Interview Prep
Everything pointed at the interview loop — AI and system-design handbooks, language Q&A, the DSA roadmap and runnable coding challenges in one place.
Handbooks 6
The Agentic AI Interview Handbook
Twenty topics every senior AI engineer should be able to reason about live — from eval pipelines to reliability patterns for generative systems.
The Senior AI Engineer Interview Handbook
60 questions across architecture, production incidents, agentic systems, RAG, evals, cost, safety, and leadership — what staff-level AI interviewers actually probe for.
50 Angular Interview Questions
A visual handbook covering components, change detection, RxJS, signals, routing, forms, performance, and testing — what interviewers actually probe for in senior Angular roles.
50 Python Interview Questions
Fundamentals to advanced: data structures, OOP, iterators & generators, the GIL, asyncio, memory, testing, and the standard library — a visual walk through everything a Python interview touches.
51 LLM Evals Interview Questions
Golden sets, LLM-as-judge, regression testing, offline vs online evals, RAG evals, agent evals, red-teaming, and observability — demystified for interviews and production.
The Agent Evaluations Handbook
A self-contained handbook on evaluating AI agents — theory, interactive widgets, and practical guidance. Trajectory evals, tool-use scoring, LLM-as-judge, observability, and reliability for PMs, engineers, and founders.
Roadmaps 1
Coding Challenges 7
Two Sum
The classic warm-up: find the two numbers that add up to a target. Brute force is O(n²) — a hash map gets you to one pass, O(n). Solve it in Python or TypeScript, right in your browser, with hidden tests and a reveal-solution button.
Valid Parentheses
The canonical stack problem: decide whether every bracket is closed by the right type, in the right order. A stack turns nested matching into a single pass. Solve it in Python or TypeScript with hidden tests.
Fizz Buzz
The famous screening question. Print 1…n, but multiples of 3 become "Fizz", of 5 become "Buzz", and of both become "FizzBuzz". Easy — the catch is testing divisibility in the right order. Solve it in Python or TypeScript.
Softmax
The function at the end of every classifier and language model: turn raw scores (logits) into a probability distribution. Implement the numerically stable version so big logits do not overflow. Solve it in Python or TypeScript.
Cosine Similarity
The measure behind every embedding search and RAG system: how aligned are two vectors, ignoring their length? Dot product over the product of magnitudes — 1 identical, 0 orthogonal, -1 opposite. Solve it in Python or TypeScript.
Top-K Retrieval
The core of the "R" in RAG: given a query embedding and a set of document embeddings, return the indices of the k most similar docs by cosine similarity, with a stable tie-break. Solve it in Python or TypeScript.
Token-Level F1
The metric behind QA evaluation (SQuAD and friends): how well does a predicted answer overlap a reference as a bag of words? Compute token precision and recall, then their harmonic-mean F1. Solve it in Python or TypeScript.