Before a Cloudflare app goes live, do not stop at a successful deploy. Check Custom Domains, Routes, www/root redirects, maintenance pages, CPU/memory/subrequest limits, log sampling, and fallback paths. This appendix turns the Edge Platform series into a production checklist.
Cloudflare Email Service connects transactional email, magic links, notifications, and inbound routing to Workers. Arbitrary outbound sending currently requires Workers Paid; inbound routing is available on Free and Paid, with DNS, quota, message-size, bounce, and anti-spam limits still shaping the design.
Hyperdrive solves the latency and connection-pooling problem when Workers connect to existing Postgres / MySQL databases. It uses edge connection setup, database-near pooling, and read query caching so a regional database works better with global Workers.
Cloudflare Images has two paths: transform images stored in R2/S3/origin at the edge, or store images in Images and deliver named variants. The first is priced by unique transformations; the second also involves stored and delivered images.
Workers Observability is for debugging and request tracing; Workers Analytics Engine is for high-cardinality product events and custom metrics; GraphQL Analytics API is for querying existing Cloudflare product data. Keeping those roles separate prevents logs from becoming a database and keeps billing, monitoring, and product analytics from blending together.
Smart Shield is Cloudflare's origin protection bundle: Smart Tiered Cache, connection reuse, Argo Smart Routing, Regional Tiered Cache, Cache Reserve, Health Checks, and Dedicated CDN Egress IPs reduce requests and connections reaching your origin.
Turnstile is Cloudflare's CAPTCHA alternative: the client widget generates a token, and the server must validate it with the Siteverify API. Tokens expire after 300 seconds and are single-use; a widget without server validation is incomplete.
Cloudflare Workflows turns multi-step Workers processes into durable steps: each step can retry, sleep, wait for events, and register rollbacks, while instances can be inspected, paused, resumed, or terminated. Queues fit single-step background work; Workflows fit long processes that must remember progress.
Week 1 is about getting a program to derive new facts from known ones: propositional symbols and five connectives turn knowledge into sentences; model checking enumerates all 2^n truth assignments to decide whether the knowledge base entails a query; inference rules and resolution work on the sentences directly, converting to CNF and deriving the empty clause by contradiction; first-order logic cuts the number of symbols with constants, predicates and quantifiers. With time links for every section, plus the approach, common mistakes and self-checks for the Knights and Minesweeper projects (reference implementations folded away).
Week 2 is about probability: conditional probability and Bayes' rule turn evidence into probabilities; a Bayesian network uses arrows to split a joint probability into one conditional probability table per node, inference by enumeration is exact but slow, and rejection sampling and likelihood weighting approximate it with samples; a Markov chain describes a state that changes over time with a transition model, and a hidden Markov model adds a sensor model to work back from observations to states you cannot see. With time links for every section, plus the approach and checks for the Heredity and PageRank projects.
Week 3 covers three ways to write an optimization problem. Local search keeps a single state: hill climbing moves to a better neighbor and gets stuck at local optima, and simulated annealing accepts a worse move with probability e^(ΔE/T). Linear programming writes the problem as a linear cost function plus linear constraints and hands it to an existing solver. Constraint satisfaction problems shrink domains with node consistency and arc consistency (AC-3), then find a solution with backtracking search guided by the MRV, degree and least-constraining-value heuristics. Includes per-section time links, plus the approach, pseudocode, pitfalls and checks for the Crossword project (the reference implementation is folded at the end).
Week 4 covers three forms of machine learning. Supervised learning fits a hypothesis function to labeled data: classify by the k nearest neighbors, or draw a decision boundary with a set of weights (perceptron learning rule, logistic regression, support vector machines), then use loss functions, regularization and cross-validation against overfitting. Reinforcement learning uses Q-learning to update the estimated value of each state and action from rewards, with ε-greedy keeping some probability of random exploration. Unsupervised learning clusters with k-means. With time links for every section, plus the approach and ways to check your work for the Shopping and Nim projects.
Week 5 is about neural networks: a unit multiplies its inputs by weights, adds a bias and applies an activation function; gradient descent adjusts the weights one small step at a time, and backpropagation passes the error back so hidden layers can be trained; for images, convolution and pooling come first and an ordinary network follows, which is a CNN; for sequences, an RNN feeds its output back into itself. With time links for every section, plus the approach, common mistakes and checks for the Traffic project.
Week 6 is about getting computers to handle natural language: parse sentences with hand-written context-free grammar rules, then switch to statistics, from n-grams and Markov-chain text generation to a Naive Bayes classifier for review sentiment; turn words into vectors with word2vec, process whole sequences with an RNN encoder–decoder, and finish with attention and the Transformer. Includes section-by-section time links, plus an approach, common mistakes and ways to check your work for the Parser and Attention projects.
A single hidden layer can in principle approximate any continuous function (universal approximation), but width can blow up exponentially with dimension; Barron's theorem lets error decay as 1/sqrt(n) independent of dimension for a specific function class; and depth yields exponential width savings on compositional functions — that is the real reason deep beats shallow.
A GNN is essentially 'an MLP with local message passing on a graph' — it generalizes CNN's fixed-grid neighborhood to arbitrary topology. It must satisfy permutation equivariance/invariance. In theory, a first-order GNN's expressiveness is bounded by the Weisfeiler–Lehman graph isomorphism test: some structures it can never tell apart, which is exactly the gap GIN, positional encodings, and subgraph tricks later fill.
Lecture 1 is the 6.7960 opener: deep learning took off because data + compute + algorithms matured together; the course threads from architectures (CNN/GNN/Transformer) through training, representation, generation, transfer, scaling, and LLMs; ends with a ~30-line PyTorch training loop to confirm your environment works.
Optimization is not an isolated numerical problem: view SGD spectrally, the magnitude of weight updates determines feature learning; Maximal Update Parameterization transfers LR/init across width, and the critical batch size sets the marginal return of trading compute for convergence.
A Transformer is not an architecture from nowhere: tokens discretize data, attention does soft aggregation, positional codes restore order. Seen next to MLPs/CNNs/GNNs, all of them are special cases of 'weighted aggregation over neighbors'.
Training neural nets is closer to engineering than magic: look at the data, overfit a mini-batch to prove capacity exists, then regularize back the generalization; learning rate is always the highest-leverage knob.