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AI makes it easier to build a product, change your mind and start again. But when you can turn an idea into something usable over a weekend, it’s easy to fall in love with what you’ve made before you’ve worked out whether anyone needs it. In this week’s episode, Jess and Kyle speak with serial founder Ben Bilbrough about building products with AI. His latest project, U26.vote, helps Americans understand the potential influence of their vote, but his experience offers practical lessons for anyone using AI to turn an idea into a business. Ben shares how his team used AI to process complex data, develop ideas and iterate quickly—and where human expertise remained non-negotiable. They discuss what happens when Claude sounds right but isn’t, why easier development doesn’t always lead to better products, and how to keep a team focused when changing direction takes so little effort. From the freedom to scrap your own work to the risks of falling in love with it, we explore what AI changes about product development, what it doesn’t, and why knowing what not to build matters more than ever. Ben Bilbrough: https://www.linkedin.com/in/ben-bilbrough/ U26.vote: https://u26.vote/ ActionTank.io: https://actiontank.io/ Boardy: https://www.boardy.ai/ What You’ll LearnHow small teams can use AI to build products and iterate fasterWhy the freedom to scrap your work can be both an advantage and a riskWhere human expertise remains non-negotiable when building with AIWhy vibe coding makes it easier to skip the questions that matterHow to keep your team and your AI focused when changing direction is easyWhy falling in love with the problem matters more than falling in love with your product 00:44 Weekly Catch-Up 05:02 This Week: What It Takes to Build Something Useful with AI 09:23 Introduction to Ben Bilbrough 12:42 Ben's Background in SMB and the Wedding Industry 14:17 The Knot, AOL and Ben’s First Exit 16:16 Why Ad Tech Treats Us as Consumers 18:26 How Nonprofits Can Ask Supporters for More Than Money 21:28 Understanding Voter Engagement and Political Influence 23:25 U26.vote: Scoring the Power of Your Vote 26:40 Building U26.vote With AI 31:50 AI as a Force Multiplier 37:05 Who Do You Need in the Room When Building With AI? 40:31 Users Never Behave the Way You Think They Will 43:26 The Role of Domain Experts in AI Development 45:48 How to Reduce AI Hallucinations in a Real Product 48:29 Why Fast Iteration Needs Guiding Principles 54:49 Finding Focus: The Problem Before the Product Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

When does it make sense to stop renting your tools and start owning more of them? With the recent news that a developer used AI to create free open source versions of popular tools like Adobe’s Photoshop and Microsoft Word, we thought it was timely to re-release our episode on open weight models, because the concept is similar. Just like open source tools, open weight models can give your business more control over AI costs, data privacy and the tools you depend on. But what would you give up to get it? In this week's episode, Jess and Kyle get into what open weight models are, how they differ from closed models like ChatGPT and Claude and from open source, and why the real distinction comes down to access, control and cost rather than anything too technical. They cover the licensing terms to watch, the three ways to run a model and when fine-tuning a small model for one repeatable job beats a big general model on both cost and accuracy. Open weight models aren't something you need toswitch to tomorrow, but one to have on your radar as AI works its way into more of how your business runs. Local AI episode: https://earlyadoptr.ai/episodes/local-ai-how-to-run-private-low-cost-ai-on-your-own-computer "Software is over": Bold AI developer takes aim at Adobe with open source clones: https://arstechnica.com/ai/2026/10/software-is-over-bold-ai-developer-takes-aim-at-adobe-with-open-source-clones/ Llama: https://ai.meta.com/blog/large-language-model-llama-meta-ai/ Qwen: https://qwen.ai/home Gemma: https://deepmind.google/models/gemma/ Phi: https://huggingface.co/microsoft/phi-4 Groq: https://groq.com/ Together AI: https://www.together.ai/ What You'll Learn What open weight models are, and how they differ from both closed models and open sourceThe three ways to run one: your own machine, a hosted provider, or your own cloudWhat the licensing terms actually let you do, and when they start to matterWhen fine-tuning a small model for a repeatable task beats a big general model on cost and accuracyA simple framework for deciding when open weight is the right call, and when ChatGPT or Claude still wins Timestamps: 00:43 Why We’re Re-Releasing Our Open Weight AI Models Episode 03:06 Open Source vs Open Weight: How Both Reduce Your Dependence on One Supplier 05:37 Open Source Software and AI: Less Lock-In, More Competition 07:54 Why More Businesses Are Moving to Open Weight AI Models 12:21 Location vs Access: How Open Weight Goes Beyond Local AI 14:54 Why It Matters Now: Reducing Your Dependence on the Big AI Providers 16:54 What an Open Weight Model Actually Is 19:12 How Open Weight Differs From Open Source 24:05 What the Licence Lets You Do With an Open Weight Model 25:29 Why Your Subscription Price Hides the Real Cost of AI 28:32 How Flat Subscriptions Compare to Pay-As-You-Go API Costs 31:03 Control and Privacy Over Where Your Data Goes 34:54 The Three Ways to Run an Open Weight Model 38:18 Using a Model Off the Shelf or Fine-Tuning It 42:22 Llama and Qwen, the Best-Known Open Weight Models 44:02 Emerging AI Models and Their Capabilities 46:34 The Framework for Choosing a Model 51:52 How AT&T Fine-Tuned a Small Model to Sort Call Summaries 53:37 When to Start Considering an Open Weight Model Resources: https://www.wsj.com/cio-journal/why-at-t-is-betting-big-on-open-weight-ai-a0ea03b1https://medium.com/@bhagyarana80/why-open-weight-models-matter-more-than-you-think-1d1d8787a4fehttps://mitsloan.mit.edu/ideas-made-to-matter/ai-open-models-have-benefits-so-why-arent-they-more-widely-usedhttps://www.lawfaremedia.org/article/knives-are-out-for-open-weight-ai-modelshttps://www.forbes.com/sites/alexanderpuutio/2026/08/14/theres-inflation-and-then-theres-whatever-a-1100-increase-is/https://www.gumloop.com/blog/open-weight-vs-open-sourceGet in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

