Vibe Coding

What is Vibe Coding?

Vibe coding is a collaborative workflow where humans and AI assistants work together seamlessly, each playing to their strengths. Instead of treating AI as just a code completion tool, vibe coding recognizes AI as a first-class team member with its own workspace, documentation practices, and self-review processes.

Traditional AI Usage:

AI generates code on demand
Human copies/pastes results
Context lost between sessions
No documentation of AI decisions
Synchronous interaction required

Vibe Coding:

AI plans, implements, and documents
Shared workspace in task files
Complete context recovery
AI self-documents decisions
Async-first collaboration

Core Principles

1️⃣ AI as a Partner, Not a Tool

AI assistants are treated as collaborative partners with their own dedicated spaces for planning, note-taking, documentation, and self-review. They're not just code generators - they're team members.

2️⃣ Explicit is Better than Implicit

Every decision, implementation detail, and thought process is documented explicitly. Nothing is left to memory or assumed knowledge. The task file becomes the complete project history.

3️⃣ Async-First Collaboration

Human and AI don't need to work simultaneously. AI can work through tasks autonomously, documenting progress and questions. Humans review and provide guidance when available.

4️⃣ Trust but Verify

AI provides comprehensive self-review and quality checks, but humans validate critical decisions. The AI-review space makes verification transparent and efficient.

The Vibe Coding Workflow

Phase 1: Human Defines the Goal

The human creates a task with clear objectives and acceptance criteria:

$ backmark task create "Implement user authentication" \

-d "JWT-based auth with refresh tokens" \

-a "Claude" \

-p high

Human Responsibilities: Define what needs to be done, why it matters, and what success looks like.

Phase 2: AI Plans the Implementation

AI analyzes the task and creates a detailed implementation plan:

$ backmark task ai-plan 1 "## Implementation Steps 1. Install jsonwebtoken library 2. Create auth middleware 3. Implement login/logout endpoints 4. Add refresh token mechanism 5. Write integration tests ## Files to Modify - src/middleware/auth.js (new) - src/routes/auth.js (new) - package.json ## Dependencies - jsonwebtoken - bcrypt"

✓ Added AI plan to task #1

AI Responsibilities: Break down the task, identify dependencies, plan architecture.

Phase 3: AI Implements with Real-Time Notes

As AI works, it documents progress, decisions, and issues:

$ backmark task ai-note 1 "**2024-11-06 14:30** Installed jsonwebtoken and bcrypt. Created auth middleware with JWT verification. **2024-11-06 15:00** Implemented login endpoint. Decision: Using 15min access tokens + 7day refresh tokens. Reasoning: Balance between security and UX. **2024-11-06 15:30** Issue: bcrypt version conflict with existing dependencies. Solution: Upgraded to bcrypt@5.1.0, tests still passing."

✓ Added AI note to task #1

AI Responsibilities: Implement, log decisions, document issues and solutions.

Phase 4: AI Documents the Implementation

AI creates comprehensive documentation for future reference:

$ backmark task ai-doc 1 "## Authentication System ### Usage \`\`\`javascript POST /api/auth/login Body: { email, password } Response: { accessToken, refreshToken } \`\`\` ### Configuration Set JWT_SECRET in .env file. ### Token Lifetimes - Access: 15 minutes - Refresh: 7 days"

✓ Added AI documentation to task #1

AI Responsibilities: Create API docs, usage examples, configuration guides.

Phase 5: AI Performs Self-Review

Before marking complete, AI reviews its own work:

$ backmark task ai-review 1 "## Self Review ### Completed Tasks ✅ JWT authentication implemented ✅ Refresh token mechanism working ✅ Integration tests passing (12/12) ✅ Documentation complete ### Quality Metrics - Test coverage: 94% - Response time: ~50ms average - Security: OWASP guidelines followed ### Questions for Human Review 1. Should we add rate limiting to login endpoint? 2. Token lifetime acceptable or adjust?"

✓ Added AI review to task #1

AI Responsibilities: Validate completeness, report metrics, flag concerns.

Phase 6: Human Validates and Closes

Human reviews AI's work and documentation, then approves:

$ backmark task view 1 --ai-all

[Reviews complete task with all AI sections]

$ backmark task edit 1 --status Done

✓ Task #1 marked as Done

Human Responsibilities: Validate outcomes, answer AI questions, approve completion.

Why Vibe Coding Works

🧠 Complete Context

Every decision, implementation detail, and thought process is preserved in the task file. No more "why did we do it this way?" moments six months later.

🔄 Session Recovery

AI can pick up exactly where it left off, even days later. The AI spaces provide complete context for continuation.

📚 Knowledge Transfer

New team members (human or AI) can understand project history by reading task files. Every decision is documented.

⚡ Async Productivity

Humans and AI don't need to be online simultaneously. AI works autonomously, human reviews when convenient.

🎯 Clear Ownership

AI sections vs human sections create clear ownership boundaries. Each party knows their responsibilities.

✅ Built-in QA

AI self-review catches issues early. Human validation ensures quality. Two layers of review for better outcomes.

Real-World Example

Scenario: Adding Search Feature

# Monday 9am - Human creates task

$ backmark task create "Add fuzzy search to task list" -a Claude

# Monday 9:15am - AI creates plan

$ backmark task ai-plan 42 "Use Fuse.js library..."

# Monday 10am-2pm - AI implements (human offline)

$ backmark task ai-note 42 "Installed Fuse.js..."

# Tuesday 9am - Human reviews (fresh context from task file)

$ backmark task view 42 --ai-all

[Sees complete implementation history, decisions, docs]

# Tuesday 9:30am - Human approves

$ backmark task edit 42 --status Done

Result: Feature implemented with full documentation, decision log, and self-review, all without requiring simultaneous human-AI interaction.

Benefits of Vibe Coding

For Developers:

✅ Less context switching
✅ Better work-life balance (true async)
✅ Documented decision history
✅ AI handles tedious tasks
✅ Focus on high-value work

For Teams:

✅ Knowledge sharing by default
✅ Onboarding made easy
✅ Consistent documentation
✅ Audit trail for compliance
✅ Improved code quality

For AI Assistants:

✅ Clear workspace and boundaries
✅ Context preservation
✅ Autonomy to work deeply
✅ Self-review capabilities
✅ Meaningful contribution

For Projects:

✅ Complete project history
✅ Better maintainability
✅ Reduced technical debt
✅ Faster feature delivery
✅ Higher code quality