{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Medtech Innovation Podcast","title":"Use His Hardware Cheat Code","html":"","width":"100%","height":180,"duration":3632,"description":"I'm joined by Serge Kadjo, Founder & CEO at Wearer Lab dba Productflo.io, as we explore how AI-powered design tools are revolutionizing hardware development and compressing the medtech innovation cycle.From African Robotics Dreams to Global Hardware Innovation→ Serge's journey spans West Africa, military service, and multiple continents (Poland, China, Morocco, France) with exits in smart agriculture and deep tech, culminating in Product Flow's mission to democratize hardware development→ His multidisciplinary background across IoT systems, BCI neurotechnology (partnered with CERN), and medtech sleep devices informs Product Flow's systems-thinking approachHaitch: The AI Design Engine Transforming Ideas Into CAD→ Haitch converts text prompts or engineering drawings into parametric 3D CAD models in minutes, generating complete design briefs with DFM analysis, dimensions, and manufacturing constraints automatically→ Real application: Serge took a client's screenshot, prompted Haitch to design sliding mechanisms, generated three options with exploded views, and produced manufacturable CAD files conversationallyProduct Flow: Breaking Down Engineering Silos→ Consolidates mechanical files, PCBs, code repositories, and drawings into one version-controlled workspace, enabling 75-80% faster development through unified visibility→ Generates dependency trees showing how functions, components, and tasks interconnect—critical for complex systems like drug delivery devices with mechanical, electrical, and software componentsThe AI-Academia Application Gap→ NVIDIA's ML simulation engines perform FEA 10x faster, yet medtech domain experts remain unaware these tools exist because researchers and practitioners occupy separate worlds→ Transit/ship testing and EO residuals involve known variables that computational models could simulate, potentially compressing 3-month, $40K validation cycles into daysSecurity and the Zero-Trust Framework→ Never trust AI providers even when they...","thumbnail_url":"https://img.transistorcdn.com/SKJTlzh1Dih_UjWAMpaf9asEOJluhYL_CM3FATuVvVc/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8yM2U1/NjA3NjAzY2M1NDA0/ZDZkYTRiY2Y3Mjky/MTRmNi5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}