Lessons

Learn to build Model Context Protocol servers from scratch using the official Python and TypeScript SDKs. Cover tool schemas, resource URIs, prompt templates, and how Claude Code, Claude.ai, and Cursor consume them.

A deep dive into MCP — Anthropic's open protocol for connecting LLMs to external tools, data, and prompts. Covers JSON-RPC transport layers, the three core primitives, capability negotiation, and how to build a working server from scratch.

A rigorous guide to measuring RAG quality using RAGAS metrics — faithfulness, answer relevancy, context precision, and context recall — plus how to build a golden dataset and recognize where automated metrics fall short.

Go beyond naive RAG pipelines. Learn how Self-RAG, Corrective RAG, and retrieval-as-tool patterns let an LLM decide when, what, and how many times to retrieve — enabling reliable multi-hop reasoning over complex knowledge bases.

Deep dive into two advanced RAG architectures — HippoRAG's hippocampal-inspired knowledge graph indexing and RAPTOR's recursive summarization tree — and why both dramatically outperform flat vector retrieval on multi-hop questions.

Go beyond dense-vector search: learn how Microsoft GraphRAG extracts entities, builds a knowledge graph, clusters it with the Leiden algorithm, and serves both local and global queries with community summaries — delivering answers that classic RAG cannot.

Master four advanced query rewriting techniques that dramatically improve RAG retrieval quality: Hypothetical Document Embeddings, multi-query expansion, query decomposition, and step-back prompting. Learn when to reach for each and how to implement them.

Build a production-grade retrieval pipeline combining BM25 keyword search with dense vector search, then sharpen precision with cross-encoder rerankers (Cohere Rerank, Voyage rerank-2, BGE). Learn when each layer matters and how to wire them together.

A deep dive into how you split documents for retrieval-augmented generation — fixed-size, recursive, semantic, hierarchical, and late chunking — with concrete trade-offs and code for each approach.

Understand how Valve's Steam became the default PC storefront, how its tiered revenue splits work, why wishlists and reviews govern discovery, and whether the Epic Games Store's 12% cut has actually threatened Steam's position.

Build the vocabulary that underlies all of ML: sample spaces, discrete and continuous random variables, PMFs, PDFs, and CDFs. Then tour the key distributions — Bernoulli, Binomial, Categorical, Gaussian, Poisson, Exponential, Uniform — with their parameters, mean, variance, and exactly when each appears in practice.

Master the three numbers that summarize any distribution: mean, variance, and standard deviation. Derive linearity of expectation, understand covariance and correlation, then see why the Central Limit Theorem makes the Gaussian unavoidable — with a worked numeric example from scratch.

From raw data to defensible conclusions: derive Maximum Likelihood Estimators for Bernoulli and Gaussian, understand bias-variance in estimation, construct confidence intervals, and learn what p-values actually say — and don't say — including the most common misinterpretation that has corrupted thousands of papers.

Understand what it really means to update beliefs with data. Derive Bayes' theorem from first principles, dissect the roles of prior, likelihood, posterior, and evidence, work through a complete Beta-Binomial conjugate example numerically, and see why the base-rate fallacy trips up even experts.

Go beyond a single database: learn how read replicas offload query load, how range and hash sharding partition data, why consistent hashing makes rebalancing tractable, and how to pick a shard key that avoids the celebrity problem and cross-shard pain.

Master the mechanics of scaling distributed systems: when to scale up versus out, why statelessness is the prerequisite for horizontal scale, how L4 and L7 load balancers differ, which balancing algorithm fits which workload, and how to avoid turning your load balancer into the next single point of failure.

Learn how message queues and pub/sub decouple services, why at-least-once delivery demands idempotent consumers, how backpressure and dead-letter queues protect the system, and when microservices are worth the complexity — with a sequenceDiagram of async order processing.

Understand where caches live, how cache-aside, read-through, write-through, and write-back differ, how to choose eviction policies, and how to prevent cache stampedes — with hit ratio math and a concrete cache-aside code snippet.

Open-loop control is a guess; closed-loop control is a conversation. Learn why feedback transforms an unreliable robot into a reliable one, how the read-compute-actuate cycle works, what microcontrollers and single-board computers each do best, and why loop timing is just as critical as the algorithm running inside it.

A robot is only as good as what it can measure. Explore the full sensor toolkit — encoders, IMUs, ultrasonic rangefinders, LiDAR, cameras, and force/torque sensors — and learn the four metrics that determine whether a sensor is fit for purpose: resolution, range, noise, and sampling rate.

Strip any robot to its bones and you find three things: sensors that gather data, a controller that thinks, and actuators that move. Learn how these pieces fit together through the Sense-Plan-Act paradigm, why reactive control sometimes beats planning, and what makes a Roomba and a welding arm both qualify as robots.

Every robot motion — spinning a wheel, bending a joint, squeezing a gripper — starts with an actuator. Learn the key differences between DC, stepper, and servo motors; how gears trade speed for torque; how PWM lets a microcontroller dial motor power; and where hydraulics and pneumatics step in when electricity isn't enough.

Tabular methods break down when the state space is continuous or astronomical in size. Learn how neural networks extend RL via DQN, how the policy gradient theorem makes it possible to differentiate through stochastic policies, and where actor-critic, PPO, and the deadly triad fit into the picture.

Leave the model behind. Monte Carlo methods wait for a full episode to update; TD methods bootstrap from the very next step. See exactly where SARSA and Q-learning diverge on the on-policy/off-policy axis, and why that single difference changes everything about convergence guarantees.