AI Engineering Cohort: https://aiengg.dev/ Github resources: https://github.com/InterviewReady/ai-engineering-resources On 6th June 2026, we are launching our first AI Engineering Cohort. A focused, intense 8-week program for software engineers who want real, production-grade AI skills. More details coming soon. Cheers!

Ferryman has hit $1,000 MRR! I'm documenting building Ferryman.io to be a 10k+ MRR SaaS product. I'm capturing everything I'm doing to build, market, and grow the product from the very beginning and I hope you'll follow along with the journey. My goal is to one day be able to look back and see what I did to build the product to $10k+ MRR and I hope documenting this process can help others. If you're thinking of building something, just start! Starting is the most difficult part, but you'll figure it out as you go so start now! Try Ferryman: https://ferryman.io Try Linear (6 months free): https://linear.app/partners/knj

Cohort Link: https://aiengg.dev 00:00 Introduction 00:48 1. Hallucinations in AI 04:28 2. Evals and Tool Calling 13:45 3. Taking AI to 100% Automation 20:33 4. What will happen in 5 years? 27:30 5. Is DSA Dead? 28:23 6. Is AGI Coming 29:55 7. Steps to Learn AI as an SDE 33:16 Thank you! Core Theme: Problem Definition & Evaluation The key to successful AI systems isn't the model—it's how well you define the problem and evaluate the solution. Problem definition accounts for 30% of success, evaluation 70%. Main Points: Problem Definition - Be explicit about constraints, tech stack, success metrics, and requirements. Don't give vague instructions to AI; frame the problem like you would for a human engineer. Context Matters - Hallucinations don't just come from models; bad context you provide causes them. Scope context to what's actually needed for the problem. Evaluation is Everything - Most engineers evaluate AI systems on output alone. The 5% winners evaluate on tool-calling sequences, decision-making logic, and whether the system followed proper patterns—not just whether the final answer looks right. Example: Booking a Flight - A bad system just books the ticket (95% metric). A good system first gathers user data, identifies missing information (date, preferences, airline), asks clarifying questions, then books (5% thinking). Subject Matter Experts Are Critical - Don't automate without verification. Sit with domain experts (nurses for healthcare, aerospace engineers for aviation) to validate prompts and instructions before deployment. Don't Overcomplicate - Focus on fundamentals, not frameworks. Don't use LLM-as-judge as a cop-out; that's lazy when you should have ground truth and proper evaluation. Bottom Line: Success comes from rigorous problem scoping, proper evaluation metrics, and subject matter expert involvement.

InterviewReady: https://interviewready.io/ You can follow me on Twitter: https://twitter.com/gkcs_ #ML #AI #Roadmap

In this mock machine learning interview, a data science student tackles fundamental ML concepts with a faang engineer. Watch as they explore supervised vs unsupervised learning, dive deep into algorithms like K-means clustering and decision trees, discuss neural network training with backpropagation, and attempt to model a real-world product recommendation system. The interview showcases both strong theoretical foundations and areas for practical improvement. 🧩 The Problem: ML Engineering Fundamentals (Medium) This technical interview covers core machine learning concepts including supervised/unsupervised learning algorithms, model training techniques, overfitting prevention, neural network architecture, loss functions, optimization methods, and practical problem modeling. The candidate must demonstrate understanding of both theoretical concepts and their real-world applications, culminating in designing an ML solution for Amazon's product recommendation system. Chapters 0:00 - Introduction and background 2:30 - Supervised vs unsupervised learning 3:21 - K-means clustering deep dive 8:48 - Decision trees and Gini impurity 17:27 - Overfitting and underfitting concepts 21:37 - Neural network training and backpropagation 37:52 - Practical ML problem modeling 43:58 - Interview feedback and discussion Concepts Learning Algorithm Fundamentals - Supervised learning with labeled data and targets - Unsupervised pattern recognition without labels - Algorithm selection based on problem type - Distance metrics and optimization objectives Model Training and Optimization - Gradient descent and parameter updates - Loss function selection for different problem types - Learning rate scheduling and convergence strategies - Backpropagation for neural network weight updates Overfitting Prevention and Regularization - Model complexity control through hyperparameters - Training vs validation performance monitoring - Regularization techniques like L1 penalty terms - Feature engineering and dimensionality reduction Practical Problem Modeling - Translating business problems into ML frameworks - Training data construction and feature representation - Supervised learning for recommendation systems - User-item interaction modeling and probability prediction 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-ml-engineering-fundamentals-1 🔗 Explore more faang interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock machine learning interview, a senior college student studying AI applications walks through their background and experience before fielding questions on core ML concepts. Drawing on their self-directed ASL-to-speech translation project as a practical example, the conversation covers supervised vs unsupervised learning, feature engineering with MediaPipe hand landmarks, overfitting and regularization techniques, and the general training process. The interviewer closes with feedback on the value of pairing theoretical grounding with hands-on experimentation. 🧩 Mock Machine Learning Interview (Beginner) A general machine learning mock interview covering core concepts including supervised learning, overfitting, and regularization, using the candidate's self-directed ASL-to-speech translation project as the running practical example throughout. Chapters - 0:00 Introduction and candidate background - 2:32 Supervised vs unsupervised learning fundamentals - 4:43 Deep dive into ASL translation project details - 15:19 Model architecture and training process discussion - 20:51 Training methodology and gradient descent concepts - 25:09 Overfitting challenges and solutions - 29:31 Regularization techniques and model complexity - 37:18 Feedback and learning recommendations Concepts Machine Learning Fundamentals - Supervised learning with labeled datasets vs unsupervised clustering approaches - Common algorithms like regression, decision trees, and neural networks - Training data format and feature engineering considerations Model Architecture & Implementation - Transitioning from CNN to MLP with coordinate-based features - Using MediaPipe for hand landmark detection and coordinate extraction - Feature engineering with 160+ attributes including distances, angles, and finger positions Training Process & Optimization - Understanding model parameter updates during training - Introduction to gradient descent and backpropagation concepts - Performance evaluation on training vs test datasets Overfitting & Regularization - Recognizing when models memorize training data vs learning generalizable patterns - Techniques like dropout, early stopping, and noise injection - Balancing model complexity with generalization ability Data Quality & Preprocessing - Challenges with synthetic vs real-world training data - Using cosine similarity to validate dataset consistency - Importance of diverse, representative training examples 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/ML-Behavioral-Interview-1 🔗 Explore more FAANG interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.

