Master AI before it masters your roadmap
A continuously updated, practitioner-built school for developers and engineers who want a clear, honest map of the AI landscape — and the hands-on skills to build with it right now.
My job isn't to excite you about AI — it's to give you the framework to evaluate it clearly and the skills to build with it confidently.— Freddy Foster
What you'll learn
What you'll be able to do
- Accurately map the current AI landscape — key models, labs, and paradigms — and explain how it has shifted over the past 12 months
- Evaluate new AI breakthroughs critically, distinguishing genuine capability leaps from marketing noise
- Integrate leading AI APIs and open-source models (LLMs, vision, multimodal) into real software projects
- Design and deploy prompt engineering and RAG pipelines that solve concrete business problems
- Identify the ethical, legal, and geopolitical pressures shaping AI development and their impact on product decisions
- Build a personal learning system to keep pace with AI advances long after the course ends
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
6 modules · 26 lessons
The Current AI Landscape
Builds a clear, accurate mental map of today's AI ecosystem — key models, labs, and paradigms — so developers can orient themselves before diving deeper.
- 1.1How We Got Here: A Developer's History of Modern AIIncluded
- 1.2Who's Building What: Labs, Models, and Power PlayersIncluded
- 1.3The Last 12 Months: What Actually ChangedIncluded
- 1.4AI Paradigms Decoded: Generative, Discriminative, Multimodal, and AgenticIncluded
Critical Evaluation: Cutting Through the Hype
Equips developers with a practical framework for reading benchmarks, research papers, and press releases with healthy scepticism.
- 2.1How to Read a Benchmark Without Being MisledIncluded
- 2.2Anatomy of an AI Announcement: Signal vs. NoiseIncluded
- 2.3Reading Research Papers as a PractitionerIncluded
- 2.4Case Studies: Breakthroughs vs. Overhyped MomentsIncluded
Building with AI APIs and Open-Source Models
Gives developers hands-on experience integrating leading commercial APIs and open-source models into real software projects.
- 3.1Choosing the Right Model for the JobIncluded
- 3.2Integrating LLM APIs into a Real ApplicationIncluded
- 3.3Working with Vision and Multimodal ModelsIncluded
- 3.4Running Open-Source Models Locally and in the CloudIncluded
- 3.5Evaluating and Testing AI-Powered FeaturesIncluded
Prompt Engineering and RAG Pipelines
Takes developers from prompt basics to production-grade Retrieval-Augmented Generation systems that solve real business problems.
- 4.1Prompt Engineering FundamentalsIncluded
- 4.2Advanced Prompting: Structured Output, Tool Use, and AgentsIncluded
- 4.3How RAG Works: Embeddings, Vector Stores, and RetrievalIncluded
- 4.4Building a Production RAG PipelineIncluded
- 4.5Debugging and Improving RAG and Agent PipelinesIncluded
Ethics, Law, and the Geopolitics of AI
Examines the ethical, legal, and geopolitical forces shaping AI development and how they translate into real product and career decisions.
- 5.1AI Ethics in Practice: Bias, Fairness, and HarmIncluded
- 5.2The Global AI Regulatory LandscapeIncluded
- 5.3Geopolitics of AI: Chips, Data, and National StrategyIncluded
- 5.4Responsible AI in Your Own ProductsIncluded
Staying Current: Building Your Personal AI Learning System
Helps developers build a sustainable, personalised system for tracking AI advances and continuously levelling up long after the course ends.
- 6.1The Firehose Problem: Filtering the AI Information StreamIncluded
- 6.2Curating Your Signal Stack: Sources, Tools, and RoutinesIncluded
- 6.3Learning by Building: Keeping Skills Sharp Through ProjectsIncluded
- 6.4Contributing to the Community and Building in PublicIncluded
Who it's for
Is this you?
Backend engineers going AI-first
You're comfortable with APIs and system design but want a principled, practical path into LLM integration and RAG — without wading through hype or toy tutorials.
Tech leads making model decisions
You're the person in the room who needs to evaluate whether that new model, framework, or AI feature is worth the engineering investment — and say so with confidence.
Full-stack devs adding AI features
You're already shipping product and need to integrate vision, multimodal, or LLM capabilities into a real codebase — not a Jupyter notebook demo.
Early-career engineers future-proofing
You're a few years into your career and want to build the AI literacy and hands-on skills that will define the next decade of software engineering.
Product-minded engineers in AI-adjacent roles
You sit at the intersection of engineering and product, and need to critically evaluate AI capabilities and constraints to make better roadmap calls.
Self-taught builders in emerging tech hubs
You're building in Bengaluru, Jakarta, Manila, or beyond, and want AI education that's grounded in the infrastructure and market realities you actually face.
Questions
Frequently asked
Your teacher
A note from your teacher
Freddy Foster
If you're a developer who's felt genuinely behind on AI in the last year or two — not because you're lazy or uninterested, but because the pace is relentless and most of the content out there is either way too basic or buried in academic notation — I built this school for you.
I know what it's like to sit in a planning meeting where someone drops a model name you've never heard of, or to read a breathless announcement about a "breakthrough" and have no reliable framework to decide whether it actually changes anything you're building. The AI information environment is genuinely difficult to navigate. Most sources are optimised for engagement, not accuracy. Benchmarks are routinely gamed. "State of the art" has become nearly meaningless. And if you're building software in India or across South or Southeast Asia, a lot of the content out there doesn't map cleanly to the infrastructure, regulatory, or market realities you're actually working within.
What I've tried to do here is give you the map I wish I'd had: a clear, honest picture of where AI actually is right now, how we got here, who the real players are, and what the last 12 months genuinely changed versus what was just well-distributed hype. Then the practical skills to build with it — integrating APIs, choosing the right model for a real use case, designing RAG pipelines that hold up in production, debugging agents when they go sideways. And then — because this field will keep moving long after you finish the last lesson — a system for staying current that doesn't require you to spend three hours a day reading Twitter.
I'm not going to oversell what AI can do. I'm also not going to dismiss it. The honest position is that it's a genuinely powerful set of tools that require genuine engineering judgement to use well. That judgement is exactly what this school is designed to build. Come in curious, come in sceptical, and come ready to build things. That's all I ask.
— Freddy Foster
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- 6 modules, 26 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
- Full access for as long as you're subscribed