Early Adoptr

A new client signs...congrats, you're excited! And then the onboarding begins process with all the tedious tasks: folder creation, the welcome email, the kickoff scheduling, the project setup, the intake form you'll need to chase twice. None of it is difficult, all of it takes time, and it always seems to happen at the exact moment you're least available to do it well.This week on Early Adoptr, we walk one real, familiar business process through every single rung of the Ladder of Autonomy, from fully manual through to fully autonomous, using real examples at each stage. By the end, you'll know what each level actually looks like in practice, which rung your current setup sits on, and what a realistic next step looks like for your business.This is episode three in Early Adoptr's ongoing series on AI agents. If you haven't listened to our previous episodes (links below), it's worth starting there.What is an AI Agent: https://shows.acast.com/early-adoptr/episodes/what-is-an-ai-agent-a-plain-english-guide-for-business-ownerAI Agent Frameworks Explained: https://shows.acast.com/early-adoptr/episodes/ai-agent-frameworks-explained-the-five-things-every-agent-syWhat You'll LearnHow to tell which steps in your workflow genuinely benefit from AI and which ones are better handled by a simple automationWhat it actually means to add an AI agent to a business workflow, and how that differs from using a chat tool like Claude or ChatGPTHow AI agents become more capable and more autonomous at each level — and what that progression looks like applied to a single, familiar business processWhy keeping a human in the loop isn't just a safety measure, and how the way you structure that oversight changes as your setup becomes more sophisticatedWhat the real security and risk considerations are when AI starts taking actions on your behalf, with practical guidance on how to approach permissions and accessWhy the most advanced level of AI autonomy is worth understanding and what goes wrong for businesses that skip the basics00:00 What We've Been Up to This Week03:46 Exploring Client Onboarding and Automation07:27 The Ladder of Autonomy: A Quick Recap13:11 Before You Start: Why Workflow Mapping Comes First14:55 Rung One: Basic Automation and Where It Falls Apart20:32 Rung Two: Adding AI Into Your Onboarding Workflow28:41 Rung Three: Handing the Agent a Goal, Not a Task39:19 Rung Four: Full Autonomy and What Can Go Wrong47:08 Your Action Plan: How to Start Without Overcomplicating It51:57 AI News of the Week: Anthropic Launches Claude Design & Allbirds AI PivotFollow Us: Email: hello@earlyadoptr.aiLinkedIn: https://www.linkedin.com/company/early-adoptr/ TikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptrResources:https://www.reddit.com/r/AI_Agents/comments/1nv3sd4/case_study_client_onboarding_issue_how_i_fixed_it/https://www.linkedin.com/pulse/how-agentic-ai-can-re-define-customer-onboarding-product-upadhye-7ikje/https://www.mindstudio.ai/blog/ai-powered-client-onboarding-tools-workflowshttps://churnzero.com/blog/customer-onboarding-with-ai/ https://techcrunch.com/2026/04/17/anthropic-launches-claude-design-a-new-product-for-creating-quick-visuals/ https://slate.com/technology/2026/04/ai-allbirds-pivot-silicon-valley.html

Most business owners have heard the word "framework" thrown around a lot lately and filed it under "probably technical, not my problem." In this episode, we make the case that it is your problem, not because you need to build one, but because understanding what a framework actually does is what helps you evaluate any agent tool being pitched to you, spot where an agentic workflow is likely to break down, and make smarter decisions about what to hand over and what to keep human.Most business owners have heard the word "framework" thrown around a lot lately and filed it under "probably technical, not my problem." In this episode, we make the case that it is your problem — not because you need to build one, but because understanding what a framework actually does is what helps you evaluate any agent tool being pitched to you, spot where an agentic workflow is likely to break down, and make smarter decisions about what to hand over and what to keep human.In this episode, Jess and Kyle walk through a complete practical example, that shows how