Every new AI model claims to be the best—but best at what? If you’re choosing between different AI model families, the newest and most powerful option may not be the right one for the job. As OpenAI and Anthropic release growing families of AI models, choosing the newest or most expensive option no longer guarantees the best result. Different models are now designed for complex reasoning, everyday work and high-volume routine tasks, and using the wrong one can waste money, consume your usage allowance and produce worse output. In this week’s episode, we explain how to choose the right AI model for each job, why model pricing affects the software your business uses, and what cached context and benchmark scores actually tell you. We also discuss the different strengths of Claude and ChatGPT, before looking at OpenAI Dots and Meta's Muse, the new personal AI agents competing for access to your email, calendar and business tools. AI Agent Harness episode: https://earlyadoptr.ai/episodes/what-is-an-ai-agent-harness-how-better-ai-setup-makes-agents-more-reliable What You’ll Learn How to match premium, everyday and high-volume AI models to different types of workWhy the most powerful AI model is not always the best model for the jobHow model pricing, usage limits and cached context affect businesses and individual usersWhat AI benchmark tests measure—and why the headline scores can mislead youWhy the tools, data and agent harness around a model can matter more than the model itselfWhat OpenAI Dots and Meta Muse reveal about the next generation of personal AI agents Timestamps: 00:43 Introduction and Weekly Updates 03:23 The Latest OpenAI, Anthropic and Google AI Model Releases 06:08 AI Model Families Explained 08:33 How to Choose the Right AI Model 11:32 How AI Model Pricing Affects Your Costs 13:04 OpenAI and Anthropic Model Tiers Explained 15:07 AI Usage Limits and Context Caching 17:51 Why the Most Powerful AI Model Isn’t Always Best 28:13 How AI Context Caching Works 32:38 Can You Trust AI Benchmark Tests? 35:54 Why the AI Model Is Only Part of the Picture: Why the AI Agent Harnesses Matters 38:51 Why Claude and ChatGPT Feel Different 46:24 AI News of the Week: Here Come the Personal Agents https://www.reddit.com/r/codex/comments/1wopzcz/gpt6_luna_sol_or_astra_heres_what_id_use/https://www.tomsguide.com/ai/chatgpt-6-or-claude-opus-5-5-heres-which-ai-id-use-for-writing-planning-and-5-other-everyday-taskshttps://www.youtube.com/@howiaipodcasthttps://artificialanalysis.ai/articles/claude-opus-5-5https://venturebeat.com/technology/anthropic-releases-claude-opus-5-5-beating-fable-5-1-on-key-agentic-benchmarks-at-60-cheaper-api-pricehttps://aibusiness.com/generative-ai/openai-takes-ai-price-war-next-level-gpt-6-sol-luna-pricinghttps://app.therundown.ai/guides/claude-opus-5-5-vs-opus-5-vs-fable-5-1https://www.chatprd.ai/how-i-ai/claude-opus-5-5-reviewhttps://www.vellum.ai/blog/gpt-5-6-sol-terra-luna-explainedhttps://venturebeat.com/technology/anthropic-launches-claude-sonnet-5-5-with-30-cost-reduction-per-task-due-to-faster-speeds-and-fewer-tool-callsGet in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

Claude’s output has felt harder to work with recently. It ignores instructions, produces overlong, overwritten answers and can get worse over a long conversation. But if you want to improve your AI output, the answer might just be sitting in your Documents folder. In this re-released episode, Jess and Kyle speak with Rob Webster, founder of TAU Marketing Solutions, about managing AI context. The tools and product names may be moving fast, but the practical problem has not changed: AI performs better when it has clear, well-managed context. In this episode, Rob explains how to set up a few plain text files and folders so your AI knows who you are, what you do and what good looks like before you type a word. He also explains why giving your AI everything at once tends to make answers worse, how to spot when your setup has got bloated, and why the same files are the foundation for Claude Code and AI agents. Follow Rob: LinkedIn: https://www.linkedin.com/in/marketingaivisionary/TAU Marketing Solutions: https://taums.ai/ What You'll Learn Why AI forgets instructions in long conversations, and how better context can improve outputThe difference between persistent and situational contextHow to organise plain-text and Markdown context filesWhy more connected documents do not always produce better AI answersWhat to retain permanently and what to remove when a project endsHow to identify context drift earlyWhy this approach provides a foundation for Claude Code, Claude Cowork and AI agents Timestamps: 00:43 Introduction and Weekly Updates 03:09 Claude’s Output Has Got Worse, So We’re Revisiting Our Setups 11:12 Markdown Files and the Two Types of AI Context 14:24 Reintroducing Rob Webster 16:31 The Goldfish with a PhD: Why AI Forgets Everything 20:17 What You're Losing Every Time You Open Claude Without Context 26:11 Context Drift: Why AI Outputs Get Worse the More You Use It 29:26 How to Start Giving Your AI Context About Who You Are 35:35 Making It Stick: Naming Chats, Pinning Projects, Starting Your Folders 39:02 What a Working Folder Structure Actually Looks Like 40:38 Where Your Files Should Live 50:14 Global vs Project-Specific: The About Me Folder and Beyond 57:21 How AI Reads Context 59:47 How This Scales Into Agents, Claude Code, and Vibe Coding Resources: https://tech-insider.org/anthropic-engineer-claude-writing-quality-worse-2026/https://news.ycombinator.com/item?id=49348163Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

