Master AI-Augmented
Interviews
of developers use AI tools daily
real-world scenarios to practice
less anxiety with clear expectations
free and open for everyone
Master AI-Augmented Interviews with Interactive Practice
Learn what good looks like through real-world scenarios with clear examples of good and bad approaches
Coding Challenges
Algorithms, data structures, async programming, and performance optimization
- ✓LRU Cache implementation
- ✓Promise.all vs sequential
- ✓React performance tuning
System Design
Distributed systems, ML models, APIs, and scalability patterns
- ✓ML fraud detection systems
- ✓Distributed caching layers
- ✓RESTful API best practices
Real Problems
Debugging, security, communication, and code review scenarios
- ✓SQL query optimization
- ✓Security vulnerabilities
- ✓AI-generated code review
How It Works
Read the Scenario
Understand the interview question and see what AI suggests
Compare Approaches
See good vs bad side-by-side with real examples and impact
Remember Key Lessons
Take away concrete strategies you can apply immediately
Who Is This For?
Whether you're interviewing or conducting interviews, we've got you covered
For Candidates
Master the art of using AI tools effectively during technical interviews
- ✓Learn what works: See exact examples of good approaches to common interview questions
- ✓Avoid pitfalls: Understand common mistakes that make you look bad even with AI
- ✓Build confidence: Practice with realistic scenarios before the real interview
- ✓Use AI ethically: Learn to communicate transparently about AI usage
For Interviewers
Assess candidates fairly in the AI era while maintaining signal quality
- ✓Ask better questions: Focus on understanding and judgment, not memorization
- ✓Spot red flags: Identify when candidates don't understand AI-generated code
- ✓Design fair assessments: Create AI-aware interviews that test real skills
- ✓Set clear policies: Learn how to communicate AI guidelines effectively
Resources & Further Reading
Curated collection of articles, research, and guides on modern technical assessment and AI-assisted coding.
How GenAI is Reshaping Tech Hiring
AI interview questions tripled since 2023. Companies now test how candidates think WITH AI, not just what they know.
State of Interviewing 2025: AI Trends
In-person interviews rose from 24% to 38% in 2025 as companies counter AI cheating. Project-based assessments replace algorithmic take-homes.
AI in Interviews: Cheating or New Normal?
Karat found 7-25% of candidates use GenAI when explicitly banned. Industry shifts from prevention to acceptance with clear policies.
Candidate Assessment Ultimate Guide 2025
Structured assessments with clear expectations reduce bias by 48%. Skills-based hiring is now essential, not optional.
Fairness in AI-Driven Recruitment
Academic research on reducing bias in AI hiring. Covers differential item functioning (DIF) and algorithmic fairness metrics.
AI Assisted Programming Complete Guide
82% of developers use AI tools. Best practice: "If an LLM writes code and you review and test it, that's responsible AI assisted programming."
GitHub Copilot Security Analysis
Stanford research: AI coding assistant users wrote significantly less secure code but believed it was MORE secure. Critical awareness needed.
Prompt Engineering in Coding Interviews
HackerRank launched prompt engineering questions in January 2025, making AI proficiency a core assessment skill.
Reducing Bias in Recruitment: 5 Steps
Implement blind CV screening, structured assessments, diverse interview panels, and skills-based evaluation to eliminate hiring bias.
Top AI Interview Tools for Recruiters
Overview of platforms like HireVue, Paradox, Woven, and others reshaping technical assessments with AI capabilities.
ChatGPT & Future of Coding Interviews
Technical founder perspective on adapting coding interviews for an era where candidates have AI tools.
AI Code Compliance Strategies
Addressing compliance, security, and privacy concerns when using AI coding assistants in regulated environments.