In this mock programming interview, a software engineer with 3.5 years of experience preps for an upcoming interview by tackling a hard algorithmic problem. Watch as the interviewee works through problem clarification, proposes a brute-force baseline, and then derives an efficient recursive quad-search solution...all while managing time and communicating their thought process clearly. 🧩 The Problem: Counting Black Holes with Quadtree Binary Search (Hard) Given the bottom-left and top-right coordinates of a 2D region of space (with coordinate values ranging from 0 to 1,000), and a black-box helper function blackhole_present(bottom_left, top_right) that returns true if one or more black holes exist within a given sub-region, design an efficient algorithm to count the total number of black holes in the region. The challenge is that each call to the helper function is expensive, so a brute-force, cell-by-cell scan is impractical; the solution must minimize the number of calls using a divide-and-conquer, quad-search strategy. Chapters - 0:00 Introductions & interview format - 4:10 Problem statement presented - 7:31 Clarifying questions: inputs, outputs, and constraints - 13:56 Brute-force approach & transition to binary/quad search - 30:41 Coding the recursive quad-search solution - 44:50 Dry run & edge case walkthrough - 50:40 Feedback session Concepts Problem Clarification & Constraint Gathering - Confirmed inputs (bottom-left and top-right coordinate pairs) and output (integer count of black holes) - Identified coordinate range (0–1,000 for both x and y axes) - Clarified behavior of the helper function, including single-cell queries and boundary conditions Brute-Force Baseline & Optimization Motivation - Proposed iterating over every cell as an O(n) time, O(1) space baseline - Identified the practical constraint on total helper function calls as the driver for optimization - Used the brute-force analysis to justify transitioning to a logarithmic approach Divide-and-Conquer (Quadtree) Strategy - Key insight: splitting a 2D region requires dividing into four quadrants, not two halves - Recursive structure: if a region returns false, prune immediately; if true, subdivide further - Base cases: return 0 if no black hole present, return 1 if the region collapses to a single coordinate point Coordinate Handling & Edge Cases - Carefully computed midpoints for both x and y axes to define quadrant boundaries - Added boundary guards to prevent out-of-bounds calls to the helper function (e.g., mid_x + 1 ≤ top_x) - Discussed edge cases: empty grid, single-row or single-column regions, and single-point regions Time & Space Complexity Analysis - Time complexity: O(m log n), where m is the number of black holes and n is the total number of cells - Space complexity discussion: tied to the recursive call stack depth rather than the black hole count - Interviewer noted the importance of thoroughly justifying space complexity, not just stating the result Interview Execution & Communication - Strong proactive communication: thought process was visible throughout, clarifying questions were well-timed - Brute-force solution was proposed early and used as a written baseline for optimization - Areas for improvement: read the problem output specification more carefully before proposing a return type; dry-run the code proactively rather than waiting to be prompted; consider wrapping the solution in an input-validation function for added polish 👉 Book coaching or watch more mock interviews: https://www.interviewing.io 📝 Interview transcript & feedback: https://interviewing.io/mocks/faang-python-black-hole-quadtree-search 🔗 Explore more FAANG interviews: https://interviewing.io/mocks?company=faang Disclaimer: All interviews are shared with explicit permission from the interviewer and interviewee. All candidates remain anonymous.