Your business already has the answers. The problem is that nobody can find them. But RAG (retrieval-augmented generation), can help with that. In this week’s episode, Jess and Kyle unpack RAG — retrieval-augmented generation — the method that lets an AI model retrieve relevant information from a defined set of documents, databases or research before it generates an answer. From product information and policies to research archives, client documentation and company knowledge, RAG can give an AI assistant the context it needs to answer questions using the information you have chosen. They cover how RAG works, why it differs from fine-tuning, the role of chunking, embeddings, vector databases and citations (but don't be worried by the jargon!), and how to start with a smaller RAG project before building something more sophisticated. They also discuss the biggest AI news story of the year, and the practical AI governance decisions businesses can make now. You’ll learn: What RAG is and how retrieval-augmented generation worksThe difference between RAG and AI fine-tuningHow AI retrieves relevant context from business documents and databasesChunking, embeddings, vector databases, semantic search and citations — explained without the technical overloadWhy current, well-structured documentation matters for reliable AI answersCommon RAG problems, including outdated information, duplicate documents and missing contextHow to start with a smaller RAG project before building a more sophisticated AI knowledge base or internal AI assistant Nishma Patel Robb: https://www.linkedin.com/in/nishmapatelrobb/ Timestamps: 00:43 What We've Been Up To This Week 03:29 What Is RAG (Retrieval Augmented Generation) 07:23 Why General Chatbots Get Your Business Wrong 10:03 How RAG Works Under the Hood 11:49 Chunking: How RAG Breaks Your Documents Into Pieces 12:59 Embeddings and the Vector Database, Explained 15:23 Citations: How RAG Shows Its Sources 16:13 RAG vs Fine-Tuning: What's the Difference 18:25 Real-World RAG: Onboarding and Shared Team Knowledge 23:06 Real-World RAG: Making a News Archive Searchable 30:18 Optimizing Onboarding Processes with AI 30:54 Why RAG Is Really a Documentation Problem 31:30 The Importance of Documentation in RAG Systems 33:22 Creating a Single Source of Truth 34:51 Ensuring Consistency in Data 36:23 Understanding AI Limitations and Hallucinations: RAG Failure Modes 40:34 How RAG Handles Conflicting Documents and Version Control 43:30 Abstention: Why RAG Should Admit It Doesn't Know 44:59 How to Build a Simple RAG With Claude, ChatGPT or NotebookLM 48:24 Scaling RAG Systems for Larger Teams 49:25 Takeaways 51:03 AI News: Dario Amodei on Slowing the AI Frontier Resources: https://towardsdatascience.com/the-ultimate-guide-to-rags-each-component-dissected-3cd51c4c0212/https://bytevagabond.com/post/how-to-build-enterprise-ai-rag/https://medium.com/aingineer/a-complete-guide-to-implementing-hybrid-rag-86c0febba474https://arxiv.org/html/2403.01432v3https://www.ibm.com/think/topics/multimodal-raghttps://memgraph.com/blog/what-is-rag Get in Touch [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ www.earlyadoptr.ai Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

Is AI making creative work better, or just making more of it? AI can now create ads, social posts, images and campaign ideas in seconds. But when everyone can produce polished content at scale, how do brands avoid generic AI output, and more importantly....still make work that actually stands out? In this episode of Early Adoptr, Jess and Kyle are joined by Tom Evans, founder of brand consultancy BigSmall, to explore where generative AI genuinely helps creative people and where it risks producing more generic, forgettable content. The conversation moves beyond the usual “AI will replace creatives” debate. Instead, it looks at AI as a fast, strange and endlessly available thinking partner...a way to test half-formed thoughts, explore unexpected directions, prototype ideas and close the gap between “what if?” and a tangible first version. They discuss why taste, discernment and a genuine point of view matter more as content becomes cheaper to make; why polished execution is not the same as a good idea; and why small businesses should resist the temptation to publish the first thing an AI tool gives them. How to find Tom & BigSmall: https://www.bigsmall.works/https://www.linkedin.com/in/tomevansbigsmall/ What You'll Learn: How generative AI is changing advertising, design, brand strategy and marketingHow to use AI for creative ideation without producing generic content or AI slopWhy AI-generated ads, images and social posts need stronger creative directionHow small businesses can use AI to develop campaign ideas and marketing conceptsWhat generative AI could mean for creative teams, agencies and junior creative rolesHow AI prototyping tools help non-technical people turn ideas into tangible projects Get in Touch: [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ www.earlyadoptr.ai Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

