Early Adoptr

Early Adoptr

Early Adoptr helps founders and small business owners cut through AI jargon and turn real tools into real results. Hosted by Jess and Kyle, startup founders themselves, this podcast breaks down AI in plain English: what works, what’s hype, and how to use AI to grow faster, work smarter, and build your unfair advantage. No buzzwords. No confusion. Just practical, step-by-step guidance you can implement today. Hosted on Acast. See acast.com/privacy for more information.

  1. 5 GG FA

    So You're Thinking of Breaking Up with ChatGPT: A Practical Guide to the Alternatives

    The QuitGPT movement has been spreading across Reddit and Instagram, with people canceling their ChatGPT subscriptions for reasons ranging from political concerns to product frustration to simple curiosity about what else is out there. Whatever you think of the movement itself, it has done something actually useful: it has made a lot of people stop and ask whether ChatGPT is actually the best tool for what they need. In this episode, Kyle and Jess break down four of the strongest ChatGPT alternatives — Perplexity, Gemini, Mistral, and Claude (yes, we know about DeepSeek and Grok and we have reasons for not covering them) — covering what each one is actually good at, who it is for, and where it falls short. This is not a ChatGPT takedown. It is a practical guide to understanding the alternatives, and why sometimes ChatGPT isn't the best tool for the job. If you are a founder, operator, or small business owner who has been defaulting to ChatGPT out of habit, this episode will help you make a more deliberate choice. For a deep dive into Claude Co-work, check out our recent episode - https://shows.acast.com/early-adoptr/episodes/claude-cowork-explained-can-ai-really-organize-your-files-an Key Topics CoveredWhat the QuitGPT movement is and why it startedHow to build a practical AI stack on a limited budgetThere is no universally best AI tool. There is the best tool for your specific job, your budget, and where your team already works. Tools Covered in This EpisodePerplexity (perplexity.ai) — AI-powered research with cited sourcesGoogle Gemini — AI integrated into Google WorkspaceNotebook LM — Google's document-based research tool, free to useMistral / LeChat — open weight AI models with EU hosting optionsClaude (Anthropic) — deep reasoning, long document analysis, agentic capabilitiesClaude Cowork — desktop AI agent for file and document managementClaude Code — AI-assisted coding for developers and technical foundersClaude for Excel — spreadsheet automation within Microsoft Excel Timestamps: 00:00 What We've Been Up To 03:18 So You're Thinking of Breaking Up with ChatGPT?  09:35 Perplexity: The Best AI Tool for Research  16:07 Google Gemini: The Strongest AI Option for Teams Already in Google Workspace 21:59 Mistral: The Best AI Choice for European Businesses and Regulated Industries 33:07 Claude: The Strongest AI Tool for Deep Analysis, Long Documents, and High-Stakes Work 37:23 Building Your AI Tech Stack 41:51 AI News: Anthropic's Safety Policy Shift 47:20 AI Gone Wrong: Robot Vacuum Army & Even AI Safety People Go Wrong 53:38 Wrapping Up Get in Touchhello@earlyadoptr.ai TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr All links and resources: https://linktr.ee/early_adoptr Get in touch with Early Adoptr: hello@earlyadoptr.ai Follow Us on Socials & Resources: IG: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr YouTube: https://www.youtube.com/@early_adoptr Substack: https://substack.com/@earlyadoptrpod Resources: https://linktr.ee/early_adoptr Hosted on Acast. See acast.com/privacy for more information.

    55 min

  2. 25 FEB

    The AI Gap Is Already Widening. Which Side Are You On?

