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Why AI coding assistants drive you crazy

You start excited about building a feature, but watch the AI slowly lose track of what you're building...

Chaotic AI Planning

Current Reality
1
"I want user auth"

AI tries to build everything at once

2
"20 tasks in one response"

Database, routes, middleware, frontend, tests...

3
"Task 8 forgot task 3"

Conflicts with earlier code, overwrites files

4
"No documentation trail"

Can't track what was built or why

5
"Small change breaks everything"

Touching one piece destroys the whole flow

6
"'Working' solution with mocked data"

Fake users, dummy endpoints, no real testing

7
"Burns through your Cursor usage quota"

Wasteful token usage from constant re-explaining

Burning tokens on trial-and-error Hours wasted

Smart AI Planning

Context Engineering
1
"I want user auth"

🎯 Analyzes your setup, creates structured plan

2
"Breaks into clear phases"

Database → Routes → Middleware → Frontend → Tests

3
"Each task references previous"

Perfect continuity, no conflicts or overwrites

4
"Complete documentation trail"

PRD → Blueprint → Tasks with full context

5
"Changes are surgical"

Modify one piece, everything else stays intact

6
"Production-ready implementation"

Real data, proper validation, comprehensive tests

7
"Maximizes your existing Cursor subscription"

By reducing token waste and repeated explanations

Optimized token usage First-time accuracy

How Context Engineering Works

Add our Context Engineer MCP in Cursor and ask to plan your next feature. As simple as that.

Codebase Analysis

🏗️ Foundation - Your architecture, patterns, and conventions

Context flows down

Smart PRD

📋 Codebase insights + Your requirements

Context grows richer

Technical Blueprint

🏗️ Codebase + PRD = Implementation plan

Perfect context achieved

Actionable Tasks

📋 Complete context = Ready to code

See The Context Engineer in Action

Context Engineer Demo

Watch Demo on YouTube

Video embedding not available - click to view directly

Get early access to lock in the launch price!

Your AI gets a complete project briefing every time

Like having a Senior PM and Senior Architect and Tech Lead always with you.

Generated by your Senior Product Manager

Product Requirements Document: TaskFlow Smart Assignment System

1. Overview & Vision

The Smart Task Assignment System will enhance TaskFlow's productivity dashboard by automatically suggesting optimal task assignments based on team member skills, current workload, and historical performance data. This AI-powered feature aims to reduce manual assignment overhead by 60% while improving task completion rates.

2. Problem Statement

Development team leads currently spend 2-3 hours weekly manually assigning tasks, often leading to suboptimal decisions. This results in:

  • • Uneven workload distribution across team members
  • • Tasks assigned to developers without relevant skill sets
  • • Delayed project timelines due to assignment bottlenecks
  • • Reduced team satisfaction from mismatched task complexity

3. Target Users

Primary Persona: Development Team Leads

  • Demographics: Senior developers, tech leads, and engineering managers
  • Behavior: Manage 5-12 person development teams with varied skill levels
  • Pain Points: Time-consuming manual assignment, difficulty tracking team capacity
  • Goals: Efficient task distribution, improved team productivity, better project outcomes

4. Success Metrics

  • Assignment Accuracy: 85%+ team lead approval rate for AI suggestions
  • Time Savings: 60% reduction in manual assignment time per sprint
  • Team Satisfaction: 4.2+ rating for task-skill matching (5-point scale)

Note: This is a condensed preview. The full PRD contains detailed user stories, acceptance criteria, technical considerations, and implementation phases.

Generated by your Senior Software Architect

Technical Implementation Blueprint: Smart Assignment System

1. Current vs Target Analysis

1.1 Current System Architecture

graph TD Client[React Frontend
TaskFlow Dashboard] --> API[Node.js API Server
:3001] API --> Auth[Authentication
JWT Middleware] API --> Tasks[Task Controller
src/controllers/tasks.js] API --> Users[User Controller
src/controllers/users.js] Tasks --> TaskService[Task Service
Manual Assignment Logic] Users --> UserService[User Service
Profile Management] TaskService --> DB[(PostgreSQL Database
Tasks Table
Users Table)] UserService --> DB API --> Config[Config
src/config/database.js] Config --> Env[Environment Variables
DB_HOST
JWT_SECRET]