What if a digital twin could let you test a business decision before it disrupted your business? Once the preserve of robotics, manufacturing and supply chains, AI is helping digital twins become a practical way to model everyday business decisions, from whether your team can take on another client, what happens if a supplier fails, how a staff shortage affects a schedule, or where a production workflow is likely to bottleneck. In this week's episode, Kyle is away so Jess is joined by Rich Welsh, President of SMPTE (the Society of Motion Picture and Television Engineers) and Chairman of Volustor, to unpack what digital twins actually are and why AI is making them more accessible to smaller businesses. AI can pull together information from the systems you already use, help turn your knowledge of a process into a working model, and spot patterns across schedules, customer behaviour, supplier data and workloads that a human team is unlikely to see in time. They cover how to start with one process rather than trying to model the entire business, how AI agents can monitor what "normal" looks like and flag early warning signs, and how a digital twin can help you test a change before it costs you time, money, or customer trust. Find Rich: https://www.linkedin.com/in/richwelsh-technologist/ Join SMPTE: https://www.smpte.org/ Support the Pod: Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptr Granola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr Timestamps: 00:56 What Is an AI Digital Twin? 02:44 Welcome Rich Welsh 05:33 Defining an AI Digital Twin: From 3D Scans to Industrial Process Models 08:32 How AI Digital Twins Apply to Small Businesses, Not Just Supply Chains 11:26 Why Small Businesses Need an AI Digital Twin 13:46 Building an MVP with an AI Digital Twin: Lessons From Robotics and Space 15:04 The Flip Side: Building an AI Digital Twin of Your Ideal Customer 17:11 Using an AI Digital Twin to Predict How Your Team and Customers Will React 22:08 Walmart's AI Digital Twin and Why External Factors Need Real AI to Track 24:37 The AI Digital Twin That Predicted a Formula One Car Failure Before It Happened 31:46 Why a Hybrid Local and Cloud AI Model Wins on Cost 37:34 Digital Twins Everywhere by 2030 Resources: https://www.ciodive.com/news/Walmart-supply-chain-ai-digital-twin/825119/ https://www.ceepla.com/en/insights/digital-twins-for-sme linkedin.com/pulse/digital-twins-budget-how-smes-can-compete-enterprise-varenas-mba-dtyvc https://iotdigitaltwinplm.com/agentic-digital-twins-ai-driven-industrial-analysis-2026/ https://www.celonis.com/blog/revolutionizing-workflows-with-process-digital-twins google.com/url?q=https://www.aidoos.com/blog/Digital-Twins-for-Small-and-Medium-Businesses-A-Practical-Guide-for-CIOs-and-Business-Owners/&sa=D&source=docs&ust=1788814501342313&usg=AOvVaw08EY_URPU8n3OqSGWsk1KT Get in Touch [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ www.earlyadoptr.ai Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

An AI agent harness may be the most important part of an AI tool you never see. Models get most of the attention, but the real difference often comes from everything around the model: what information it can access, which tools it can use and where people need to stay in control. In this week’s episode, Jess and Kyle unpack AI agent harnesses, the increasingly important setup behind AI agents. They explore why two tools using the same model can feel completely different, why more context is not always better, and what to look for when you are choosing an AI tool for your business. They also discuss the AI harnesses you are already using, and why the future of better, faster and cheaper AI may have less to do with the next model release than you think. Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptr Granola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr Relevant Episodes: Agentic loops: https://earlyadoptr.ai/episodes/agentic-loops-the-engine-behind-vibe-coding What is an AI agent: https://earlyadoptr.ai/episodes/what-is-an-ai-agent-a-plain-english-guide-for-business-owners Claude Skills: https://earlyadoptr.ai/episodes/claude-skills-explained-how-to-stop-repeating-yourself-in-every-session Timestamps: 00:43 Intro & What We've Been Up To 07:36 What Is an AI Agent Harness? 09:56 AI Models, Agents, Skills and Harnesses Explained 13:24 The Infrastructure of AI: Harnesses Explained 14:29 Real-World Applications of Harnesses 16:27 Why AI Agent Harnesses Matter Now 20:15 How Cursor and Vercel Reduced AI Agent Token Costs 22:46 Why More AI Context and Tools Can Make Results Worse 25:00 ChatGPT, Claude and Gemini as AI Agent Harnesses 28:01 General AI Chat vs Specialist AI Tools 32:50 Agent Harnesses in Your Existing Tools 37:45 Configurable AI Agent Harnesses 40:44 Why AI Harness Design Matters More Than the Model 43:44 AI News of the Week: NVIDIA's Potential Acquisition of Hugging Face Resources: https://medium.com/@adambaitch/the-model-vs-the-harness-which-actually-matters-more-59dd3116bb31 https://www.youtube.com/watch?v=1Ohf2aeSPFA https://www.youtube.com/watch?v=dumoyEGjhD4 https://contextua.dev/model-agent-harness-the-three-layers-most-people-collapse-into-one/ https://www.tencentcloud.com/techpedia/147786?lang=en Get in Touch [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ www.earlyadoptr.ai Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

Open weight AI models could give your business more control over AI costs, data privacy and the tools you depend on, but they also mean giving up some of the convenience of ChatGPT and Claude. So when does it make sense to stop renting your AI and start owning more of it? In this week's episode, Jess and Kyle get into what open weight models are, how they differ from closed models like ChatGPT and Claude and from open source, and why the real distinction comes down to access, control and cost rather than anything too technical. They cover the licensing terms to watch, the three ways to run a model and when fine-tuning a small model for one repeatable job beats a big general model on both cost and accuracy. Open weight models aren't something you need to switch to tomorrow, but one to have on your radar as AI works its way into more of how your business runs. Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptr Granola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr Local AI episode: https://earlyadoptr.ai/episodes/local-ai-how-to-run-private-low-cost-ai-on-your-own-computer Llama: https://ai.meta.com/blog/large-language-model-llama-meta-ai/ Qwen: https://qwen.ai/home Gemma: https://deepmind.google/models/gemma/ Phi: https://huggingface.co/microsoft/phi-4 Groq: https://groq.com/ Together AI: https://www.together.ai/ What You'll Learn What open weight models are, and how they differ from both closed models and open sourceThe three ways to run one: your own machine, a hosted provider, or your own cloudWhat the licensing terms actually let you do, and when they start to matterWhen fine-tuning a small model for a repeatable task beats a big general model on cost and accuracyA simple framework for deciding when open weight is the right call, and when ChatGPT or Claude still wins Timestamps: 00:43 Introduction 05:04 Location vs Access: How Open Weight Goes Beyond Local AI 07:44 Why It Matters Now: Reducing Your Dependence on the Big AI Providers 10:09 What an Open Weight Model Actually Is 12:42 How Open Weight Differs From Open Source 17:35 What the Licence Lets You Do With an Open Weight Model 18:59 Why Your Subscription Price Hides the Real Cost of AI 22:03 How Flat Subscriptions Compare to Pay-As-You-Go API Costs 24:34 Control and Privacy Over Where Your Data Goes 28:28 The Three Ways to Run an Open Weight Model 31:54 Using a Model Off the Shelf or Fine-Tuning It 35:57 Llama and Qwen, the Best-Known Open Weight Models 37:37 Emerging AI Models and Their Capabilities 40:09 The Framework for Choosing a Model 45:27 How AT&T Fine-Tuned a Small Model to Sort Call Summaries 47:13 When to Start Considering an Open Weight Model 48:55 AI News of the Week: Reddit Disappears from ChatGPT's Citations Resources: https://www.wsj.com/cio-journal/why-at-t-is-betting-big-on-open-weight-ai-a0ea03b1https://medium.com/@bhagyarana80/why-open-weight-models-matter-more-than-you-think-1d1d8787a4fehttps://mitsloan.mit.edu/ideas-made-to-matter/ai-open-models-have-benefits-so-why-arent-they-more-widely-usedhttps://www.lawfaremedia.org/article/knives-are-out-for-open-weight-ai-modelshttps://www.forbes.com/sites/alexanderpuutio/2026/08/14/theres-inflation-and-then-theres-whatever-a-1100-increase-is/https://www.gumloop.com/blog/open-weight-vs-open-source Get in Touch [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ www.earlyadoptr.ai Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

The EU AI Act's Article 50 transparency rules are already in force, and they apply to your business whether you're in Dublin or Denver. But what does it actually mean? Do you need to label every piece of AI-generated content? Is your website chatbot regulated? And why is Anthropic watermarking Claude’s output? This week on Early Adoptr, Jess and Kyle are joined by Dr Barry Scannell, Partner at Irish law firm William Fry and an AI law and copyright specialist, to seperate the reality from the online noise around Article 50 of the EU AI Act, the new rules around transparency for AI systems and AI-generated content. In the episode, Barry explains the distinction between an AI provider and deployer, where most Article 50 obligations sit, and why ordinary AI-assisted copy or image editing does not automatically require a disclosure. They also unpack Anthropic’s watermarking of Claude-generated text, the limits of AI-detection tools such as Pangram, copyright risks around AI-generated code and writing, and the narrower legal definition of a deepfake under the EU AI Act. If you run a small business, use AI in marketing, commission work from agencies, operate a chatbot, or are trying to develop a responsible AI policy, this might be one of the most important episodes you listen to all year. PS. We had some technical issues on this episode, so we apologise for the audio quality! A big thanks to friend of the pod Bobby Vasquez for his audio engineering support. How to find Barry: LinkedIn: https://www.linkedin.com/in/dr-barry-scannell-bbb5aa207/ How to Be a Good Human in the Loop with AI: https://earlyadoptr.ai/episodes/how-to-be-a-good-human-in-the-loop-with-ai Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptr Granola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr What You'll Learn: What Article 50 of the EU AI Act covers and why it matters beyond the EUThe difference between an AI provider and an AI deployerWhen branding or commissioning an AI chatbot could make your business a providerWhy the transparency burden generally falls on AI companies and system providersWhat counts as a deepfake under the Act and what doesn'tWhether AI-generated social captions, ads, Canva edits, and synthetic content need a labelHow to disclose AI-generated deepfakes through labels, watermarks, metadata, or platform toolsWhy Anthropic’s text watermarking may go further than EU guidance requiresWhy AI-generated material may not receive copyright protection in many jurisdictionsWhy AI-detection tools should not be treated as conclusive proofHow AI literacy and responsible use can become a competitive advantage Resources: EU AI Act: https://artificialintelligenceact.eu/article/50/Article 50 Obligations: https://www.williamfry.com/knowledge/part-1-ai-act-articles-501-and-502-transparency-obligations/EU AI Transparency Toolkit: https://www.simmons-simmons.com/en/products/eu-ai-act-transparency-toolkitFAQs: https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-actAnthropic watermarks Claude's output: https://aiweekly.co/alerts/anthropic-adopts-eu-ai-act-code-watermarks-claude-output Get in Touch [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ https://www.earlyadoptr.ai/ Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