    A new study from the National Bureau of Economic Research made headlines with a blunt claim: AI has had no measurable impact on productivity. Kyle and guest co-host Sean (filling in for Jess) do what most people never bother to do...they actually read the full 70-page report! What they find is far more interesting, and far more useful, than the headline suggests. Here's what the headline buried: firms with the highest productivity (measured by sales per employee) have AI adoption rates of around 80%. The lowest performers? Closer to 40%. Companies generating $500K per employee are nearly twice as likely to be using AI as those generating $10K. The gap is already widening, and it has nothing to do with which tools you're buying. This episode breaks down why flat productivity numbers are completely normal for a technology only three years into mainstream adoption, what history tells us about what comes next (spoiler: the Solow Paradox predicted this exact moment back in 1987), and why the organizations that move now are setting themselves up for the J-curve surge that's coming. It is not a story about failure. It is a story about timing, organizational readiness, and what you should be doing right now to be on the right side of that gap. If you are a founder, operator, or small business leader wondering whether AI is actually delivering (or whether you have been wasting your time_ this episode gives you the honest, grounded answer. Plus practical frameworks you can start using this week. Our guest this week is Sean, a partner at Breakthrough Growth Partners, where he advises founders, operators, and leadership teams on growth strategy and AI adoption. Website: https://breakthroughgrow.com What You'll Learn Why flat AI productivity numbers are expected and what history tells us about what comes nextThe key difference between companies seeing results and those that are not (it is not the tools)What the "agility gap" is and why smaller, newer organizations have a structural advantage right nowHow to assess whether your organization is actually ready to benefit from AIFive practical frameworks for accelerating real AI adoption in your businessWhy many high-profile "AI-driven" layoffs were actually driven by macroeconomic factors Timestamps: 00:00 Introduction 01:59 The NBER Study Everyone Misread (And What It Actually Says) 06:07 78% of US Firms Are Using AI — So Why Aren't We Seeing Results? 09:46 The Solow Paradox: We've Seen This Productivity Lag Before 13:20 High Performers vs. Low Performers: The AI Adoption Gap Is Already Widening 17:08 The Agility Gap: Why Smaller, Newer Companies Have the Upper Hand Right Now 20:43 AI and Job Losses: Separating the Real Data from the Corporate Narrative 24:26 What Happens When You Automate Away Entry-Level Roles 28:34 The J-Curve: Are We Finally Coming Out of the Dip? 32:05 Model Wars and Falling Prices: What Fierce AI Competition Means for Your Business 36:01 Same Cost, 10x the Capability: How to Think About AI Value Today 36:56 The Tool Is Becoming a Commodity — Your Implementation Strategy Is Not 37:54 Five Frameworks for Getting Real Productivity Gains from AI 39:36 The Three Frameworks That Turn AI From a Buzzword Into a Business Process 46:34 Is Your Business Ready for AI to Accelerate It — or Just Accelerate Its Problems? 50:15 The Productivity Surge Is Coming — Here's How to Be Ready When It Lands 51:06 AI News: OpenClaw Goes to OpenAI: What It Means for Agentic Security 56:36 AI Gone Wrong: Grok's Nutrition Initiative - A Case Study in Missing Guardrails Get in Touch with Early Adoptrhello@earlyadoptr.ai Follow us: @early_adoptr on TikTok, Instagram, and YouTube All links and resources: https://linktr.ee/early_adoptr Get in touch with Early Adoptr: hello@earlyadoptr.ai Follow Us on Socials & Resources: IG: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr YouTube: https://www.youtube.com/@early_adoptr Substack: https://substack.com/@earlyadoptrpod Resources: https://linktr.ee/early_adoptr Hosted on Acast. See acast.com/privacy for more information.

    1 h 3 min

  3. 18 FEB

    Claude Cowork Explained: Can AI Really Organize Your Files and Data?

