Chapter 3 Exercises: The AI Coding Tool Landscape

These exercises are organized into five tiers based on Bloom's taxonomy, progressing from basic recall to challenging creative tasks. Complete them in order to build a thorough understanding of the AI coding tool landscape.

Tier 1: Recall (Exercises 1-6)

These exercises test your ability to remember key facts and concepts from the chapter.

Exercise 1: Tool Category Matching

Match each AI coding tool to its primary category:

Write your answers as pairs (e.g., "Claude Code - C").

Exercise 2: Feature Identification

List three key features of each of the following tools. Do not refer back to the chapter -- test your recall. 1. Claude Code 2. GitHub Copilot 3. Cursor 4. Aider

Exercise 3: Pricing Recall

Without looking at the comparison table, fill in the approximate monthly cost for the individual/pro tier of each tool: 1. Claude Code (Pro) 2. GitHub Copilot (Pro) 3. Cursor (Pro) 4. Windsurf (Pro) 5. Aider

Exercise 4: Vocabulary Definitions

Define each of the following terms in your own words: 1. Ghost text 2. Agentic development 3. Interaction model 4. Repository mapping 5. Inline completion

Exercise 5: Tool Limitation Recall

For each tool below, name its two most significant limitations as described in the chapter: 1. Claude Code 2. GitHub Copilot 3. Cursor 4. Windsurf 5. Aider

Exercise 6: Strengths Identification

Identify which tool is described by each statement: 1. "Excels at deep reasoning and multi-step debugging tasks" 2. "Pioneered inline ghost text code completion" 3. "Offers the most integrated AI coding experience as a single application" 4. "Emphasizes flow-based proactive assistance" 5. "Only fully open-source option among major tools" 6. "Can deploy applications directly from the development environment"

Tier 2: Apply (Exercises 7-12)

These exercises ask you to apply your knowledge to specific scenarios.

Exercise 7: Beginner Recommendation

Your friend is learning to program for the first time. They have never used a terminal and are intimidated by command-line interfaces. They want to build a simple web application as their first project. Based on the decision framework in Section 3.9, which tool or tools would you recommend? Write a brief (200-word) recommendation explaining your reasoning.

Exercise 8: Comparison Table Extension

The chapter's comparison table covers five major tools. Add a row for one of the "Other Notable Tools" from Section 3.7 (Replit Agent, Bolt, v0, or Devin). Fill in all columns based on the information provided in the chapter and any additional research you can do.

Exercise 9: Budget Optimization

A freelance developer has a budget of $25/month for AI coding tools. They work primarily in Python and JavaScript, building web applications. They want both inline completions and the ability to handle complex reasoning tasks. Design a tool combination that fits within their budget and explain your choices.

Exercise 10: Privacy Assessment

A developer at a healthcare company needs to write code that processes patient data. Privacy is paramount -- no source code can be sent to external servers. Evaluate each of the five major tools on their privacy capabilities and recommend a solution. Explain what trade-offs the developer will face.

Exercise 11: Workflow Mapping

Map the following development tasks to the most appropriate AI coding tool. For each task, explain why you chose that tool: 1. Writing a new REST API endpoint with complex business logic 2. Quickly generating boilerplate code for a new class 3. Debugging a race condition in a multithreaded application 4. Creating a new React component based on a design mockup 5. Refactoring a 10,000-line legacy codebase 6. Writing unit tests for existing functions

Exercise 12: Interaction Model Application

For each interaction model below, describe a specific coding scenario where it would be most effective and a scenario where it would be least effective: 1. Inline completion (keystroke-by-keystroke suggestions) 2. Chat-based conversation (describe what you want, get code back) 3. Agentic workflow (AI plans and executes multi-step tasks autonomously)

Tier 3: Analyze (Exercises 13-18)

These exercises require you to break down concepts, compare approaches, and draw conclusions.

Exercise 13: Convergence Analysis

The chapter mentions a "convergence trend" where tool categories are blurring. Analyze this trend by answering: 1. What specific features have crossed category boundaries? Give three examples. 2. What does this convergence mean for developers choosing tools in the future? 3. Could all AI coding tools eventually become the same? Why or why not? What fundamental differences might persist?

Exercise 14: Business Model Analysis

Compare the business models of the five major tools: 1. How does each tool monetize its offering? 2. What are the advantages and disadvantages of subscription pricing versus API-based pricing (pay-per-use)? 3. How does Aider's open-source model differ economically from the others? 4. Which business model do you think is most sustainable long-term? Justify your answer.

Exercise 15: Strengths and Weaknesses Trade-offs

For each pair of tools below, analyze the trade-offs a developer faces when choosing between them: 1. Claude Code vs. GitHub Copilot 2. Cursor vs. Windsurf 3. Aider vs. Claude Code 4. GitHub Copilot vs. Cursor

For each pair, identify: (a) what you gain by choosing tool A over tool B, (b) what you lose, and (c) scenarios where each would be the better choice.

Exercise 16: Context Management Comparison

Different tools handle code context differently: - Claude Code uses file reading and search - Cursor uses semantic codebase indexing with RAG - Copilot uses open file context - Aider uses repository mapping

Analyze how each approach affects: 1. The quality of AI suggestions for a small project (< 1,000 lines) 2. The quality of AI suggestions for a large project (> 100,000 lines) 3. The speed of the AI's response 4. Memory and resource consumption

Exercise 17: Market Position Analysis

Using the information from this chapter, create a 2x2 matrix with these axes: - X-axis: Autonomy level (low to high) -- how independently the AI can work - Y-axis: Integration depth (low to high) -- how deeply the tool integrates into the IDE

Plot each of the five major tools on this matrix. Write a paragraph analyzing what the positioning reveals about each tool's strategy and target user.