1.2 Target System Architecture

graph TD Client[React Frontend
TaskFlow Dashboard] --> API[Node.js API Server
:3001] API --> Auth[Authentication
JWT Middleware] API --> Tasks[Enhanced Task Controller
src/controllers/tasks.js] API --> Assignment[Assignment Controller
src/controllers/assignment.js] Assignment --> MLService[ML Assignment Service
src/services/ml_assignment.js] MLService --> SkillAnalyzer[Skill Analyzer
Team Capability Assessment] MLService --> WorkloadBalancer[Workload Balancer
Capacity Distribution] MLService --> HistoryAnalyzer[History Analyzer
Performance Patterns] Tasks --> TaskService[Task Service
Enhanced with AI Suggestions] TaskService --> DB[(PostgreSQL Database
Tasks + Skills + Assignments)] MLService --> Redis[(Redis Cache
ML Model Results
Assignment Scores)]

1.3 Current Data & Logic Flow

sequenceDiagram participant User as Team Lead participant Frontend as React Dashboard participant API as Node.js API participant TaskService as Task Service participant DB as PostgreSQL User->>Frontend: Create new task Frontend->>API: POST /api/tasks API->>TaskService: Process task creation TaskService->>DB: Store task data DB-->>TaskService: Confirm storage TaskService-->>API: Return task ID API-->>Frontend: Task created response User->>Frontend: Manually assign to team member Frontend->>API: PUT /api/tasks/:id/assign API->>DB: Update task assignment

1.4 Target Data & Logic Flow

sequenceDiagram participant User as Team Lead participant Frontend as React Dashboard participant API as Node.js API participant Assignment as Assignment Service participant ML as ML Service participant Cache as Redis Cache participant DB as PostgreSQL User->>Frontend: Create new task Frontend->>API: POST /api/tasks API->>Assignment: Request AI assignment suggestion Assignment->>ML: Analyze team capacity & skills ML->>Cache: Check cached analysis Cache-->>ML: Return cached or compute new ML->>DB: Query historical performance DB-->>ML: Return performance data ML-->>Assignment: Return assignment scores Assignment-->>API: Suggest optimal assignee API-->>Frontend: Task + AI suggestion User->>Frontend: Accept or modify suggestion

2. Implementation Phases

Phase 1: Database Schema

  • • Skills tracking tables
  • • Assignment history schema
  • • Performance metrics storage

Phase 2: ML Service

  • • Skill analysis algorithms
  • • Workload balancing logic
  • • Performance prediction models

3. Data Models

user_skills

Column Type Constraint Description
user_id INTEGER FK → users Reference to users table
skill_name VARCHAR(100) NOT NULL Technology or domain skill
proficiency_level INTEGER CHECK (1-5) 1-5 skill rating scale
last_updated TIMESTAMP DEFAULT NOW() When skill was assessed

assignment_history

Column Type Constraint Description
task_id INTEGER FK → tasks Reference to tasks table
assigned_user_id INTEGER FK → users Who was assigned the task
completion_time INTERVAL NULL How long the task took
quality_score DECIMAL(3,2) CHECK (0-5) Performance rating (0.00-5.00)

Architecture Highlight: The ML service operates as an independent microservice, providing assignment suggestions while maintaining existing task management workflows.

Generated by your Senior Tech Lead

Implementation Tasks: Smart Assignment System

Task Breakdown by Phase

✓ 1.0 Database Schema Enhancement

1.1 Create user_skills table with skill tracking schema
1.2 Add assignment_history table for performance tracking
1.3 Create database indexes for optimal query performance
1.4 Write migration scripts for existing tasks table enhancement

✓ 2.0 ML Service Development

2.1 Create src/services/ml_assignment.js with core ML logic
2.2 Implement skill analysis algorithm for team capability assessment
2.3 Build workload balancing engine with capacity distribution

✓ 3.0 API Controller Enhancement

3.1 Create src/controllers/assignment.js for AI suggestions
3.2 Enhance existing task controller with ML integration
3.3 Add API endpoints for assignment suggestions and feedback

✓ 4.0 Frontend Integration

4.1 Update React components to display AI assignment suggestions
4.2 Implement suggestion acceptance/rejection UI with feedback ✅ USER TESTING COMPLETE

○ 5.0 Performance Optimization

5.1 Implement Redis caching for ML model results
5.2 Add background job processing for heavy ML computations

Progress: 78% complete - Core ML service and API integration implemented. Performance optimization and deployment phases remaining.