    Claude Cowork is generating serious buzz as Anthropic's latest feature, but the name undersells what it actually does. This isn't collaboration software, it's a desktop AI agent that can read, create, edit, organize, and manage files on your local computer through plain English instructions. In this episode, Kyle and guest co-host Sean break down what Claude Cowork actually is, how it works, and why it represents a major change in how we interact with AI tools. They explore what Cowork is, how it works, practical use cases and the real risks of giving AI access to your local files. We also cover the Super Bowl's AI advertising blitz and the spectacular failure of AI.com's $85 million launch. What You'll LearnHow to set up and use Claude Cowork safely on your desktop without risking your filesPractical workflows for expense reports, file organization, research synthesis, and data cleanupWhy Cowork represents a major step up the "ladder of autonomy" from advisor AI to active participantThe real security risks of local file access and how to mitigate them with narrow permissionsBest practices for testing AI automation: start small, supervise closely, expand slowlyWhy the automation trap is more dangerous than dramatic failuresHow to create dedicated working folders and maintain oversight as AI handles more tasks Key TakeawaysClaude Cowork makes agentic AI accessible to everyone.Start with dedicated folders, not your entire hard drive.The automation trap is more insidious than obvious errors.Prior proper planning prevents poor performance.We're shifting from doing work to directing work. Timestamps: 00:00 What We've Been Up to This Week  03:12 What is Claude Cowork and What Does It Actually Do? 06:45 Claude Cowork: Moving Up the Ladder of Autonomy 08:43 What Cowork Actually Does: Reading, Creating, and Organizing Files 10:46 The Infinite Intern Gets Smarter 13:19 How to Set Up Cowork 16:21 Why Cowork Only Sees What You Allow 18:02 Why Now? The Tech Behind Agentic Workflows for Non-Technical Users 27:35 Practical Cowork Use Cases 35:32 Should You Label AI-Generated Content? 36:17 AI Tools: Features vs. Products 36:59 What Are the Risks of Using Cowork? 44:58 Best Practices for Using Cowork 51:12 From Clicking Buttons to Describing Outcomes: The Shift in AI Interaction 53:05 AI News of the Week: The Super Bowl Hype Cycle 59:38 AI Gone Wrong: AI.com Get in touch with Early Adoptr: hello@earlyadoptr.ai Follow Us on Socials & Resources: IG: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr YouTube: https://www.youtube.com/@early_adoptr Substack: https://substack.com/@earlyadoptrpod Resources: https://linktr.ee/early_adoptr Hosted on Acast. See acast.com/privacy for more information.

    1 h 5 min

  4. 11 FEB

    Understanding ChatGPT Apps: Where They Help, Where They Don’t, and Why

    ChatGPT “apps” have been getting a ton of hype since OpenAI opened submissions in December. The pitch is simple: this is the iPhone App Store moment for AI — build once, tap into hundreds of millions of users, and ride the distribution wave. In this episode, Jess and Kyle unpack what ChatGPT apps actually are (and what they aren’t). They break down the difference between apps, plugins, and custom GPTs, why the Apple comparison falls apart fast, and what the underlying architecture (MCP servers + in-chat widgets) means for builders who care about customer ownership, data, and monetization. We also cover the buzziest news story in a while: Moltbook and OpenClaw (formerly “Clawdbot”), the viral “agents social network” story. What You’ll LearnWhat a ChatGPT app is and how it differs from plugins and custom GPTsWhy “App Store moment” is an oversimplification and what the real opportunity isThe mall kiosk vs storefront analogy: distribution without owning the customer relationshipWhere ChatGPT apps genuinely reduce friction (and where they add it)The practical constraints developers are hitting right nowHow MCP changes the game for interoperabilityWhat the Moltbook/OpenClaw incident reveals about security, hype, and “agent culture” narratives TIMESTAMPS: 00:00 Introduction and Weekly Updates 06:41 ChatGPT App Store Launch and Overview 19:25 Understanding ChatGPT Apps vs. Plugins and Custom GPTs 28:57 The Model Context Protocol and Its Implications 33:00 The Future of AI Models and Ecosystems 36:05 Invisible Apps and Personal AI Agents 38:54 Navigating the ChatGPT App Submission Process 39:49 Exploring ChatGPT Apps for Users 43:02 Building ChatGPT Apps: Key Considerations 51:04 Evaluating the Viability of ChatGPT Apps 52:53 Moltbook and ClawdBot/Openclaw 📲 **FOLLOW EARLY ADOPTR** Email: hello@earlyadoptr.ai Instagram: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr LinkedIn: https://linkedin.com/company/early-adoptr Resources: https://linktr.ee/early_adoptr Get in touch with Early Adoptr: hello@earlyadoptr.ai Follow Us on Socials & Resources: IG: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr YouTube: https://www.youtube.com/@early_adoptr Substack: https://substack.com/@earlyadoptrpod Resources: https://linktr.ee/early_adoptr Hosted on Acast. See acast.com/privacy for more information.