Exercise 18: Evolution Prediction

Based on the current trajectory of AI coding tools described in this chapter, analyze and predict: 1. Which features are likely to become standard across all tools within two years? 2. Which tools are best positioned to adapt to future advances in AI models? 3. What entirely new categories of AI coding tools might emerge?

Write a 500-word analysis supporting your predictions.

Tier 4: Create (Exercises 19-24)

These exercises ask you to create original work based on chapter concepts.

Exercise 19: Tool Evaluation Rubric

Create a detailed evaluation rubric with at least 10 criteria that a development team could use to systematically evaluate AI coding tools. For each criterion, define: - What it measures - How to score it (1-5 scale with descriptions for each level) - Its relative weight (importance)

Test your rubric by applying it to at least two tools from the chapter.

Exercise 20: Decision Flowchart

Create a decision flowchart (in text or diagram form) that guides a developer to the right AI coding tool based on a series of yes/no questions. The flowchart should cover at least 8 decision points and lead to specific tool recommendations. Consider factors like experience level, budget, privacy needs, preferred workflow, project type, and team size.

Exercise 21: Tool Pitch

Choose one of the AI coding tools from this chapter and write a 500-word "pitch" for it, as if you were recommending it to your company's engineering leadership. Include: - What the tool does and how it works - Specific benefits for the team's workflow - Cost analysis compared to developer time saved - Risk assessment (limitations and mitigation strategies) - An implementation plan for adoption

Exercise 22: Multi-Tool Workflow Design

Design a complete multi-tool workflow for one of the following scenarios. Specify which tool to use for each phase, how to transition between tools, and how to manage context across tools. 1. Building a full-stack web application from scratch 2. Maintaining and extending a large legacy codebase 3. A data science project from exploration to production 4. An open-source library development workflow

Exercise 23: Comparison Blog Post

Write a 1,000-word blog post titled "I Tried 5 AI Coding Tools So You Don't Have To" that compares the major tools from a first-person developer perspective. Include specific scenarios where each tool shone and where each struggled. Make it engaging and practical for a developer audience.

Exercise 24: Onboarding Guide

Create a one-page quick-start onboarding guide for a new team member who needs to set up and start using either Claude Code or Cursor (choose one). Include: - Installation steps - Initial configuration recommendations - Five "first things to try" with specific example prompts - Common pitfalls to avoid - Links to further resources

Tier 5: Challenge (Exercises 25-30)

These exercises push beyond the chapter content and require independent research and critical thinking.

Exercise 25: Hands-On Tool Comparison

Install free tiers of at least two AI coding tools discussed in this chapter. Use them both for the same coding task (such as building a simple to-do application or a REST API). Document your experience with each tool, noting: - Setup time and ease - Quality of AI suggestions - Speed of interaction - How naturally the tool fit into your workflow - Specific instances where the AI impressed or disappointed you

Write a 1,000-word comparative review based on your hands-on experience.

Exercise 26: Ethical Analysis

AI coding tools raise several ethical questions. Write a 750-word essay addressing: 1. Code ownership: When AI generates code, who owns it? 2. Training data concerns: These tools were trained on publicly available code. What are the ethical implications for open-source developers whose code was used for training? 3. Job displacement: How might AI coding tools affect junior developer hiring? 4. Skill atrophy: Could reliance on AI tools diminish developers' coding skills over time?

Take a clear position on each question and support it with reasoning.

Exercise 27: Custom Tool Design

If you were designing a new AI coding tool from scratch, what would it look like? Write a detailed product specification (1,000+ words) that includes: - Target user persona - Core interaction model - Key differentiating features - Technical architecture (at a high level) - Pricing strategy - How it would compete with existing tools

Justify each design decision based on gaps you identified in the current tool landscape.

Exercise 28: Enterprise Evaluation Report

Imagine you are a senior developer tasked with evaluating AI coding tools for a 50-person engineering team at a mid-size company. Write a formal evaluation report that includes: - Requirements gathering (what does the team need?) - Tool shortlist with justification - Detailed evaluation of top 3 candidates - Security and compliance assessment - Cost projections for one year - Recommendation with rollout plan

Exercise 29: Historical Analysis

Research the history of AI-assisted coding tools, going back to early autocomplete features in IDEs. Create a timeline of key milestones and write a 750-word analysis of how the field has evolved. Consider questions like: - What were the key technological breakthroughs? - How did developer attitudes toward AI tools change over time? - What failed approaches were attempted before the current generation of tools?

Exercise 30: Future of AI Coding Tools

Write a speculative but well-reasoned 1,000-word essay on where AI coding tools will be in five years (by 2031). Address: - How will the interaction models evolve? - Will there be a dominant tool, or will the ecosystem remain fragmented? - What new capabilities might emerge as AI models improve? - How will the role of the human developer change? - What are the biggest risks and challenges ahead?

Support your predictions with trends you can observe in the current landscape.

Submission Guidelines

• For written exercises, aim for clarity and specificity over length.
• For analytical exercises, support your claims with evidence from the chapter or external research.
• For creative exercises, be original -- do not simply restate chapter content.
• For coding exercises (referenced in the code directory), ensure all Python code is syntactically correct and follows PEP 8 conventions.
• For hands-on exercises, document your actual experience honestly, including frustrations and surprises.