Get Better Results with Smarter Token Usage

Optimize your existing Cursor usage by giving AI the perfect context every time. Reduce wasted tokens, eliminate back-and-forth clarifications, and get precise results faster.

Stop burning tokens on trial-and-error. Get it right the first time with perfect context.

Uses your existing Claude/GPT
Reduces token waste
Code never leaves your machine

Same AI models, smarter usage, better results

Context Engineering is the new skill in AI

This is the best time in history to build your product, if you have the right tools.

How to Get Started

From setup to feature planning in under 5 minutes

1

1-Minute Setup

Add your access key to Cursor IDE. Copy, paste, done.

No complex configuration needed
2

Ask to Plan

"I want to build user authentication with Google OAuth"

AI asks smart follow-up questions
3

Get Your Plan

PRD, technical blueprint, and step-by-step tasks

Saved to your project folder
4

Start Building

Add plan as context and watch AI code with perfect understanding

References your actual files & functions

Ready to experience context-aware feature planning?

Choose your plan

Start free, then upgrade when you need unlimited access. Built by developers, for developers.

Free Plan

$0 /month

Perfect for trying out the Context Engineer

10 Free Tool Calls
Try planning 2 complex features
Complete PRD, Blueprint & Implementation Plan
Auto-detects Feature Type (UI, API, Database, Auth)
Asks you codebase-aware follow up questions
Shows Exactly Which Files to Modify
Before Vs After Data Flow Charts
Before Vs After Architecture Charts
100% Private - Code never leaves your computer
No Credit Card required
⭐ RECOMMENDED

Context Engineer PRO

$9 /month $29

Launch price special. Limited time offer.

∞ Unlimited Tool Calls
Perfect to work on large and complex codebases
Everything in Free Plan
Unlimited Product Requirement Documents (PRD)
Unlimited Architecture and Data Flow Analysis
Unlimited Context-Rich Task Lists
Launch Price Locked Forever
Priority Support
All Future Features Included
Save 69% - Early Bird Forever!

Frequently Asked Questions

A "tool call" is each individual action the Context Engineer performs - like analyzing your codebase, generating a PRD section, or creating implementation tasks.

🎯 Your 10 Free Tool Calls Include:

  • ~5 calls: Complete feature planning session (PRD + Blueprint + Tasks)
  • ~5 calls: Second feature or major refinements to existing plan

Perfect for evaluation: Plan 1-2 complete features to see how Context Engineer transforms your development workflow.

PRO Plan: Unlimited tool calls means no counting, no limits - plan as many features as you need!

Yes it works. Not only that, but the AI itself recommends following it. Check this video where we compare Claude Code Planning vs Context Engineer Planning, and then we ask Claude Code to pick the best plan for implementation:

The AI consistently chooses the Context Engineer approach because it provides better structured context and clearer implementation paths.

Context Engineering gives you predictable token usage regardless of your project's complexity. Without it, token usage grows exponentially as your codebase and features become more complex.

💡 Simple rule: With Context Engineering, you use roughly the same tokens for any feature. Without it, complex projects can consume 10x-50x more tokens than simple ones.

📊 Token Usage: Simple vs Complex Project

❌ Without Context Engineering:

Simple project: "Add login page"

~2,000 tokens (back-and-forth)

Complex project: "Add auth to existing enterprise app"

~25,000+ tokens (explaining context, multiple iterations)

📈 Token usage scales with complexity

✅ With Context Engineering:

Simple project: "Add login page"

~1,500 tokens (direct solution)

Complex project: "Add auth to existing enterprise app"

~1,800 tokens (direct solution)

📊 Consistent token usage regardless of complexity

🎯 The key insight: Context Engineering frontloads the complexity. Your AI already knows your codebase, so every request - simple or complex - uses roughly the same tokens for a complete solution.

We ran a comprehensive analysis on Context Engineer's own codebase since we used the tool to build itself. Here's the actual data:

Feature Manual Time Context Engineer Time Time Saved
MCP Implementation 3 weeks 4.5 hours 27x faster
Intelligent Categorization 2 weeks 3.5 hours 23x faster
Landing Page 1.5 weeks 4 hours 15x faster
User Verification 2.5 weeks 4 hours 25x faster
Stripe Webhooks 3.5 weeks 4.5 hours 31x faster
Mixpanel Analytics 1.5 weeks 3 hours 20x faster
Freemium Model 4 weeks 5 hours 32x faster
TOTALS (7 features) 18 weeks 28.5 hours 25x faster

Here's what this means:

  • • 2 hours total spent providing requirements and answering follow-up questions across all 7 features
  • • 28.5 hours total from plan to production (planning + implementation)
  • • Manual approach: Would have been 18 weeks (720 hours)

Average per complex feature: 17 minutes of answering questions, then 4.1 hours from plan to production vs 2.6 weeks of manual work.