    51 min

  5. 4 FEB

    Market Research on a Startup Budget : When to Trust Synthetic Data

    Synthetic data is often pitched as a shortcut around slow, expensive market research. In this episode, we break down when that promise holds, and when it falls apart. This week, we welcome our guest Lee Henshaw, founder and AI marketing guru, to share how he actually uses synthetic respondents in real business decisions. From testing pricing and sales messaging to simulating focus groups of UK media buyers and retail CMOs, Lee walks through what works, what doesn’t, and where founders can get into trouble if they over-trust the output. This episode introduces a practical, risk-based approach: use synthetics for speed and direction, validate with real people when the stakes are high, and design research around decisions, not curiosity. If you want better customer insight without a six-figure research budget, this episode shows what’s realistically possible right now. Listen to our previous episode for the basics on synthetic data - https://shows.acast.com/early-adoptr/episodes/synthetic-data-without-the-hype-practical-uses-and-real-risk Make sure to check out Lee's course on Maven: https://maven.com/dino-myers-lamptey-lee-henshaw/the-marketer-in-the-loop https://www.linkedin.com/in/leehenshaw/ What You’ll LearnHow synthetic respondents differ from traditional synthetic datasetsWhen synthetic research is useful for fast decision-making, and when it’s riskyHow to design synthetic focus groups that mirror real buyer segmentsA decision-first approach to market research that reduces wasted effortHow to validate synthetic insights against real customer feedback Key Topics CoveredSynthetic respondents vs synthetic datasetsPrompting and validation strategies for synthetic focus groupsRisk-based decision frameworks for using AI research toolsBackward market research and the “phantom report” methodIterative follow-up in synthetic interviewsLarge-scale qualitative analysis using AI agentsAccuracy, bias, and trust issues in synthetic dataHow agencies are incorporating synthetic research into client workGaps in market research training among marketers Timestamps: 00:00 What We've Been Up to This Week 03:41 Synthetic Data Explained: A Quick, Practical Recap 07:45 Meet Lee Henshaw: Using AI for Real Market Research 10:28 “Brains in a Jar”: What Synthetic Respondents Actually Are 12:42 Predicting The Traitors With Synthetic Data 15:22 Pricing With Synthetic Focus Groups: A Real Synthetic Research Example 19:37 Talking to Retail CMOs Using Synthetic Focus Groups 23:20 Can You Trust Synthetic Data? Accuracy, Bias, and Validation 28:18 How to Build and Engineer Synthetic Respondent Audiences 31:44 Why Secondary Market Research Still Matters 35:15 Backward Market Research: Start With the Decision 38:57 Common Mistakes & Top Tips When Using Synthetic Respondents 50:16 AI News of the Week: World Models and What’s Next 01:00:31 AI Gone Wrong 01:03:29 Where to Find Us 📲 **FOLLOW EARLY ADOPTR** Email: hello@earlyadoptr.ai Instagram: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr LinkedIn: https://linkedin.com/company/early-adoptr Resources: https://linktr.ee/early_adoptr Get in touch with Early Adoptr: hello@earlyadoptr.ai Follow Us on Socials & Resources: IG: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr YouTube: https://www.youtube.com/@early_adoptr Substack: https://substack.com/@earlyadoptrpod Resources: https://linktr.ee/early_adoptr Hosted on Acast. See acast.com/privacy for more information.

