{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Building Out Loud","title":"Episode 7: Training agents & initial alpha feedback","html":"","width":"100%","height":180,"duration":737,"description":"Building Out Loud: Initial MVP Feedback & wrestling with Agents \nRandy Silver and Faith Forster catch up after Faith reaches a key milestone: a working MVP that covers the full workflow. \nAfter walking a few “friendly” product leaders through the tool, Faith uses early feedback to sharpen the purpose of each element. She shares strong early demand signals and early indications of willingness to pay.\nFaith explains recent work on AI agents and why it has been harder than expected. Based on user conversations, Faith talks about some of the updates she has already made to deliver even more value. \n00:00 MVP Update: Full Workflow Working & Early Friendly Feedback\n00:50 Sharpening Features: Rethinking the Roadmap Around Impact\n01:38 Demand Signal: 53 Product Leaders Raise Their Hands\n02:19 Will They Use It and Pay? Early Buyer Validation\n04:12 Building with AI Agents: The Pain of Training Web Search & Pricing Scrapes\n05:37 Refactoring the Agent Stack: Router + Choosing Perplexity/OpenAI/Gemini\n06:36 Cadence & Cost: Scheduling Agents and Avoiding Market Noise\n08:01 Competitor Intelligence: Threat Levels, Refreshes, and Instant Battle Cards\n09:06 Assistant vs Autonomous Agents: What’s Hard, What Learns Over Time\n09:39 Evals, Corrections, and Learning Loops in the Product\n11:26 Next Week: Sending 60 Invites, Collecting Beta Feedback, and Messaging Prep\n12:07 Wrap-Up and What’s Coming Next\nLearn more about Discoveree: https://discoveree.com/","thumbnail_url":"https://img.transistorcdn.com/zRtsvl0fP7pXHBBa0YY5Eq9tGJczgYrCbA80W7Cr0VY/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8yYTJm/MDM0NDYxN2QwMGZi/ODhlNGVkNGM0MmVl/MDQwMC5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}