you move from writing down your decision logic to deploying a real working agent, step by step.We also cover two major stories coming out from Anthropic, plus, a look at Meta's first model from its Superintelligence Labs — and what it says about what big spending can and can't buy in AI right now.What You'll LearnWhat an AI agent framework actually isThe five components every agent system needs to work — model access, tools, memory, coordination, and human oversight — and why these are your map for evaluating any AI productWhy decision logic mapping is important before you build anythingHow to automate a real business process, using inbound enquiry handling as a worked example, from writing your decision logic through to rolling out with guardrailsThe difference between CrewAI, LangChain, and LangGraph, and which situations each one is suited toInbound enquiry automation as a practical use caseHuman-in-the-loop and why it matters as agents gain more accessTimestamps: 00:00 Introduction and Personal Updates06:42 Recapping AI Agents and the Ladder of Autonomy12:07 Frameworks 101: The Five Capabilities Every Agent Needs18:41 How Framework Design Affects Performance and Cost19:20 Frameworks vs. Capabilities in Agent Systems25:40 How to Get Started with Frameworks: A Five-Stage Path40:10 Real-World Example: Automating Inbound Sales Enquiries47:52 Safety Considerations in Agent Use50:21 Key Takeaways for Agent Frameworks52:28 AI News of the Week: Claude Mythos and Third-Party Harnesses58:42 AI Gone Wrong : Meta's Expensive Model Mistake📱 Instagram: https://instagram.com/early_adoptr🎵 TikTok: https://tiktok.com/@early_adoptr💼 LinkedIn: https://linkedin.com/company/early-adoptr🔗 Resources: https://linktr.ee/early_adoptrHave an AI story or question?📩 hello@earlyadoptr.aiResources:https://www.anthropic.com/engineering/building-effective-agents https://www.moveworks.com/us/en/resources/blog/what-is-agentic-framework https://www.linkedin.com/pulse/turning-your-icp-framework-working-ai-agent-without-code-aird-mash-ni1me/ https://www.moxo.com/blog/agentic-ai-framework-comparison https://www.freecodecamp.org/news/the-agentic-ai-handbook/ https://huggingface.co/learn/agents-course/unit2/introduction https://www.reddit.com/r/AI_Agents/comments/1pcrjgn/trying_to_learn_agentic_ai_please_suggest_me_a/ http://aiagentskit.com/blog/agentic-ai-frameworks/https://www.datacamp.com/blog/agentic-ai https://cloudsecurityalliance.org/blog/2026/02/02/the-agentic-trust-framework-zero-trust-governance-for-ai-agents# https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explained https://claude.com/blog/claude-managed-agents https://techcrunch.com/2026/04/04/anthropic-says-claude-code-subscribers-will-need-to-pay-extra-for-openclaw-support/https://futurism.com/artificial-intelligence/first-model-zuckerberg-superintelligence-labs-flopshttps://thenewstack.io/anthropic-claude-mythos-cybersecurity/

Claude Skills is one of the most useful features available to Claude users right now, and solves something that you almost definitely have encountered.You start a new conversation, and Claude has no idea how you like things done. You end up re-explaining your tone, pasting in your brand guidelines, or manually correcting the output back into something that actually sounds like you. Every. Single. Time.Claude Skills fixes that but allowing you to build your preferences, your rules, your formats, and your style into a reusable package that Claude can pull in automatically whenever it is relevant. Set it up once, and stop repeating yourself.In this episode, Kyle and Jess break down what Skills actually are, how they sit alongside Model Context Protocol (MCP), and the pros and cons. They also get into where to find pre-built Skills, how to build your own without any technical knowledge, and what to watch out for when you are browsing the public marketplaces. The episode also covers OpenAI's decision to shut down Sora and merge its products into a single super app, plus, a humanoid robot causes chaos at a hot pot restaurant in California.If you're fed up with constantly repeating yourself to Claude, this is the episode for you.PS. Kyle's audio is a little weird on this one, apologies in advance! What You'll LearnWhat Claude Skills are, how they differ from custom GPTs and Google Gems, and why