Edwy

Foundational primer

AI Primer

The Phase 0 curriculum path starts here. Source MDX is ported from the approved primer artifact.

25 modules

View local reader progress

Agent loop demo

A placeholder sandboxed JavaScript demo for showing the shape of an agent loop.

const steps = ['plan', 'act', 'observe', 'review'];
for (const step of steps) {
  console.log('agent loop:', step);
}
Run the demo to see sandboxed console output.

Orientation

  1. Module 1Why this matters right nowAI in 2026 is no longer a research curiosity. It is the most consequential technology shift of our working lifetimes — comparable in scale to the internet, mobile, and cloud combined.
  2. Module 2Roadmap for the next 25 slidesI

Probability

  1. Module 3PART IProbability
  2. Module 4Probability — the basicsA probability is a number between 0 and 1 that expresses how strongly we believe something will happen.
  3. Module 5Conditional probability + Bayes’ ruleThe single most important idea in modern AI is updating beliefs in light of evidence.

Statistics

  1. Module 6PART IIStatistics
  2. Module 7Estimation, sampling, and biasEstimation
  3. Module 8The fundamental machine-learning questionGiven examples of inputs and the outcomes they produced,

Machine learning

  1. Module 9PART IIIMachine learning
  2. Module 10Supervised learningLearn from input–output pairs.
  3. Module 11Unsupervised learningFind structure in data without labels.
  4. Module 12Reinforcement learningLearn to act by trial and error, guided by reward.

Neural networks

  1. Module 13PART IVNeural networks
  2. Module 14The neuron, the layer, the networkA neuron is a weighted sum of its inputs, passed through a non-linear squashing function.
  3. Module 15Why depth changed everythingShallow models can represent simple patterns. Deep models — many stacked layers — discover hierarchical representations: edges → textures → parts → objects → meaning.

Transformers and LLMs

  1. Module 16PART VTransformers + LLMs
  2. Module 17The attention mechanismWhen reading a sentence, you don’t weight every word equally. You attend to the words that matter for understanding the next one.
  3. Module 18Transformers → Large Language ModelsStack attention layers, train on a large fraction of the public text on the internet, and remarkable things happen.

Agentic AI

  1. Module 19PART VIAgentic AI
  2. Module 20From language model to agentAn agent is an LLM wrapped in a loop that can use tools, maintain memory, plan, and execute multi-step work.
  3. Module 21Multi-agent systemsOnce you have one agent, the next step is many. They specialise, coordinate, escalate, and audit each other.
  4. Module 22Why safety layers matter (and how they’re built)Autonomous agents can act on the world. The interesting engineering problem is bounding what they do, with what evidence, under whose authorisation.
  5. Module 23The 2026 landscapeFrontier models
  6. Module 24What this means for anyone buildingDistribution beats raw capability
  7. Module 25The one idea to take awayModern AI is the engineering of bounded autonomy.

Continue into a vertical primer

The vertical primers translate the same AI foundations into regulated healthcare, simulation engineering, finance, compliance, fintech, and industrial evaluation work.

Healthcare8 modules
Simulation engineering7 modules
Finance / accounting8 modules
Compliance / SOX / audit7 modules
Fintech / trading8 modules
Industrial / manufacturing7 modules

Wilfrid course paths

Each vertical now has placeholder Wilfrid course paths for readers who want to go deeper after the public primer.

HealthcareWilfrid course catalog
Simulation engineeringWilfrid course catalog
Finance / accountingWilfrid course catalog
Compliance / SOX / auditWilfrid course catalog
Fintech / tradingWilfrid course catalog
Industrial / manufacturingWilfrid course catalog

Talk to Barg Labs

For regulated-industry teams exploring bounded autonomy, share an email and area of interest.

AI Primer | Edwy