CS224W is Stanford's machine learning with graphs course: nineteen lectures from node embeddings, GNNs, graph transformers and knowledge graphs to relational deep learning and GNNs with LLMs. The slides, three written homeworks and six Colabs are public and run as downloaded; the public recordings date from 2021, so the newer topics in the second half exist only as slides, and the exam and autograding are out of reach.
CS228 is Stanford's probabilistic graphical models course, in three parts — representation, inference and learning: graphs write down independence assumptions so that exponentially large probability computations become polynomial. What self-learners can get is the 16-chapter public notes, cs228-notes; lecture slides, recordings and the current assignments are not available.
CS229 is the Stanford course that works through the mathematical derivations of machine learning. The Spring 2026 recordings make 17 lectures public: the first ten cover linear models, neural networks, and unsupervised learning, and the last seven go on to diffusion models, LLMs, and training reasoning models with reinforcement learning. This series has one post per lecture, and this post is the map: what each lecture actually teaches, where the video titles don't match, and where to pick up what the lecture notes cover but the lectures skipped.
CS25 is Stanford's 1-unit seminar where attendance is the only homework and anyone can audit. Of the nine talks in the Spring 2026 season, the three worth your time are Albert Gu on the inductive biases of SSMs vs Transformers, Charles Frye on serving inference across thousands of GPUs, and Victoria Lin on what native multimodality still hasn't solved.
CS329Z is Stanford's course on engineering AI agents: you first build RAG, tool use and the agent loop by hand without frameworks, then see what frameworks like DSPy abstract away; the two assignments are building an agent harness and designing an agent evaluation. The syllabus, the assigned readings, the first eleven slide decks, and the starter code, data and public tests for Assignment 1 are available; the recordings require an enrolled Canvas login.
CS336 is Stanford's course on building a language model from scratch: five assignments cover the tokenizer and Transformer, training-systems optimization, scaling laws, data processing, and alignment and reasoning RL, with no code skeleton, only tests. The lectures, recordings, all five assignment repos and the leaderboards are public; from Assignment 2 on you supply your own compute, and the course-sponsored compute and grading are not available.
Tavily exposes Search, Extract, Map, and Crawl through one web API for agents. The free plan includes 1,000 credits per month; basic, fast, and ultra-fast Search cost 1 credit each, while advanced costs 2.
vLLM is the de facto standard for self-hosted LLM inference (89,470 GitHub stars, verified 2026-08-21), built on managing the KV cache the way an OS manages paged memory. But the selection question isn't how fast it is — it's your GPU utilization. Using Red Hat's measured 793 output tokens/second, a fully saturated A100 costs roughly $0.70 per million output tokens; at 10% utilization that becomes $7, more than most cloud APIs.
A web retrieval benchmark must evaluate complete tasks, not HTTP 200s: 30 fixed cases across five failure strata and three live channels, measuring answers, citations, freshness, latency, cost, and unnecessary escalation. This article delivers the harness and gates, but no fabricated ranking while the three live channels remain unconfigured.
An agent should not open a browser for every web task: route first to Search or Fetch, then escalate on explicit signals such as status codes, weak content, JavaScript shells, authentication, or challenge pages, with retry, budget, cache, deduplication, and provenance constraints at every step.
Behavioral interviews aren't about improvisation — they're about a pre-prepared story library. AI Engineer behavioral interviews have unique focus areas: AI ethics (bias, fairness, privacy), technical decision impact narratives (why you chose this model/architecture), and experience driving ML projects across teams. Strategy: build 8-10 STAR stories, practice each until you can deliver it in under 2 minutes.
AI Engineer coding interviews aren't identical to SWE — beyond LeetCode medium, you'll face ML-flavored problems (implementing a tokenizer, writing a batch inference pipeline, handling sparse matrices). Strategy: practice LeetCode medium to 70% pass rate, then spend remaining time on numpy/pandas operations, data processing pipelines, and ML-related programming problems.
Deep learning interviews don't ask you to derive backpropagation — they test whether you can explain the design intuition behind architectures. High-frequency topics: CNN's locality and translation invariance, why the evolution from RNN to Transformer was necessary, self-attention computation and complexity, BatchNorm vs LayerNorm use cases, and common training tricks (learning rate scheduling, gradient clipping, mixed precision).
LLM Application Design is the hottest new interview topic in 2025-2026. Key focus areas: RAG pipeline chunking/retrieval/reranking design, agent tool-use and planning loops, context window management strategies, guardrails and safety design, and LLM application evaluation methods. Interviewers especially value whether you've hit real-world pitfalls.
ML fundamentals interviews don't test formula memorization — they test whether you can explain concepts intuitively and hold up under follow-up questions. High-frequency topics: the practical meaning of bias-variance tradeoff, the selection logic for L1/L2 regularization, why cross-entropy beats MSE for classification, SGD vs. Adam tradeoffs, and how precision/recall priorities differ by scenario.
The core of ML System Design interviews isn't choosing the model — it's how to turn a business objective into a system that's deployable, monitorable, and iterable. Interviewers want to see if you can: translate business goals into ML objectives, design data pipelines and feature stores, choose reasonable serving strategies, and plan monitoring and A/B testing.
MLOps interviews test whether you have experience pushing models to production. Key topics: ML pipeline CI/CD (how it differs from software CI/CD), model registry and version management, A/B testing design and pitfalls, inference scaling strategies (horizontal scaling, model compression, caching), and production monitoring and alerting design.
The dividing line in LLM interviews is whether you've actually used these things. High-frequency topics: BPE tokenization logic and multilingual challenges, pretraining objectives (CLM vs MLM), three levels of fine-tuning (full/LoRA/prompt tuning), RLHF workflow and failure modes, prompting as engineering practice, and the difficulty of LLM evaluation with current methods.
AI Engineer interviews go beyond ML — big tech emphasizes system design and coding, startups look for end-to-end delivery, and AI-native companies test LLM engineering depth. Strategy: identify your target company types first, then allocate prep time across six dimensions (ML fundamentals, system design, LLM applications, coding, paper reading, and behavioral).
Paper reading interviews don't test whether you've read that specific paper — they test whether you can quickly understand a new method and identify its limitations. AI-native companies (Anthropic, OpenAI) particularly favor this format. Strategy: practice reading a paper in 30 minutes and verbally stating contribution + limitation, build your own must-read list, and practice summarizing each paper in three sentences.