portability gives them a longer shelf life than eitherHow Skills, MCP, Projects, and memory all fit together and when to reach for each oneWhere to find pre-built Skills, what to check before you install anything from a public marketplace, and how to build your own without any technical knowledgeWhy the skill description is an activation condition, not a title, and what to do if your skill is not triggeringWhat OpenAI shutting down Sora and consolidating its products signals about where the money is actually flowing in AI right nowWhy the window where small businesses can run the same AI stack as enterprises is real, and why it probably will not stay open indefinitelyWhat are Claude Skills and how are they different from custom GPTs or Google Gems?How do Claude Skills and MCP work together?How do I find and install Claude Skills without needing any technical knowledge?Are public Claude Skills safe to install, and what should I check before using one?How do I write a Claude Skill that actually activates when I need it?Timestamps:00:00 Introduction and Weekly Updates04:19 Quick Recap: What Model Context Protocol (MCP) Does and Why Skills Come Next06:28 What Are Claude Skills and Why Do They Matter11:17 Inside a Skill: How It Is Built and How It Knows When to Activat16:51 Where to Find Skills and How to Install Them Without Any Technical Knowledge17:59 Memory, Projects, and Skills: Which One Does What20:21 Projects vs Skills: How to Use Both Without Getting Confused24:09 Where to Find Skills: Build vs Pre-made26:50 Free, Portable, and Consistent: The Pros of Claude Skills30:18 Skills Are Not Perfect: The Limitations Worth Knowing About35:27 Real Business Use Cases: Brand Voice, Sales Prep, and More42:39 Getting Started with Claude Skills: Tips and Tricks46:14 AI News of the Week: Sora and OpenAI's SuperApp55:12 AI Gone Wrong: Robot Hot Pot ChaosResources:Anthropic Skills repositoryskills.sh Find Skills SkillVoice DNA skillAnthropic's official guidance on skillsMCP Episode Part 1 MCP Episode Part 2Disney Exits OpenAI Deal After AI Giant Shutters SoraOpenAI Plans Launch of Desktop ‘Superapp’ to Refocus, Simplify User ExperienceFollow Us:Email: hello@earlyadoptr.aiTikTok: @early_adoptrInstagram: @early_adoptrYouTube: @early_adoptrLinkedIn: @early_adoptr

MCP — Model Context Protocol — is the AI infrastructure that's quickly becoming the key layer underneath almost every serious AI setup. It's a big part of why AI is shifting from something where you copy and paste from one tab to the other, to something that actually can act on your behalf, and is the foundation that makes agentic AI possible.Last week in part one of this series, we covered the what MCP is and why it's such a big deal. In part two, we get into what MCP actually looks like in practice, from the easy non-technical entry points (no technical knowledge required!) already built into Claude to automation tools like Zapier to Kyle's own more advanced setup that allows you to have plain-English conversation with your business data in 30 minutes. We cover a range of options so you can find the right starting point for where you are right now, and understand how far you can take it from there.If you are a founder, operator or small business owners who is tired of manually looking at data across all your different systems and tools, this is the episode for you.What You Will LearnThe easiest way to get started with MCP with no technical knowledge requiredWhat a more advanced setup looks like using BigQuery and ClaudeWhy clean data still matters — MCP removes the barrier between you and your data, but it can't fix what is broken underneathThe safety rules that apply to every MCP setupWhat multi-agent systems look like next, and why MCP is the infrastructure that makes them possibleTimestamps: 00:00 What We've Been Up To This Week04:43 What Is MCP and Why Does It Matter? A Quick Recap08:58 Why AI Agents Need MCP to Actually Be Useful11:28 The Easy Wins: MCP Connectors Already Built Into Claude18:12 Zapier, n8n and Make: The Next Step Up23:40 The Advanced Setup: Talking to Your Data Warehouse With Claude26:28 The Problem MCP Solves: Getting Answers Without a Developer28:47 Asking Your Data Questions in Plain English32:41 Democratizing Data Analysis for All Businesses34:33 Garbage In, Garbage Out: Why Clean