ai

Given that the tradable-signal path is narrow and hard to evidence, the systems worth building are the ones the first lesson identified: extraction at scale. This lesson covers the engineering that makes them survive audit, the evaluation that does not depend on returns, and the governance obligations that apply once a model touches a regulated process.

The popular framing is a model that predicts prices. That is the one job the technology is worst suited to, and it obscures the one it is genuinely good at: turning unstructured text into structured data at a scale that was previously unaffordable. This lesson locates LLMs against the trading stack and rules out the places they cannot go.

A model that predicts well can still be impossible to justify. This lesson maps explainable AI: interpretable-by-design versus post-hoc, global versus local, model-specific versus model-agnostic. It covers the global workhorses (permutation importance, partial dependence, ICE), faithfulness versus plausibility, and the argument that post-hoc explanation is the wrong tool for high-stakes decisions.

Training an EBM means shaping an energy landscape so real data sits in valleys, but the intractable partition function blocks plain maximum likelihood. This lesson covers the push-down-push-up principle, contrastive methods (contrastive divergence, noise-contrastive estimation), score matching, and the regularized alternative that avoids sampling entirely.

Why LeCun treats energy as the common language of machine learning. This lesson shows how classification, generative models, self-supervised learning, JEPA, and diffusion all read as energy-based models, ties the contrastive-versus-regularized split back to self-supervised learning, and gives an honest account of where explicit EBMs help and where they do not.

Energy-based models replace input-to-output functions with a scalar energy that scores how compatible a configuration is. This lesson covers the energy function, inference as finding the lowest-energy answer, the energy landscape picture, the link from energy to probability via the partition function, and why this framing handles problems with many valid answers.

How to prevent collapse without negative samples. This lesson covers distillation methods (BYOL, SimSiam) that rely on stop-gradients and asymmetry, regularization methods (Barlow Twins, VICReg) that constrain the embedding's statistics, and the masked-modeling family (MAE, BERT), then ties them to JEPA and the energy-based view.

The first family to make joint-embedding self-supervised learning work: pull together two views of the same image, push apart different images. This lesson covers the InfoNCE loss, SimCLR and its reliance on large batches, MoCo's momentum encoder and queue, why negatives block collapse, and the practical costs that motivated negative-free methods.

Self-supervised learning turns unlabeled data into its own teacher. This lesson covers why labels are the bottleneck, how a pretext task manufactures free supervision, the shift from predicting pixels to learning embeddings invariant to augmentation, and the collapse problem that every method after it must solve.

Hundreds of billions of dollars a year are being spent on AI infrastructure. Is that rational investment or a bubble? This lesson gives you the tools to reason about it: what capex is and why it dwarfs AI revenue today, the depreciation trap of fast-aging chips, what circular financing means, and the two coherent cases, for and against, so you can judge for yourself.

The surprise of the AI buildout is that the scarcest resource is not chips or money but electricity, and the grid was not built for this. Learn why power became the binding constraint, why connecting a big new load takes years, why data centers are turning to gas and nuclear, and why an unglamorous device, the transformer, quietly gates the whole thing.

An AI data center is not a warehouse of ordinary computers; it is a single supercomputer built from tens of thousands of specialized chips wired together. Learn why GPUs beat regular processors for AI, why the network between chips matters as much as the chips, why one company dominates the market, and the cost structure that makes these buildings so expensive.

Modern AI got better mainly by getting bigger, and bigger means more computation, which is why the story of AI is now a story about hardware, power, and money. Learn what compute actually is, why scaling laws made more of it pay off so reliably, the difference between training and inference demand, and why this buildout is unlike previous technology booms.

How platforms decide what stays up, and why nobody can reliably tell you whether something was made by AI. Covers classification at scale and the precision-recall tradeoff, why AI detectors accuse innocent people at rates that make them unusable, and how provenance approaches like C2PA and SynthID try a different answer, plus where they break too.

A practical, honest account of using generative models to make social content. Covers the jobs they genuinely do well, the specific ways they fail, why a model that sounds confident can still be wrong, the disclosure rules that now apply in the EU, and why the tool that removes effort also removes the thing that made your work worth watching.

Before the tools, the map. AI does three separate jobs on a social platform: it decides what you see, it helps make the content, and it decides what stays up. Learn what each job actually is, why they are built and judged differently, and why the same word covering all three causes most of the confusion about AI and social media.

You already let Copilot autocomplete your code. This lesson dives into the underused power features: chat participants, slash commands, Copilot Edits, agent mode, custom instructions, prompt files, the model picker, MCP servers, and Copilot Spaces.

Explore Retrieval-Augmented Generation (RAG), a powerful technique that enhances Large Language Models (LLMs) by grounding their responses in external, up-to-date, and domain-specific information, mitigating hallucinations and improving factual accuracy. This lesson covers its core components, workflow, and practical considerations.

Dive into the world of vector databases, specialized systems designed to store and query high-dimensional vector embeddings efficiently. Learn how these databases power semantic search, recommendation systems, and large language model applications by finding semantically similar data points at scale.

A beginner's guide to LangChain, the popular framework for composing applications with Large Language Models. Learn the core concepts of Models, Prompts, and Chains, and build a simple application using the LangChain Expression Language (LCEL).