The Growth Podcast

Nadav Abrahami, Wix co-founder now leading Dazzle (AI prototyping and visual editor founder), walks through AI-driven product prototyping. He demos building a LinkedIn sentiment feature from screenshot to multi-page clickable prototype. Topics include starting from your design system, rapid divergent variants, visual editing vs code, user testing, and handing off production-ready code and specs.

Frank Lee, Principal Product Manager at Amplitude who builds agent-enabled analytics and MCP integrations. He demos CloudCode + MCP workflows, automatic chart analysis, and end-to-end vibe PMing. Short, concrete showcases include dashboard agents, feedback synthesis across tools, and turning insights into PRDs and tickets.

Xinran Ma, AI product and design expert who runs the Design with AI newsletter and teaches AI-for-design workflows. He breaks down AI design into prompting, ideation, prototyping, workflows, and conscious checks. Hear demos of custom GPTs that generate PRD-style specs, rapid prototyping tools like Lovable and Google AI Studio, and workflows for redesigning existing pages with high-variability options.

Ankit Shukla, an AI product management educator who trains thousands of PMs, breaks down AI evals from first principles. He covers why PMs need eval skills, how to evaluate non-deterministic models, and a job-site case study. Short takes on eval types, guardrails, metrics, prototyping pitfalls, and production monitoring keep the conversation practical and actionable.

Caitlin Sullivan, a user research instructor who specializes in AI-powered discovery, walks through live demos and step-by-step workflows. She covers per-participant analysis, multi-step prompting, coding surveys before counting, auditing AI outputs, model trade-offs (Claude vs Gemini), and using markdown and agentic tools to scale rigorous research.

Mike Bal, Head of Product at David's Bridal leading a large digital transformation, builds AI-native product workflows. He explains replacing 20-tool chaos with a single interface using Cursor and Claude Desktop. He demos Model Context Protocol integrations, rapid design vs requirement checks, Manus for traceable research, and smooth prototype-to-code flows.

Naman Pandey, AI product practitioner and podcaster (Ready Set Do), has hands-on experience testing AI agentic browsers. He compares ChatGPT Atlas, Perplexity Comet, and Arc Dia. Expect demos of research, scraping, form-fills, price checks, and workflow automations. He highlights strengths, weaknesses, install steps, and practical PM use cases in short, demo-driven segments.

This is a free preview of a paid episode. To hear more, visit www.news.aakashg.comToday’s EpisodeWhen you first start using AI prototyping tools, you get wowed.You type “create me a CRM application” and boom a fully functioning app appears in 60 seconds.But here’s the problem.It looks generic. The styling is basic. The features are vanilla. You’d never ship this to customers.This is AI slop.Sachin Rekhi was the former Head of Product at LinkedIn Sales Navigator. He’s now teaching thousands of PMs at Reforge how to master AI prototyping.----Brought to you by - Reforge:Get 1 month free of Reforge Build (the AI prototyping tool built for PMs) with code BUILD----Key Takeaways:1. Product shaping changes everything - Anthropic builds multiple prototypes for every problem, launches internally, sees what people use, then productionizes winners. This used to only be possible at Apple with massive labs.2. AI slop is real - Type "create a CRM" and you get generic styling, vanilla features, basic scenarios. Looks magical but you'd never ship it. The challenge is going from slop to production-grade prototypes.3. The 15-skill mastery ladder - Apprentice level: prompting, editing, design consistency. Journeyman: versioning, debugging, diverging. Master: functional prototyping, product shaping, analytics integration.4. Design consistency starts with baselining - Take screenshot of your product. Recreate it. Iterate until perfect. Save as template. Now every prototype inherits your design system automatically.5. Diverging is the secret weapon - Generate 4 design variants instead of 1. Magic Patterns has this built in. Or use multiple tools to get 8 options. Evaluate alternatives like designers do.6. Functional prototypes unlock real validation - Integrate OpenAI API for actual responses. Add PostHog for session recordings and heatmaps. Build surveys. Track clicks. Test with real data, not mockups.7. The tools face-off: which to actually use - Bolt for speed. V0 for beautiful UIs. Replit for full-stack. Magic Patterns for product teams with diverging. Reforge Build for context integration. Cursor for technical PMs.8. The $5/month unlimited execution hack - Host n8n on Hostinger instead of paying per execution. Get unlimited runs. Build workflow that backs up to Google Drive for version history.9. PMs can build what used to require engineering - Calendar integration. Email agents. Analytics dashboards. Multi-model comparison. Survey collection. All from prompts. No code required.10. Traditional workflows beat agents for production - Workflows save tokens, run faster, and are more reliable. Use agents only when tasks need real decision-making. For known processes, use workflows.----Where to Find Sachin* LinkedIn* Newseltter* X* Youtube* Reforge cources----Related ContentNewsletters:* AI Product Strategy* How to Build AI Products* AI Prototype to Production* AI Prototyping Tutorial* Product Requirements DocumentsPodcasts:* AI Prototyping for PMs* How to Become an AI PM* Everything You Need to Know About AI----PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!If you want to advertise, email productgrowthppp at gmail.

Colin Matthews, an AI product builder and prototyping expert, joins to discuss the future of ChatGPT apps. He explains how these apps could revolutionize user interaction by embedding branded solutions directly into chats. Colin highlights the benefits of integrated distribution compared to traditional app stores and explores various consumer use cases, from to-do lists to healthcare tools. He also emphasizes the importance of app testing, metadata optimization, and team structure for success in this emerging landscape.

Hamel Husain and Shreya Shankar, experienced instructors in AI evals, share their expertise on building reliable production AI. They discuss the critical importance of systematic evaluations over simple demos, emphasizing real-world error analysis. Listeners learn about analyzing real traces, identifying UX failures, and refining categories for actionable insights. The duo highlights the need for tailored evaluations and proper validations, advocating for structured methodologies that prioritize high-impact issues. This engaging tutorial is a must for aspiring PMs in AI.