own context and returns only the summary, and your main conversation stays clean. To have specialized agents (reviewer, tester), ask Claude to create them or write them in .claude/agents/.
Alternatives: Gemini Canvas and More
Besides Claude Code, there are visual alternatives such as Gemini Canvas and other tools that can complement your workflow.
Web Apps and Dashboards
▼Canvas generates working prototypes of HTML/React interfaces instantly. Describe what you want and see the preview in the side panel. Ideal for validating ideas quickly.
Conversational Debugging
▼If the generated code has bugs, you can debug right in the Canvas interface: click the snippet, ask for an explanation and an instant fix. No rewriting prompts.
Content Transformation
▼Turn the Deep Research report into a web page, an infographic, an interactive quiz or an Audio Overview. Perfect for presenting the project to clients or partners.
Overview of AI Coding Tools
| Tool | Strength | Best For |
|---|---|---|
| Claude Code | Full terminal agent, deep reasoning, file reading/writing, persistent CLAUDE.md | Main tool — complete projects from zero to deploy |
| Cursor (with a Claude model) | IDE with a built-in agent, predictive autocomplete, multi-file edit. Select the Claude models (Opus 5.5 or Sonnet 5.5) to keep the same brain as Claude Code. Pro from US$ 20/month. | Those who prefer a visual editor to the bare terminal |
| Antigravity CLI (Google, free, with a caveat) | It replaced the free tier of Gemini CLI on June 18, 2026, with basic weekly limits. Warning: on the free tier, Google may use what you send (your prompts and the files the agent reads) to improve products and models, and human reviewers may read it. Do not use it on proprietary/sensitive code. | Free audits and research on open-source projects |
Essential APIs for 2025-2026
You don't need to know how to implement a computer vision algorithm. You need to know that the API exists and ask the AI to integrate it.
| Category | Recommended API | Use |
|---|---|---|
| Language | Claude Opus 5.5 or Fable 5.1 (Anthropic) / GPT-6 Astra (OpenAI) / Gemini 3.1 Pro or 3.8 Flash (Google) | Reasoning, text, chatbots |
| Vision | Gemini Vision / Google Cloud Vision / Clarifai | OCR, analysis of images, video and documents |
| Audio | ElevenLabs / Whisper (OpenAI) / AssemblyAI | Realistic voices, transcription |
| Data | Dumpling AI / Apify | Web scraping, data extraction |
| Images | Stability AI / Midjourney API | Visual assets and designs |
| Backend | ExpressJS / FastAPI | Lightweight servers to connect APIs |
Security: Protecting Your Software
Security is not optional. Every piece of software that goes to production needs to consider these fundamentals from the start.
Authentication
Use JWT (JSON Web Tokens) for stateless APIs, OAuth 2.0 for social login (Google, GitHub), and bcrypt for password hashing. Never store passwords in plain text.
HTTPS / SSL
All traffic must be encrypted. Use Let's Encrypt for free certificates. Platforms such as Vercel and Railway already include automatic SSL.
Environment Variables (.env)
API keys, secrets and credentials go in .env, NEVER in the code. Add .env to .gitignore. Use .env.example as a template.
Rate Limiting
Limit requests per IP/user to prevent abuse. Use express-rate-limit (Node) or equivalent middleware. Protect authentication endpoints in particular.
Input Validation
Sanitize ALL user input. Prevent XSS (Cross-Site Scripting) with HTML escaping, and SQL Injection with parameterized queries or ORMs such as Prisma/Drizzle.
LGPD / Compliance
If you collect data from Brazilian users (LGPD is Brazil's data protection law): have a privacy policy, allow data deletion, obtain explicit consent. Use cookies only with opt-in.
Analyze the current project and implement the following security measures: 1. JWT authentication with refresh tokens 2. Password hashing with bcrypt (salt rounds: 12) 3. Rate limiting middleware (100 req/15min per IP) 4. Input validation with Zod on all routes 5. HTML sanitization to prevent XSS 6. Parameterized queries (never concatenate SQL) 7. Helmet.js for HTTP security headers 8. CORS configured only for allowed domains 9. .env.example file with all the required variables 10. Logging middleware for auditing Explain each measure implemented.
Deploy: From Local to the World
Your software works locally. Now it is time to put it online for real users to access.
Deploy Options
| Platform | Best For | Cost | Difficulty |
|---|---|---|---|
| Vercel | Frontend, Next.js, static sites | Free (hobby, personal use) | Easy |
| Railway | Backend, databases, full-stack | $5/month+ | Easy |
| AWS (EC2/ECS) | Projects at scale, full control | Variable | Advanced |
| VPS (Hetzner/DigitalOcean) | Full control, predictable cost | $4-20/month | Medium |
Docker Basics
Docker packages your app with all its dependencies, ensuring it works the same anywhere.
# Basic Dockerfile for Node.js FROM node:24-alpine WORKDIR /app COPY package*.json ./ RUN npm ci --omit=dev COPY . . EXPOSE 3000 CMD ["node", "dist/index.js"] # Essential commands: # docker build -t meu-app . # docker run -p 3000:3000 meu-app
CI/CD with GitHub Actions
Automate tests and deploy on every push. The code goes to production automatically when the tests pass.