Data Still Matters36:31 Having a Real Conversation With Your Data: Memory and Context in Data Conversations39:35 Pulling From Multiple Systems in a Single Question42:27 Where MCP Is Heading in the Next 12 Months47:03 AI News of the Week: What 81,000 Claude Users Actually Want From AI49:06 AI Gone Wrong: The Importance of Human Oversight52:34 Wrapping Up for the WeekGet in TouchEmail: hello@earlyadoptr.aiTikTok: @early_adoptrInstagram: @early_adoptrYouTube: @early_adoptrLinkedIn: @early_adoptrResources:Official Anthropic MCP server list: github.com/anthropics/mcp-serversGitHub MCP server (what we used): github.com/github/github-mcp-serverBigQuery MCP server options: search Smithery.ai for “bigquery”Zapier MCP (no-code entry point): zapier.com/mcpSmithery.ai — browse and discover MCP serversOWASP MCP Top 10 — security reference: owasp.org/www-project-mcp-top-10AI News of the Week: https://www.anthropic.com/features/81k-interviewsAI Gone Wrong: https://fortune.com/2026/03/18/ai-coding-risks-amazon-agents-enterprise/AI Gone Wrong: https://techcrunch.com/2026/03/18/meta-is-having-trouble-with-rogue-ai-agents/

MCP — Model Context Protocol — is the open standard that is quickly becoming the infrastructure layer underneath almost every serious AI tool you will encounter in 2026. It's one of the main reasons that AI is shifting from something you consult to something that acts on your behalf. And like most big developments in this space, it has arrived with both significant opportunity and risks.In this episode, Kyle and Jess do a full deep dive into what MCP is, why the whole industry has moved on it faster than almost any standard in modern tech, and what the upside looks like for a small business that has never had access to serious AI integrations before. We also cover the cons including some new security risks. This is part one of two. This week we're tackling what it is, the pros, the cons and some quick wins to make sure you understand what you are dealing with before next week's episode gets into the practical setup, the safety framework, and Kyle's actual tech stack. If you are going to connect AI to your business systems — and increasingly, you will be — this is the episode to start with.What You Will LearnWhat MCP isHow MCP differs from APIs, and why that distinction matters Why OpenAI, Google, and Microsoft all adopted a competitor's open standard within six months Why agentic AI only delivers on its promise if the AI can move fluidly across multiple systemsThe real business advantages: cost efficiency, flexibility, the ecosystem of ready-made connections, and why a cheaper model with good connections beats an expensive one working blindThe risks that matter: over-permissioned access, supply chain vulnerabilities, and a novel attack type called tool poisoninSome practical rules for staying safe with MCP before next week's full setup guideTimestamps:00:00 What We've Been Up To06:41 What Is Model Context Protol (MCP) and Why Is Everyone Suddenly Talking About It17:34 Why MCP Is the Missing Piece for AI That Actually Does Things22:40 The Real Advantages of MCP for Small Businesses24:07 The Importance of Your Tool Integrations27:05 Competitive Advantage through Connected Workflows29:31 Pros of MCP31:22 The Downsides to MCP39:31 Best Practices for Safe MCP Implementation42:26 AI News: Meta Acquires Molt Book49:43 AI Gone Wrong: Amazon Pauses AI-Generated Code After Costly OutagesGet in TouchEmail: hello@earlyadoptr.aiTikTok: @early_adoptr Instagram: @early_adoptr YouTube: @early_adoptr

OpenClaw is one of the biggest AI stories of the year, and it is generating equal parts excitement and concern. Unlike every other AI tool you have probably used, it does not just respond to questions...it takes action. In this episode, Kyle and Jess get into what OpenClaw actually is, why the use cases are compelling enough that people are buying spare laptops just to run it, but (most importantly) why the security risks are serious enough that they were both nervous about discussing it at all. From prompt injection attacks to a skills marketplace where nearly one in seven tools has been found to contain malicious code, and a new category of threat called cognitive context theft — this is not a light risk profile. The episode exists because the tool is worth understanding, and