Developer roadmaps where every node is actually taught.

Interactive graphs of what to learn and in what order. Pick a track and follow the nodes — each one opens an original lesson.

• AI AgentsSoftware that reasons, calls tools, and acts on its own, instead of a model that only answers one prompt. Thirty-three lessons from the agent loop through tools, memory, multi-agent architectures and the frameworks used to ship them, to evaluating and deploying agents safely.Intermediate · 33 lessons · 749 min
• AI EngineerBuilding products on top of models you did not train. Thirty-six lessons from what a token is through retrieval, agents, evaluation and safety, to the work of keeping an LLM feature alive in production.Intermediate · 36 lessons · 571 min
• AI Red TeamingFinding the ways a model, its prompts, and the systems around it can be broken — before someone else does. Twenty-four lessons from prompt injection and jailbreaks through model extraction, poisoning, and agentic exploits, to the testing methodology and defenses a real engagement runs on.Advanced · 24 lessons · 548 min
• Inference EngineeringRunning a trained model efficiently in production, not training one. Thirty-two lessons from latency and throughput through hardware, serving software and the techniques that speed generation up, to running it all at scale.Advanced · 32 lessons · 983 min
• Machine LearningBuilding and training models from data, not calling ones someone else trained. Thirty-three lessons from what learning from data means through classical models, evaluation and neural networks, to where deep learning picks up.Intermediate · 33 lessons · 429 min
• MLOpsTaking a trained model from a notebook to something that keeps running in production: versioning data, tracking experiments, building the pipelines that retrain and redeploy it, and watching it once it is live.Intermediate · 32 lessons · 599 min
• Prompt EngineeringMaster the art and science of crafting effective prompts for large language models. From basics to advanced techniques, learn how to communicate with LLMs to get reliable, structured outputs at scale.Beginner · 29 lessons · 319 min

No tiers, no paywalls, no upgrade nags — one hand-picked video per topic, turned into a proper lesson, open to anyone who wants it. 7 of wave one's 9 are live; the rest are in active production.

The maths the lessons lean on, each one written out, glossed in plain English, and cross-linked to the topics that use it.

• $P(A \mid B) = \frac{P(B \mid A)\, P(A)}{P(B)}$Bayes' theoremProbability
• $\mathrm{Attention}(Q, K, V) = \mathrm{softmax}\!\left(\frac{QK^{\top}}{\sqrt{d_k}}\right) V$Scaled dot-product attentionDeep learning
• $H(X) = -\sum_{x} p(x) \log p(x)$Shannon entropyInformation theory
• $\theta_{t+1} = \theta_t - \eta \nabla_{\theta} L(\theta_t)$Gradient descentOptimization
• $A = U \Sigma V^{\top}$Singular value decompositionLinear algebra
• $M_{\text{KV}} = 2 \cdot L \cdot H \cdot d_h \cdot S \cdot B \cdot b$KV-cache sizeInference

• Linear algebra10
• Calculus10
• Probability13
• Information theory10
• Optimization10
• Deep learning13
• Reinforcement learning10
• Inference11