    1 h 5 min

  6. 28 GEN

    Synthetic Data Without the Hype: Practical Uses and Real Risks

    Synthetic data is being pitched as the end of slow, expensive market research. And in some cases, it really can help: it’s useful for testing systems safely, generating options quickly, and reducing the cost of experimentation, especially for small teams. But “synthetic data” is used to describe two very different things. One is synthetic datasets (fake-but-realistic data for testing and privacy). The other is synthetic respondents (AI-simulated people used for market research), and confusing the two can be a major issue. In this episode, we break down where synthetic data works, where it breaks, and the guardrails founders should use so it accelerates learning instead of replacing it. Key Topics Covered What synthetic data is: artificially generated data designed to mimic real-world patternsSynthetic datasets vs synthetic respondents — and why confusing them leads to bad decisionsDirectional insight vs reliable truth in AI-assisted researchBias in / bias out, and how synthetic data can amplify existing assumptionsPrivacy tradeoffs: when synthetic data is privacy-enhancing vs when it still carries riskReal-world use cases discussed:Testing and simulation in autonomous systems and rare edge casesFinance and fraud-pattern modeling under data restrictionsMarketing measurement challenges (cookie loss, attribution gaps)Founder use cases: pricing ranges, messaging tests, early segmentation, objection handling Timestamps: 00:00 Introduction and Personal Updates 04:53 What synthetic data actually is (and why it’s confusing) 09:07 Understanding Synthetic Data Definitions: datasets vs synthetic respondents 12:28 Why synthetic data is everywhere now: privacy, speed, and survey fatigue 15:03 Real World Use Cases: Where synthetic data already works outside of marketing 17:47 Synthetic Respondents: Opportunities and Challenges 18:14 How synthetic respondents simulate customer opinions 22:05 The Mark Ritson argument  and the context you shouldn’t ignore 23:16 Downsides to Synthetic Data: bias, false confidence, and missing the signal 29:45 Guardrails for using synthetic data 32:04 Practical founder use cases: pricing, messaging, and segmentation 34:47 Cultural pushback against AI: San Diego Comic Con & Bandcamp 38:25 AI gone wrong: the Kafkaesque spelling fail 41:40 Wrapping up 📲 **FOLLOW EARLY ADOPTR** Email: hello@earlyadoptr.ai Instagram: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr LinkedIn: https://linkedin.com/company/early-adoptr Resources: https://linktr.ee/early_adoptr Get in touch with Early Adoptr: hello@earlyadoptr.ai Follow Us on Socials & Resources: IG: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr YouTube: https://www.youtube.com/@early_adoptr Substack: https://substack.com/@earlyadoptrpod Resources: https://linktr.ee/early_adoptr Hosted on Acast. See acast.com/privacy for more information.

    43 min

  7. 21 GEN

    AI Video Tools Explained: Best Use Cases, Limits, and Risks

    AI video tools like Sora 2 and Nano Banana are finally crossing a line that earlier generations couldn’t: they don’t feel creepy anymore, and in some cases, they actually work! But “looking impressive” and “being useful” are two very different things. In this episode, we break down where AI video actually makes sense for founders (think: fast prototyping, early-stage demos, internal storytelling), and where it’s still more trouble than it’s worth. We talk through real business use cases, the hidden costs, the brand risks, and why these tools reward clear intention but punish sloppy thinking. The takeaway: AI video can save you time and money in the right context, but it’s not a free win, and it’s definitely not risk-free. Key Takeaways: What’s actually changed in Sora 2 (and what hasn’t)When AI video speeds you up vs. slows you downWhy good results come from thinking, not just promptingHow founders accidentally damage their brand with AI videoWhy deepfake safeguards matter — and where they fall shortWhen traditional video is still the smarter choiceWhen does AI video generation make sense for my business?What does “good prompting” actually look like in practice?How do audiences really feel about AI-generated video?What legal, ethical, and reputational risks should I factor in Timestamps: 00:00 What We've Been Up To This Week 05:48 AI Video: Useful Now, or Still Slop? 08:01 Sora 2: The Physics Upgrade That Makes It Watchable 16:48 Nano Banana: Gibberish Free Text (Finally!) 20:31 Real Use Cases: Headshots, Demos, Pitch Decks 29:27 Prompting for Video: Best Practice 37:47 Where It Breaks: Risks to Be Aware Of 39:34 Deepfakes, Watermarks, and Guardrails That Aren’t Perfect 42:51 Will People Hate This? The Trust & Transparency Test 49:20 This Week in AI: Microsoft, Apple × Google, Anthropic Cowork 55:16 AI Gone Wrong: The Weather Map That Invented Towns 58:20 Key Takeaways: AI Video Rewards Taste, Not Chaos 📲 **FOLLOW EARLY ADOPTR** Email: hello@earlyadoptr.ai Instagram: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr LinkedIn: https://linkedin.com/company/early-adoptr Resources: https://linktr.ee/early_adoptr Get in touch with Early Adoptr: hello@earlyadoptr.ai Follow Us on Socials & Resources: IG: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr YouTube: https://www.youtube.com/@early_adoptr Substack: https://substack.com/@earlyadoptrpod Resources: https://linktr.ee/early_adoptr Hosted on Acast. See acast.com/privacy for more information.