because understanding the risks is the only responsible way to approach it.This episode is slightly more technical than most, and that is intentional. The goal is not to scare you off, but to make sure that if you do decide to experiment with OpenClaw, you know exactly what you are handing over and how to protect yourself.What You Will LearnWhat OpenClaw is, how it launched, and it's chaotic journey so farWhy the developer behind OpenClaw was acqui-hired by OpenAI and what that signals about where the industry is headingThedifference between AI that advises and AI that actsHow OpenClaw's skills system and the ClawHub marketplace workWhat prompt injection is, how attackers are already exploiting it against OpenClaw users, and why there is no clean solution to it yetWhat cognitive context theft is and why OpenClaw creates a new category of security risk that did not exist beforeReal business use casesHow OpenClaw compares to Claude CoworkWhy setting OpenClaw up safely is a technical undertaking — and what to do if that is not your skill setThe SAVE framework: practical rules for using OpenClaw responsiblyChapters00:00 Introduction and What We've Been Up to This Week 02:33 What Is OpenClaw and Why Is Everyone Talking About It?05:17 Understanding OpenClaw: How OpenClaw Actually Works08:05 OpenClaw vs. Cloud Cowork: Key Differences10:55 Exploring OpenClaw's Skills System13:26 Use Cases and Potential Applications of OpenClaw16:28 Pros of Using OpenClaw19:13 Cons of Using OpenClaw21:40 Final Thoughts on OpenClaw28:17 Cons of OpenClaw36:49 Practical Guidance for Safe Usage47:54 Framework for Safe OpenClaw Usage50:32 AI News of the Week: Perplexity Launches Perplexity Computer54:30 AI Gone Wrong: Woolworth's Chat Bot57:54 Wrapping up for the weekGet in TouchEmail: hello@earlyadoptr.aiFollow us: @early_adoptr on TikTok, Instagram, YouTube, and LinkedInResources:https://www.malwarebytes.com/blog/news/2026/02/openclaw-what-is-it-and-can-you-use-it-safely https://www.gendigital.com/blog/insights/leadership-perspectives/how-to-use-openclaw-safely https://medium.com/@srechakra/sda-f079871369ae https://shawnkanungo.com/blog/how-to-use-openclaw-safely-best-practices-and-security-tipshttps://www.perplexity.ai/hub/blog/introducing-perplexity-computerhttps://www.bbc.co.uk/news/articles/cy7jeyeyd18o

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-anKey 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 ExcelTimestamps: 00:00 What We've Been Up To03:18 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 Workspace21:59 Mistral: The Best AI Choice for European Businesses and Regulated Industries33:07 Claude: The Strongest AI Tool for Deep Analysis, Long Documents, and High-Stakes Work37:23 Building Your AI Tech Stack41:51 AI News: Anthropic's Safety Policy Shift47:20 AI Gone Wrong: Robot Vacuum Army & Even AI Safety People Go Wrong53:38 Wrapping UpGet in Touchhello@earlyadoptr.aiTikTok: @early_adoptrInstagram: @early_adoptrYouTube: @early_adoptrAll links and resources: https://linktr.ee/early_adoptr

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.comWhat You'll LearnWhy 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 factorsTimestamps: 00:00 Introduction01:59 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 Before13:20 High Performers vs. Low Performers: The AI Adoption Gap Is Already Widening17:08 The Agility Gap: Why Smaller, Newer Companies Have the Upper Hand Right Now20:43 AI and Job Losses: Separating the Real Data from the Corporate Narrative24:26 What Happens When You Automate Away Entry-Level Roles28:34 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 Business36:01 Same Cost, 10x the Capability: How to Think About AI Value Today36:56 The Tool Is Becoming a Commodity — Your Implementation Strategy Is Not37:54 Five Frameworks for Getting Real Productivity Gains from AI39:36 The Three Frameworks That Turn AI From a Buzzword Into a Business Process46:34 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 Lands51:06 AI News: OpenClaw Goes to OpenAI: What It Means for Agentic Security56:36 AI Gone Wrong: Grok's Nutrition Initiative - A Case Study in Missing GuardrailsGet in Touch with Early Adoptrhello@earlyadoptr.aiFollow us: @early_adoptr on TikTok, Instagram, and YouTubeAll links and resources: https://linktr.ee/early_adoptr