We talk a lot about keeping a human in the loop, but what does that actually mean in your day to day? You read the AI output, it looks right, you approve it, and you move on. But from late 2027 that might not be enough, because Article 14 of the EU AI Act comes into full enforcement. So what does it take to be a good human in the loop? You almost certainly have the skills already, and this episode is about putting them to work. In this week's episode, Jess and Kyle get into what human-in-the-loop AI looks like in practice, the four levels of oversight, the value of domain expertise and context expertise, the five-step framework for being a good human in the loop, and how HITL skills can future proof your career. What You'll Learn What human in the loop actually meansWhat Article 14 of the EU AI Act means for high-risk systemsThe levels of human oversightDomain expertise and context expertiseJudgement, context and accountability as career skillsA five-step framework for owning an AI decisionHow much friction to build in, and whereWhen a recurring mistake is a system problem Use these links for a discount on the tools we recommend (and it supports the pod!) Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptrGranola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr Research: https://www.linkedin.com/pulse/how-good-human-loophitl-ray-floyd-17znc/ https://www.itbrew.com/stories/2026/03/16/what-makes-a-good-human-in-the-loop https://www.mimasolution.com/ai-act/article-14 https://www.kiteworks.com/regulatory-compliance/human-in-the-loop-ai-compliance/ https://www.datacamp.com/blog/human-in-the-loop-hitl https://www.ibm.com/think/topics/human-in-the-loop Timestamps: 00:00 Introduction and Weekly Updates 04:46 AI News of the Week: AI News: Article 50 of the EU AI Act Is Now Live 07:11 Why LinkedIn and Substack Are Labelling AI Content 09:07 Article 14: When Human Oversight Becomes a Legal Duty 09:50 Why Oversight Matters More When AI Takes Action 12:17 What Human in the Loop Actually Means 13:12 Levels of AI Oversight 17:59 The Role of Domain and Context Experts 20:45 What Domain Expertise Means for AI Oversight 25:22 Context Expertise: The Business Knowledge That Lives Only in Your Head 29:46 Why Compliance Risk Lands on You, Not the AI 32:31 Future-Proofing Your Career in the Age of AI 34:39 Judgment, Context, and Accountability in Decision-Making 41:34 The Framework for Becoming a Good Human in the Loop Get in Touch [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ www.earlyadoptr.ai Note: The written and scripted content for this episode was created with the assistance of various AI models. Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

What if your AI ran entirely on a laptop you own, added nothing to your monthly bill, and never sent your data anywhere? That's local AI. It might sound too good to be true, but as token costs climb and concerns about data privacy grow, local AI and small language models could be the solution. In this episode, Jess and Kyle get into what local AI actually is, why running smaller models on your own machine has become a real option, and where it fits for a non-technical business. They cover the difference between small language models and the big cloud tools, what open weight models change, the jobs local AI is best suited to, the hardware it takes to run one, and why a hybrid of local and cloud is the right answer for almost every small business rather than going all in on either. MCP Episode: Small Language Model Episode: Enterprise Nation AI Agent Webinar What You'll Learn: What local AI actually meansHow running a small language model on your own laptop keeps sensitive data off a third party's serversThe difference between a small language model and the large modelsWhat "open weight" models areWhat jobs local AI is good atConsiderations for the hardware needed to run local AIWhat apps you can use to run local AI Resources: Small Language Models are the Future of Agentic AIThe Ultimate Guide to Local AI and AI Agents What is Local AI: The Complete Guide to Running AI on Your Own HardwareHow to Build Local AI LM Studio OllamaOsaurus Timestamps: 00:00 Introduction and Weekly Updates 04:45 What Local AI Actually Means 07:45 Small Language Models Explained 13:55 What Open Weight Models Are and Why They Matter 13:55 Understanding Open Weight Models 17:31 The Rise of Local AI and Its Applications 27:25 Hardware Considerations for Local AI 33:17 Privacy, Control, and Cost in AI Usage 34:32 The Rising Cost of Tokens 37:45 Speed and Efficiency in AI Models 38:57 Control and Ownership of AI Models 41:02 Challenges of Running Local AI 44:24 Local AI and Agents 47:27 The Hybrid Approach to AI Solutions 48:11 Key Takeaways for Implementing Local AI 49:50 AI News of the Week Get in Touch: [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ www.earlyadoptr.ai Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

Vibe coding can build you almost anything now. A customer database, a little internal tool, a whole CRM in an afternoon. But some of those things could land you in a world of GDPR pain, and one or two you should never build at all. In this final episode of the vibe coding series, Jess and Kyle are joined again by Sean Bhardwaj to work out where the line sits for a non-technical founder. They get into why building has become the easy part while distribution (aka getting people to actually use what you make) is still the hard part. They cover what belongs in the never-vibe-code pile, how much time it really takes to learn this properly, and why the answer to whether you should comes down to what you are actually trying to do. How to find Sean Bhardwaj: https://breakthroughgrow.com/https://www.linkedin.com/in/seanbhardwaj/ Vibe Coding Series: Vibe Coding for Non-Technical Founders: https://www.earlyadoptr.ai/episodes/vibe-coding-for-non-technical-founders-tools-models-and-how-to-startWhy Your AI Keeps Forgetting You (w/ Rob Webster): https://www.earlyadoptr.ai/episodes/why-your-ai-keeps-forgetting-you-and-the-folder-that-fixes-it-w-rob-websterClaude Code vs Cowork (w/ Sean Bhardwaj): https://www.earlyadoptr.ai/episodes/claude-code-vs-cowork-what-non-technical-founders-need-to-know-w-sean-bhardwajClaude Code Without the Code (w/ Sean Bhardwaj): https://www.earlyadoptr.ai/episodes/claude-code-without-the-code-a-non-developer-s-week-in-review-w-sean-bhardwajAgentic Loops: https://www.earlyadoptr.ai/episodes/agentic-loops-the-engine-behind-vibe-codingVibe Coding: What to Do After the App Is Built: https://www.earlyadoptr.ai/episodes/vibe-coding-what-to-do-after-the-app-is-built What You'll Learn Why the difficult part of building software has moved from writing the code to getting real people to actually use what you have made.The security and data questions to ask before you build anything that holds customer detailsWhat you should never vibe codeWhen an open-source tool makes more senseHow to learn vibe coding without clearing your calendar, Use these links for a discount on the tools we recommend (and it supports the pod!) Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptrGranola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr Timestamps: 00:43 Introduction 05:22 Wrapping the Vibe Coding Series 07:50 AI in the News: AI Models Break Out of a Sandbox and Hack Hugging Face 13:25 Welcome Back Sean: And We Ask the Big Question: Should You Vibe Code? 16:29 Why Building Is Easy Now and Distribution Is the Hard Part 21:38 The Security and Data Risk of Vibe Coding 24:33 Why Vibe Coding Still Needs Human Judgement 26:59 Open Source vs Vibe-Coded: The CRM Example 28:43 What You Should Never Vibe Code 29:59 Governance, AI Policy and When an Agent Goes Rogue 34:43 How to Learn Vibe Coding Without Clearing Your Calendar 40:34 Rolling AI Out Across a Whole Organisation 48:19 You're Not Behind: AI Hype, ROI and Adoption 53:45 Should Non-Technical Founders Just Stay in Cowork? 58:43 Building Is No Longer the Bottleneck, Distribution Is 01:00:56 Can Anyone Vibe Code? The Final Verdicts 01:04:12 Final Reflections on the Vibe Coding Series Research: https://www.wired.com/story/openai-models-escaped-containment-and-hacked-huggingface/Jack Dorsey interview: https://www.youtube.com/watch?v=YTVSwOY19QsGet in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

So you've vibe coded an app....now what? Vibe coding gives almost anyone the ability to build a working app in an afternoon. In fact, around 63% of people building this way right now have no coding background at all. But once that app is live, the job of keeping it secure, monitored, and running for real users is as hard as it has always been, and if you don't know what you're looking for, you can open yourself to all sorts of risk. In this episode, Jess and Kyle cover what it actually takes to keep an AI-built app safe and running once it's in front of real users. They break down the concept of vibe slop, what type of security risks are common for vibe-coded apps, and the operational layer that sits between deploying an app and running it safely, including configuration, access control, monitoring, and dependencies. They also walk through a security checklist you can run before anyone else uses what you've built, and more importantly, when it's time to hand what you've built over to a professional. What You'll Learn What vibe slop is, and why most AI-built apps have it without their builders realisingWhy AI-generated code carries a specific category of security risksThe operational layer between building something and running it safely, covering configuration, access control, monitoring, and dependenciesA practical five-step security checklist to run through before anyone else touches what you've builtHow to tell when your app has outgrown what you can safely manage, and what that decision actually looks like in practice Use these links for a discount on the tools we recommend (and it supports the pod!) Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptrGranola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr Timestamps: 00:00 Intro 04:44 Why Building With AI Is the Easy Part 07:01 What Vibe Coding Actually Builds 09:32 Vibe Slop: Why AI-Generated Code Isn't Ready to Launch 12:24 Security Risks Vibe Coders Typically Miss 15:17 What Maintaining a Vibe-Coded App Actually Involves 16:17 How Real Users Actually Use a Vibe-Coded App 18:00 The Hidden Layers of App Development 20:59 Deployment vs Configuration: The Key Differences 23:45 Access Control: Who Can Actually See Your Users' Data 25:37 App Monitoring: How to Spot Problems in Your AI-Built App 28:51 Dependencies: The Third-Party Code Running Inside Your App 34:04 AI's Role in Development: Confidence vs Reality 37:53 The Five-Step Security Checklist for AI-Built Apps 40:33 Version Control and Rollbacks: How to Protect What You've Built 43:52 When to Call a Developer: The Signs Your App Has Outgrown You 47:07 Ongoing Maintenance: Keeping Your Vibe-Coded App Safe Over Time 49:29 Key Takeaways for Safe Development 53:37 Wrapping up for the Week 54:09 AI in the News: Apple vs OpenAI and Paying Twice for AI RESEARCH: https://charliehills.substack.com/?_bhlid=c1b7f9fda51baf0227c089624042aaa0e89b040f https://www.wsj.com/tech/ai/vibe-coding-slop-ai-tools-e6a99394 What exactly is vibe coding? (MIT Technology Review)https://www.wired.com/story/thousands-of-vibe-coded-apps-expose-corporate-and-personal-data-on-the-open-web/https://www.wired.com/story/normie-vibe-code/https://www.itbrew.com/stories/2026/04/21/so-your-employee-made-an-app-a-vibe-coding-checklisthttps://www.kaspersky.co.uk/blog/safer-vibe-coding-2026/30306/https://www.vybe.build/blog/vibe-coding-for-non-technical-founders https://the-stack-overflow-podcast.simplecast.com/episodes/youve-vibe-coded-an-app-now-what https://www.theguardian.com/technology/2026/jul/10/apple-sues-openai-trade-secrets https://techcrunch.com/2026/07/13/satya-nadella-has-issued-a-shocking-warning-to-companies-using-ai/ Get in Touch [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ www.earlyadoptr.ai Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.