# .github/workflows/deploy.yml
name: Deploy
on:
push:
branches: [main]
jobs:
test-and-deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/setup-node@v7
with:
node-version: 24
- run: npm ci
- run: npm test
- run: npm run build
# Add your platform's deploy step here
Domain + SSL
Register a domain (Namecheap, Cloudflare). Point the DNS to your deploy platform. SSL is automatic on most modern platforms.
Monitoring
Use UptimeRobot (free) for downtime alerts. Sentry for error tracking. Vercel Analytics or Plausible for usage metrics.
From Concept to Final Product
The complete flow in a single view:
Have a Clear Idea
Define the problem, the audience, the user flow, the data involved. It doesn't need to be technical — it needs to be precise.
Deep Research (a few minutes)
Use Prompt 1 in Gemini Deep Research or Claude. Generate the Master Document of 15-25 pages. Download it as .md.
Refine the Document
If necessary, use Prompt 2 to adjust the plan BEFORE confirming. Add competitors, security requirements, etc.
Inject into the Coding Agent
Put PROJETO.md in the project folder. Use Prompt 3 so that the agent understands everything. Next, Prompt 4 to break the execution into sprints (with theory + code per stage). Then Prompt 5 to start building the first sprint.
Build and Learn
The agent generates code, installs dependencies, runs tests. You supervise, learn and ask questions.
Debug and Iterate
Use Prompt 7 for debugging. Use Canvas or the agent to fix things. Keep CLAUDE.md and SPRINTS.md up to date.
Document and Present
Use Prompt 8 to turn the document into a professional web page. Or use MkDocs/Docusaurus for navigable documentation.
Final Presentation Options
PDFMaker
Paste the Markdown and apply professional themes with typography and syntax highlighting.
Tailwind CSS
Ask the agent to convert the report into a modern web page (like this one!).
MkDocs / Docusaurus
Turn the 25 pages into a navigable documentation portal with built-in search and dark mode.
Final Checklist
Click each item as you complete it. Your progress is saved locally.
- I defined the project idea clearly (problem, audience, flow)
- I chose the right tool (Claude Code Max, Google AI Pro, etc.)
- I wrote the deep research prompt with persona + total intent
- I refined the research plan before confirming
- I generated the Master Document (15-25 pages) and downloaded it as .md
- I created the project folder and put PROJETO.md inside it
- I injected the context into the coding agent (Prompt 3)
- I understood the architecture explained by the agent before coding
- I broke the project into sprints with theory and code (Prompt 4) and started the assisted build (Prompt 5)
- I created the persistent context file (CLAUDE.md)
- I debugged errors using Prompt 7 or Canvas
- Project working and tested
- Final documentation generated (HTML/PDF/MkDocs)
Golden Rules of Prompt Engineering
1. Define a Persona
"You are a Senior Engineer and Cloud Architecture Specialist..."
2. Demand a Format
Markdown headings, comparison tables, code blocks. Never accept loose text.
3. Iterate Before Confirming
If the research plan looks incomplete, edit it. Add names, criteria, constraints.
4. Be Specific
Don't say "make an app". Say exactly what goes in, what comes out, who uses it and why.
Those who master the art of describing workflows end to end and use giant-context tools produce in days what used to take months.
Complete Extended Course
Learn to automate entire business functions with AI — customer service, sales, finance, legal, operations. The same kind of system large companies pay R$ 50K to R$ 500K a month (Brazilian reais) to have.
Axis has not launched yet: it is counting down to launch. When it ships, everyone in the advanced course + VIP group gets into the beta. And in the VIP group, every question is answered within 24 hours.
What you will build and sell:
Automating Business Functions
Replace entire manual processes with AI agents — 24/7 customer service, lead qualification, proposal generation, billing, onboarding. Real cases: clinics, law firms, e-commerce, real estate agencies.
Custom AI Agents
Build "digital employees" that read emails, answer customers on WhatsApp, schedule meetings, generate reports and make decisions within business rules. It costs cents per run and works 24 hours a day.
SaaS from Zero to First Customer
From idea to MRR — niche discovery, validation, an MVP in weeks, first paying customers, B2B pricing (R$ 2K-50K/month). 3 complete projects with Claude Code building 80% of the code.
Integration with Real Tools
Connect AI to everything companies already use — WhatsApp Business, Gmail/Outlook, Google Sheets/Excel, CRMs (HubSpot, Pipedrive), ERPs, Notion, Slack. Learn to "fit" AI into the existing workflow without tearing anything up.
Data Analysis and Decisions with AI
Automated BI, dashboards that explain themselves, management reports generated in natural language, analysis of support tickets, churn, sales. CEOs pay well for insight, not for code.
Selling to Companies (B2B)
How to price projects from R$ 30K to R$ 500K, close recurring contracts, position yourself as a consultant and not a freelancer, and prospect companies that HAVE money and real pain. From technical delivery to signed contract.
Full Stack (no fluff)
Git, terminal, APIs, databases, deploy, security, DevOps — the whole technical foundation, condensed and taught as the projects demand it. You learn by doing, not by memorizing.
Advanced Claude Code
CLAUDE.md in production, hooks, subagents, multi-repo, MCP servers, headless in CI/CD. The level that separates those who play with AI from those who deliver systems that run in production.