Ryan Carson is a five-time founder who has spent the past 20 years building, scaling, and selling startups. In this episode, he shares his playbook for using AI to build products, turning “vibe coding” into a structured and scalable approach that can replace full engineering teams. What you’ll learn: 1. A simple three-file system that transforms chaotic AI coding into a structured, reliable process 2. How to create AI-generated PRDs and task lists that actually work 3. A step-by-step workflow using Cursor to build features systematically 4. Why slowing down to provide proper context is the secret to speeding up your AI development 5. How to use model context protocols (MCPs) to extend your AI’s capabilities beyond just coding 6. Why founders can now build entire companies with minimal engineering teams and how Ryan is doing it himself https://youtu.be/fD4ktSkNCw4?si=cbVp__wJrmyEPObt https://www.ryancarson.com/about https://github.com/snarktank/ai-dev-tasks Tools referenced: • ChatGPT: https://chat.openai.com/ • Claude: https://claude.ai/ • Gemini 2.5 Pro: https://deepmind.google/models/gemini/pro/ • Repo Prompt: https://repoprompt.com/ • Task Master: https://github.com/eyaltoledano/claude-task-master/blob/main/docs/tutorial.md • Browserbase: https://browserbase.com/ • Stagehand: https://docs.stagehand.dev/integrations/mcp-server #AICoding #RyanCarson #VibeCoding #ChatGPT #Claude #Gemini2.5Pro #RepoPrompt #TaskMaster #Browserbase #Stagehand #Cursor #AIAgent #ProductDevelopment #NoCode #LowCode #EngineeringTeams #PRD #ModelContextProtocols #AIWorkflow #SoftwareDevelopment #AIDrivenDevelopment #ThreeFileSystem #StructuredCoding #AIScaling

So whats the next big fad .... "Context Engineering". So WHAT IS CONTEXT ENGINEERING? 𝟭. It’s Feature Engineering—but for AI Agents. (*Check what's feature engineering down below) 𝟮. The art of filling the context window with exactly what’s needed. 𝟯. You’re managing “working memory” like an operating system manages RAM. 𝟰. Agents need engineered context: instructions, tools, memories, examples, feedback. 𝟱. Poor context = forgotten steps, broken tools, bad decisions. 𝟲. Long-running agents hit context limits fast—engineering is essential. 𝟳. Vibe coding doesn’t scale—context engineering does. In the context of AI prompting, feature engineering refers to the process of creating, selecting, and transforming input data (features) to improve the performance of a machine learning model. It involves using domain expertise and various techniques to make the data more suitable for the AI model to learn from and generate better outputs. Check the following links for context: - Langchain Explanation on what it is: https://youtu.be/4GiqzUHD5AA?si=BEIThE_HOT-i3I9T - First post l saw talking about this was by Andrej Karpathy (@karpathy): https://x.com/karpathy/status/1937902205765607626 - And you may wish to follow this lady she dove right into it detail ... never mind hte course it hella expensive !: https://x.com/MaryamMiradi/status/1940810454013518178 #contextengineering #featureengineering #aiagents #langchain #promptengineering #aioptimization #machinelearning #contextwindow #workingmemory #aiprompting #vibecoding #aiperformance #contextualization #aitraining #aiscaling