If you can't ship a complex feature on a complex codebase faster than 4-5 hours total, Context Engineer will probably help you significantly.

Note: These times are from manually following AI guidance step-by-step. Using background coding agents can speed up implementation even more if your budget allows it.

Especially yes. Messy, complex codebases are exactly where Context Engineering shines.

Here's why:

  • • Clean codebases are easier to explain to AI manually
  • • Complex codebases take forever to explain and are easy to get wrong
  • • Context Engineering automatically maps your complexity

Examples it handles well:

  • ✅ Large monorepos with multiple services
  • ✅ Legacy applications with custom patterns
  • ✅ Mixed technology stacks
  • ✅ APIs with complex business logic

The messier your codebase, the more valuable this becomes. Your codebase isn't too complex - it's exactly why you need this.

We're tech-stack agnostic by design. Context Engineering works by understanding code patterns and project structure, not specific technologies.

Currently works great with:

  • • JavaScript/TypeScript (React, Next.js, Node.js, Vue, Angular)
  • • Python (Django, Flask, FastAPI)
  • • Any REST/GraphQL APIs
  • • Most databases (PostgreSQL, MongoDB, etc.)

The magic isn't in framework-specific knowledge:

  • • Understanding how your project is organized
  • • Learning your specific patterns
  • • Mapping relationships between files
  • • Respecting your architectural decisions

Zero disruption. Context Engineering fits into your existing workflow seamlessly.

Your current flow:

1 Idea
2 Manual Planning
3 Start Coding

New flow:

1 Idea
2 Context Engineering
3 Better Planning
4 Start Coding

What stays exactly the same:

  • • Your IDE, your tools, your deployment process
  • • Your code review process
  • • Your team collaboration methods
  • • Your existing AI subscriptions

Great question. Context Engineering shines for medium-to-complex features, but even "simple" features often have hidden complexity.

Perfect for:

  • • User authentication (simple idea, many edge cases)
  • • Payment integration (seems straightforward, actually complex)
  • • File uploads (easy concept, security/performance concerns)
  • • Any feature touching multiple parts of your system

Maybe overkill for:

  • • Changing button colors
  • • Adding simple text fields
  • • Pure CSS styling updates
  • • Tiny one-line changes

The surprising thing:

Most "simple" features become complex once you consider your specific codebase, security, error handling, testing, etc.

Use this rule: If you'd normally spend time explaining the feature to a colleague, Context Engineering will help.

Use Context Engineering for planning, regular coding for implementation.

Use Context Engineering when:

  • • Starting any new feature
  • • Adding integrations (payments, auth, APIs)
  • • Building anything you haven't built before
  • • Working in unfamiliar parts of your codebase
  • • Need to involve non-technical stakeholders

Skip Context Engineering for:

  • • Quick bug fixes
  • • Simple styling changes
  • • Copying existing patterns exactly
  • • Tiny one-line changes

Think of it as your technical architect - you wouldn't ask a senior architect to help you fix a typo, but you'd definitely want their input before building a new system.

Yes. If the IDE supports MCP (Model Context Protocol), it works. Here's how to install your Context-Engineer MCP server across supported platforms — all confirmed and up-to-date as of January 2025.

🤖 Claude Code CLI

  1. Run: claude mcp add --transport http "Context-Engineer" https://contextengineering.ai/mcp --header "Authorization: Bearer your-access-key"
  2. Verify: claude mcp list
  3. Test: Use > /mcp in Claude Code

🧠 Cursor IDE

  1. Open ~/.cursor/mcp.json
  2. Add:
{
  "mcpServers": {
    "context-engineer": {
      "url": "https://contextengineering.ai/mcp",
      "headers": {
        "Authorization": "Bearer your-access-key"
      }
    }
  }
}
  1. Restart Cursor

🌊 Windsurf IDE

  1. Open ~/.codeium/windsurf/mcp_config.json
  2. Add:
{
  "mcpServers": {
    "context-engineer": {
      "serverUrl": "https://contextengineering.ai/mcp",
      "headers": {
        "Authorization": "Bearer your-access-key"
      }
    }
  }
}
  1. Restart Windsurf

Replace your-access-key with your actual access key.

