Highlight #1
AI-Assisted Smart Contract Development Learn how to use AI to design, generate, and refine smart contract code while maintaining strong engineering discipline and security awareness.
Highlight #2
Security and Risk Awareness in AI Workflows Understand how AI can introduce vulnerabilities into blockchain systems and learn practical techniques to review, validate, and secure AI-assisted code.
Highlight #3
Faster Development and Testing Cycles Use AI to accelerate debugging, testing, documentation, and development workflows without sacrificing reliability or architectural clarity.
About the course
This course is designed for blockchain developers who already understand smart contracts and system fundamentals and want to use AI tools responsibly and effectively across the full development lifecycle. Rather than treating AI as a code generator or shortcut, the course examines how AI can accelerate engineering work while introducing new risks, failure modes, and adversarial dynamics. The focus is on where AI genuinely adds leverage in blockchain engineering, where it poses hidden risks, and how to design guardrails so that human judgment remains central. Students will learn to use AI for analysis, review, testing, and reasoning without outsourcing responsibility for correctness, security, or system behavior.
Dr. Dan Morrill
Has been working in Blockchain nearly since its inception with the launch of Bitcoin, and has recently, over the last two years, been working in AI.
Curriculum
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1
AI Change Everything
- How AI Changes Blockchain Engineering Free preview
- Necessary Complexity Free preview
- AI as an Accelerator not an Authority Free preview
- AI Lead Me Astray! Free preview
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Why the Blockchain can Amplify AI Failure
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Where human judgement cannot be delegated
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What is and Is not AI Engineering
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Case Study the AI generated Vault
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Chapter Summary
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Section 1 Quiz
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2
AI Assisted Design and Architecture Review
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Architectural Design Review
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Using AI for Requirements Gathering
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Defining Functional and Non-Functional Design with AI
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Hidden Constraints and Tradeoffs
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Clarifying Security And Authorization Assumptions
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Case Study - Design Error
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Chapter Summary
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Section 2 Quiz
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3
AI Assisted Smart Contract Development
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AI Before and After Deployment
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AI Boundaries
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Hallucinated Correctness
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Reasoning Uncertainty
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AI Should Not Deploy
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Human Review As Control
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Plausible But Incorrect Code
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Safe Use of AI
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Chapter Summary
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Section 3 Quiz
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4
AI for Testing and Invariant Discovery
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AI Fuzzing
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AI Model Entrenchment
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AI Overfitting
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AI Risk Weighting
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AI Testing Misses
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AI Test Scenarios
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FalseConfidence
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Gradient Collapse
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Testing Not Proof
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When Testing Increases Risk
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Chapter Summary
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Section 4 Quiz
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5
AI for Threat Modeling and Security Review
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Adversarial Reasoning
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AI Threat Models
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Security Failures as Economic Design Failures
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Security Review as a Human Responsibility
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Threat Model Drift
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AI Abuse Case
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Adversarial Goal Modeling
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Chapter Summary
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Chapter 5 Quiz
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6
AI in UAT and System Validation
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AI Generated User Behavior
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Economic Incentive Testing
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Specification Gaps
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AI Validation
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Invariant Driven VS Scenario Driven UAT
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The Coverage Illusion
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Authority Inversion
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Limitations of AI Based Validation
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AI Rarely delivers bad news
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Chapter Summary
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Section 6 Quiz
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7
AI as a Second Order System
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How AI Changes Developer Behavior
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AI Induced Risk and Complacency
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Tool Dependency and Skill Atrophy
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Feedback Loops
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Responsibility Diffusion
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Metric Corruption and False Progress
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Long Term effects on Engineering Teams
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Chapter Summary
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Chapter 7 Quiz
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8
Guardrails, Policies, and Prohibited Uses
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Defining Where AI is Allowed
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Defining where AI is Prohibited
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Context Aware AI
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Enforceable Guardrails or Policy Theater?
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HITL Human In The Loop
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Auditability and Accountability for AI use
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Failure Modes
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Chapter 8 Quiz
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9
AI and Adversaries
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Attackers use AI too
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AI as a Reconnaissance Multiplier
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Automated Exploit Discovery and Social Engineering
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Defenders vs Attacker Cost Curves
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AI Arms Races in Blockchain Ecosystems
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When Assumption Based Security Fails
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Designing Systems under AI Symmetry
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AI's Pleasing Personality
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Limits of AI in Adversarial Thinking
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Chapter 9 Summary
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Chapter 9 Quiz
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10
Tooling without Lock-in
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AI Tools and Flexibility
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Over-Reliance on AI tools Failure Modes
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Output Validation Failures
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Tool Change and Drift as Incident Triggers
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Working with Technical Debt
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Early Warnings of Tool Lock-in
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AMM Model Drift
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Chapter 10 Quiz
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11
Operational use of AI
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Using AI for Operational Monitoring
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Incident Response
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Operational use of AI
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When AI is the Incident
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When AI Models Retire
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Show me the Metrics, I'll show you the Exploit
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Case Study - Developer Tools and Supply Chain Risk
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Model Change Management
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AI Model Migration Checklist
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Chapter 11 Summary
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Chapter 11 Quiz
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12
Legal, Ethical, and Ownership Implications
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Who is Responsible?
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Communicating Limitations
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Compliance, Audit and Disclosure
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Engineering Ownership
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AI Generated Code and Legal Responsibility
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Case Study - The Silent Drift Incident
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Chapter 12 Summary
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Chapter 12 Quiz
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13
Vibe Coding
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What Vibe Coding Actually Is
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Why Vibe Coding feels so Effective
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When Vibe Coding Works Well
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When Vibe Coding does not work Well
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The Skill Gap Problem
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Vibe Coding in Adversarial and Economic Systems
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Guardrails for Responsible Vibe Coding
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Professional Identity in an AI World
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Good Prompt Bad Prompt
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Vibe Coding and Token Management
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Case Study - When the Summary lost the Invariant
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Tech Debt at Scale
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Vibe Coding Example
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Vibe Coding Pushback
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Vibe Coding Failure Case Study
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Chapter 13 Summary
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Chapter 13 Quiz
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14
Prompts
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Effective AI Assisted Development Workflows
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Minimal Viable Prompting
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Context Beats Clever Wording
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Vibe Coding Safely with Guardrails
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Asking AI for Security help without getting Burned
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Verification Workflows
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Where AI Shines
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Common Failure Patterns to Avoid
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Chapter Summary
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Chapter 14 Quiz
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15
Wrap up and Testing
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Final Quiz
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