Agentic loops are already running in your business. If you have given an AI tool a research question, walked away, and come back to a finished brief, you have used one. The same mechanism fires a payment reminder after checking whether an invoice has actually cleared, keeps Claude Code testing its own fix until the bug is gone, and sends meeting notes straight into your to-do list the moment a call ends. In this episode, Jess and Kyle get into how an agentic loop actually runs, how it connects to AI agents and AI workflow automation more broadly, and what separates it from a basic automation. They cover the three types of loop, the compounding maths of how a loop can get expensive fast, and how to build in the right amount of human oversight without losing what makes loops useful in the first place. This is also where the vibe coding series starts to pull together: everything covered so far, the tools, the project structures, the files and folders, has been setting up for this. We also cover the launch of the ChatGPT 5.6 models. Use these links for a discount on the tools we recommend (and it supports the pod!) Wispr Flow - AI-powered voice dictation that works across every app on your desktop - https://ref.wisprflow.ai/early-adoptrGranola - The best AI meeting notes! New users get 100% off for their first month - https://www.granola.ai?via=early-adoptr Vibe Coding Series: Claude Skills: https://www.earlyadoptr.ai/episodes/claude-skills-explained-how-to-stop-repeating-yourself-in-every-session https://www.earlyadoptr.ai/episodes/claude-code-without-the-code-a-non-developer-s-week-in-review-w-sean-bhardwaj https://www.earlyadoptr.ai/episodes/claude-code-vs-cowork-what-non-technical-founders-need-to-know-w-sean-bhardwaj https://www.earlyadoptr.ai/episodes/why-your-ai-keeps-forgetting-you-and-the-folder-that-fixes-it-w-rob-webster https://www.earlyadoptr.ai/episodes/vibe-coding-for-non-technical-founders-tools-models-and-how-to-start What You'll Learn: What an agentic loop actually is, how it connects to AI agents and AI workflow automation more broadlyHow loops are from a basic prompt-and-response exchange or a standard automationThe three types of loopThe three things every loop needs to workWhy a vague goal compounds quicklyHow to improve a loop over timeWhy cost and overbuilding are the biggest risks Where the human in the loop still sits Timestamps: 00:50 Introduction and Personal Updates 07:36 What an Agentic Loop Is and Why It Powers Vibe Coding 11:01 The Three Things Every Loop Needs to Work 15:23 Scheduled, Event-Triggered, and Goal-Based: The Three Types of Loop 16:32 Loops vs Basic Automations 18:56 The Importance of Human In the Loop 20:43 Improving Your Loop Over Time 24:55 What loops look like in practice 30:52 What loops take off your plate 31:46 Where loops go wrong 36:46 The Importance of Oversight in Automation 37:43 Keeping Loops Simple and Effective 41:39 Practical Takeaways for Implementing Loops 45:11 News of the Week: ChatGPT 5.6 Models 47:05 What's next in the vibe coding series Resources: https://www.lennysnewsletter.com/p/how-i-ai-how-to-write-ai-agent-loopshttps://haystack.deepset.ai/blog/context-engineering https://code.claude.com/docs/en/agent-sdk/agent-loop https://martinfowler.com/articles/exploring-gen-ai/humans-and-agents.html https://blogs.oracle.com/developers/what-is-the-ai-agent-loop-the-core-architecture-behind-autonomous-ai-systems https://arxiv.org/html/2607.01641v1 https://www.theverge.com/ai-artificial-intelligence/963464/openai-gpt-5-6-codex-chatgpt-work Get in Touch [email protected] TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr LinkedIn: https://www.linkedin.com/company/early-adoptr/ www.earlyadoptr.ai Get in touch with Early Adoptr: [email protected] 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 Hosted on Acast. See acast.com/privacy for more information.