    1 h 1 min

  8. 14 GEN

    The AI Bubble Question: Hype, ROI, and the Future of Tech

    We're back from the holidays, and we've got a doozy of an episode. The stock market is betting everything on AI, but many organizations are still struggling to turn it into real results. So what’s actually going on? In this episode of Early Adopter, Jess and Kyle welcome back Rich Welsh (founder, tech advisor, and VC investor) to unpack one of the biggest questions in tech right now: Is AI an overinflated bubble waiting to burst, or is capital simply flowing toward the people who know how to use it well? From Nvidia-driven market concentration to why startups are often outpacing large enterprises, Rich breaks down where AI is delivering genuine value, where hype is distorting reality, and how founders and investors should be thinking about the next phase of adoption. We go beyond the surface level takes to explore what happens when AI becomes business-critical, and what the risks are if expectations and reality don’t line up. What you’ll learn: Rich’s buzzword of the year (hint: it’s not agentic)Why the most boring problems often make the best businessesHow founders can avoid the AI hype trap heading into 2026Why some teams are shipping faster than ever (while others are completely stuck)What an AI “bubble” would actually mean for startups, investors, and everyday people If AI underpins your operations and the market corrects, the impact won’t stop at valuations. It could affect funding, hiring, productivity....and everyone downstream. Whether you’re a founder, investor, or AI-curious operator, this episode will help you separate signal from noise and make more intentional decisions about where (and how) to to embrace AI. Early Adoptr Book Club: If Anyone Builds It, Everyone Dies - https://ifanyonebuildsit.com/ The Infinite Retina - https://www.amazon.com/Infinite-Retina-Computing-technologies-revolution/dp/1838824049 00:00 What We've Been Up To 05:34 Is There an AI Bubble? Separating Hype From Reality 08:25 Why AI ROI Is So Hard to Measure from an Investor's Perspective 11:14 Why Startups Are Winning With AI While Big Companies Struggle 13:56 What an AI Market Crash Would Mean 16:48 AI in Entertainment: Real ROI vs Studio-Scale Hype 27:33 The Next Phase of AI in Media, Gaming, and World-Building 35:48 From Large Language Models to World Models: What Comes Next 39:47 The Real AI Bubble: Where Expectations Break Down 51:35 How Founders Should Use AI in 2026 (Capital-Efficient Strategies) 57:37 AI News of the Week: CES Roundup 01:02:30 AI Gone Wrong: Bunnies on the Rampage 01:05:23 Wrapping Up for the Week Follow Us: Instagram: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr LinkedIn: https://linkedin.com/company/early-adoptr Resources: https://linktr.ee/early_adoptr Get in touch with Early Adoptr: hello@earlyadoptr.ai Follow Us on Socials & Resources: IG: https://instagram.com/early_adoptr TikTok: https://tiktok.com/@early_adoptr YouTube: https://www.youtube.com/@early_adoptr Substack: https://substack.com/@earlyadoptrpod Resources: https://linktr.ee/early_adoptr Hosted on Acast. See acast.com/privacy for more information.

    1 h 7 min
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Trailer

Descrizione

Early Adoptr helps founders and small business owners cut through AI jargon and turn real tools into real results. Hosted by Jess and Kyle, startup founders themselves, this podcast breaks down AI in plain English: what works, what’s hype, and how to use AI to grow faster, work smarter, and build your unfair advantage. No buzzwords. No confusion. Just practical, step-by-step guidance you can implement today. Hosted on Acast. See acast.com/privacy for more information.

Informazioni

  • Creatore
    Early Adoptr
  • Anni di attività
    2025 - 2026
  • Puntate
    43
  • Classificazione
    Contenuti adatti a tutti
  • Copyright
    © Early Adoptr
  • Sito web del podcast
    Early Adoptr

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