🛠 TL;DR

  • Works with Cursor, VS Code, Windsurf, Claude CLI, and Claude Desktop
  • Installation is simple: via JSON, GUI, or CLI
  • Once set up, tools appear automatically and work with full project context

Need help? DM me on X @alessiocarra_

Requirements:

  • Node.js 16+
  • MCP-enabled IDE
  • Internet connection
  • Windows/Mac/Linux

Works with:

  • JavaScript/TypeScript
  • Python
  • React/Next.js
  • Most web frameworks

Installation takes under 2 minutes. Works locally with your IDE's internet connection (same as Cursor needs).

Manual prompting requires you to explain your project architecture every single conversation. Context Engineering automates this completely.

❌ Manual Prompting:

  • • Re-explain project structure every time
  • • Copy-paste file contents manually
  • • AI forgets context after 3 messages
  • • Inconsistent with your patterns
  • • Hours wasted on setup explanations
  • • Wastes tokens on repeated explanations
  • • Burns through usage with trial-and-error

✅ Context Engineering:

  • • Automatically analyzes your codebase
  • • Understands your architecture patterns
  • • Perfect context every conversation
  • • Respects your existing code style
  • • Zero setup time per feature
  • • Reduces token waste significantly
  • • Gets accurate results on first try
  • • Maximizes your existing Cursor subscription

It's like having a full senior team of PMs, Architects, and Engineers who actually reads your entire codebase before giving advice.

Your code never leaves your machine. We're built privacy-first and only collect what's absolutely necessary:

Zero code access - Your source code stays local, always
Just your email - Only for account & billing management
Auto-delete planning data - Your answers are erased after each session
No chat monitoring - We don't see your conversations or AI responses

We work through MCP protocol in your IDE, just like your normal coding. Full privacy policy →

No catch! As an indie developer, I believe in fair pricing. The $9/month launch special is:

  • 💡 Locked forever - your price will never increase
  • 🚀 All features included - no premium tiers or paywalls
  • 🎯 Early adopter reward - first 500 users get this price

You can reach me on X directly. I'll respond to all messages as soon as possible: Alex (@alessiocarra_)

You're right — this is an early-stage product. But here's why I'm confident it works:

  • 🛠️ I use it every day: This is the tool I built for myself and use daily. If it breaks, my own development stops.
  • 📞 Direct access to me: Issues? Questions? Hit me up on X (@alessiocarra_) — I respond personally.
  • 🚀 Early adopter benefits: Your feedback directly shapes the product. You're getting in on the ground floor of something that works.

Absolutely! Context Engineering is designed to bridge the gap between non-technical stakeholders and technical implementation.

Perfect for:

  • • Product managers planning features
  • • Entrepreneurs validating ideas
  • • Business owners communicating with developers
  • • Anyone who needs technical plans but doesn't code

You get:

  • • Clear, step-by-step technical plans
  • • Realistic timelines and cost estimates
  • • Technical specs developers can follow
  • • Risk assessments and alternatives

Bottom line: If you need to plan technical work but don't want to learn to code, this gives you the technical fluency to make smart decisions and communicate effectively with developers.

Hey, I'm Alex

Alex Carra, Founder of Context Engineering

When I started building with AI, I was excited. Finally — a coding partner that never gets tired and helps me move faster. At the beginning, it felt great. Things were working.

But as the project got bigger, the problems started. The repo turned into a mess. Files everywhere, duplicated logic, random folders. The AI kept making changes that looked smart but caused issues later.

One time I was impressed that a new feature worked — until I realized the AI had faked everything with dummy data, just to make it look like it worked. Another time it removed important parts of a test just to make it pass.

I wasn't coding with AI anymore. I was babysitting it.

After years of building products and writing code, I knew this couldn't scale. So I started experimenting — slowly, manually, one piece at a time — testing different ways to give AI the right context until it finally started working the way I needed. It wasn't fancy, but it delivered real results. No expensive tools, no magic. Just a better way to work with the AI you already use in Cursor.

I built it for myself first. That's how Context Engineering was born. And yes — I used it (in its roughest form) to build itself.

If you're working with AI and tired of fixing its mess, you probably need this too.

Questions? Hit me up on X Alex — I respond to everything.