Every AI skills course in one place: lessons, free PDFs and interactive versions, from prompting and context to strategy, AI UX, pricing, governance and more.
What you can do with Claude, Claude Code, ChatGPT and Codex, Gemini, Lovable and other top AI tools, and exactly how: create a website, add pages, connect tools, connect other websites and APIs, publish on your own domain, and run PM workflows end to end.
Make every AI request precise and evidence-based: clear instructions, the right context, structured outputs, task decomposition, long-context compression, reading documents and scans, citing sources and organising knowledge.
Turn your recurring work into reusable AI skills and templates: Claude Agent Skills, Projects, Custom GPTs and Gems, PRD and report templates, a PM skill library, testing skills with evals, and a case study → PRD → presentation skill.
Capture everything you learn, organise it simply and ask AI questions across it: PARA and CODE methods, Notion, Obsidian and NotebookLM, context engineering and AI memory, meeting and reading pipelines, and building your own with MCP and automation.
SQL, Excel, Python, Power BI, statistics and AI for analysts in the Data Science & AI handbook.
Measure, improve and safely launch AI features: criteria, datasets, LLM-as-judge, RAG and agent evals, safety, monitoring.
From record-and-replay to AI agents that use the web for you: Playwright basics, computer-use and browser agents, deep research agents, real PM and analyst workflows, safety, and how to evaluate and productise browser agents.
Run longer AI workflows reliably: workflow patterns, routing and model selection, parallel work, state and checkpoints, human-in-the-loop approvals, error recovery, multi-agent systems, scheduling and triggers, cost control, and production monitoring.
Decide where AI creates real value: AI-first vs AI-added products, moats and data flywheels, build vs buy vs partner, model choice, opportunity assessment and AI roadmaps.
Design AI features people trust and use: interaction patterns (copilot, chat, agent, inline), setting expectations, explainability and citations, feedback and control, failure and handoff design, and testing AI UX.
Make AI products profitable: AI cost structure and unit economics, pricing models (seat, usage, credits, outcome, hybrid), packaging AI features, margin control and go-to-market.
Manage traditional ML products: problem types, data and labelling, metrics, working with data scientists, recommendation and personalisation, the model lifecycle and MLOps monitoring.
Ship AI responsibly: principles and risks, regulations (EU AI Act, India's DPDP Act and AI guidelines, sector rules), AI risk and impact assessments, policies and documentation, and operating governance.
Manage platforms and APIs as products: developer experience, API design decisions, versioning and limits, ecosystems and partners, platform metrics, and AI/MCP platforms.
Help product teams scale: what product ops does, processes and rituals, tools and data, customer-insight pipelines, launch management, and AI in product ops.
Use AI that sees, hears and creates: vision understanding, image generation, video and voice, multimodal product use cases, and rights, safety, deepfakes and content labelling.
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