Leveling Up with Eric Siu

95% of the time, it's not a people problem. It's a process problem. So I set out to fix the process and in doing it, I found a way to clone my best employees. How skills took my X account from ~100K to 3M+ monthly impressions The finance skill that found $500K in hidden costs in 30 minutes Our internal "Skills Dojo" - think GitHub meets an app store for your team's best skills How evals and agentic loops turn one person into the work of 5-10 Chapters 00:00 Clone Your Best Employees With Skills 00:25 The Results: 3M+ Views on X 00:43 Building & Forking Skills 02:00 Fat Skills, Thin Harness 02:13 Skills That Drive Real Leads 02:48 The AI Marketing Skills Repo (2,600 Stars) 04:00 The Finance Skill That Saved $500K 04:53 X Long-Form Writer + Humanizer 05:39 Inside Our Skills Dojo 08:04 Publishing & Sanitizing Skills 09:00 Most Problems Are Process Problems 10:48 Speaking at MozCon NYC 11:26 Training The Team Like Jedi 12:03 The Person Of One 14:05 What Makes A Real Skill 14:18 What Is A Good Eval? 15:04 Agentic Loops Explained 16:45 Daisy-Chaining Skills Into Loops

👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe Everybody technical is talking about loops right now. But almost nobody is asking the real question: how do you apply loops to your business if you're a founder, operator, or marketing leader instead of an engineer? Chapters: 00:00 Why Everyone Is Talking About Loops 00:55 Applying Loops To Business 01:46 The Old Way: Prompting & Chatting 03:27 The Four Traps Of Demonstration Mode 05:20 Build, Measure, Escalate 07:53 The Pre-Meeting Context Filler 09:44 The Resolver Loop 11:26 Not Everything Should Be A Loop 12:46 The Build-Measure-Escalate Worksheet 14:05 The EO/SEO Content Operating System 15:28 Defining Your Definition Of Done 16:10 The Loop Is Only Real With Receipts

👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe Most companies try to hire their way to more A players. I built a system that mints them instead. In this video, I break down the three-part combo we use to reliably create A players inside our organization: skills, evals, and loops. This is the same framework we run in our six-week "pod of one" apprenticeship, where one person learns to do the work of four, five, or even ten roles by leveraging AI. Here's what I cover: ⬛ How services-as-software changes the math on labor hours and revenue per employee ⬛ What a skill actually is, and why it beats traditional processes nobody uses ⬛ How to build evals so you can prove a skill is good without trusting the vibes ⬛ How to chain skills into loops that kill your biggest weekly bottleneck ⬛ The exact run of show for our apprenticeship that you can steal and skillify yourself If you're a founder or operator, you have the invisible hammer to make this happen across your team. This is how you create predictability and reliability when it comes to minting A players. Chapters: 00:00 The Three-Part Combo 00:43 Services as Software Explained 01:44 The Skills Dojo 02:39 The Six-Week Apprenticeship 03:53 Defining Success and Done 04:38 What a Skill Actually Is 06:07 Why Single Brain Ties It Together 06:43 Skills as the Intention Behind Process 08:19 What an Eval Is and Why You Need One 09:07 What a Good Eval Looks Like 10:14 Chaining Skills Into Loops 11:54 Who Belongs in the Room 12:39 Skillify This Video Yourself 13:11 The Founder's Invisible Hammer 👉 Get 25k Words of AI-Powered SEO Content: https://clickflow.com/ 👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe 👉 Single Grain - Ad Agency focused on innovation https://www.singlegrain.com

👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe Most people are buying AI backwards. They stack up copilots, agents, and a pile of tools that never talk to each other, then wonder why nothing actually compounds. The piece almost everyone is missing is a company brain: one unified intelligence that connects every tool, call, and document so your agents and your team can finally execute as one. In this video I show you exactly how we built ours at Single Grain, running on 500,000 tokens of persistent memory, 90+ daily crons, and 2,800 sales-call transcripts turned into operational playbooks. Then I break the whole thing down as a stack you can build yourself: capture, retrieval, source of truth, permissioning, feedback loops, and execution. Get these layers right and the system learns, protects, and compounds, which is how one person ends up doing the work of ten and the revenue follows. Chapters 00:00 Why Most People Buy AI Backwards 01:43 Inside Single Grain's Company Brain 02:47 The Execution Architecture in Layers 03:38 Layer 1: Capture Everything 04:10 Why Compounding Wins 04:30 Layer 2: Retrieval 05:13 Layer 3: Source of Truth 06:45 Layer 4: Permissioning 07:14 Layer 5: Feedback Loops 07:55 Broken vs Working, Layer by Layer 09:45 Layer 6: Execution How to Connect IG: / ericosiu X: / ericosiu 👉 Single Brain - Revenue agents to scale faster using our unified intelligence layer. https://www.singlebrain.com 👉 Single Grain - AI marketing firm https://www.singlegrain.com 👉 Get 25k Words of AI-Powered SEO Content: https://clickflow.com/ 👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe

👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe Everyone is obsessing over Opus 4.8 right now, but Codex is quietly making me serious money. In this video I walk through six ways I am actually using Codex inside my business today, from spinning up custom ad creative for every single prospect to a multi-threaded recruiting machine, an internal skills marketplace, and a Dream 100 revenue command center. None of this is theory. These agents are running live in my company while I record, some of them for days at a time. If you are a founder or operator, the takeaway is simple: the people who learn how to do this first are the ones who pull ahead. It all comes down to your relationship with learning and change. Chapters 00:00 Why Codex Is Printing Money Right Now 00:21 #1 Picasso: Ad Creative at Scale for Outbound 02:35 #2 Running Codex With Hermes (Autonomous Agents) 03:19 #3 A Multi-Threaded AI Recruiting Machine 05:59 #4 The Skills Dojo: A Skills Marketplace for Your Team 08:05 #5 TerraMind: An Agent That Watches and Skillifies Your Work 10:00 #6 The Dream 100 Revenue Command Center 11:27 Bonus: Build Your Own Lead-Gen Engine How to Connect IG: / ericosiu X: / ericosiu 👉 Single Brain - Revenue agents to scale faster using our unified intelligence layer. https://www.singlebrain.com 👉 Single Grain - AI marketing firm https://www.singlegrain.com 👉 Get 25k Words of AI-Powered SEO Content: https://clickflow.com/ 👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe

👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe Everyone's talking about loops as the next big thing in AI. The founder of Claude Code says he doesn't prompt anymore—he builds loops. The founder of OpenClaude agrees. But what does that actually mean, and more importantly, how does it make you more money? In this video, I break down what loops really are and how they apply to your business: ⬛ Why prompting coding agents is becoming the old way of working ⬛ The 5 parts of every business loop: trigger, signal, action, eval gate, stop condition ⬛ How to apply loops to sales, content, recruiting, and operations ⬛ Reviving stale and lost deals automatically ⬛ Why broken loops in Slack and Telegram are where work goes to die ⬛ How compounding loops put you ahead of 99% of businesses This isn't theory. If you build tighter loops around revenue, content, recruiting, and ops, you'll be ahead of nearly every business out there—because almost nobody is thinking about this yet. Chapter 00:00 Why Loops Are The Future 00:41 The Receipts: What The Founders Say 02:00 What A Loop Actually Is 04:00 Five Tips For Running Agents Autonomously 05:00 The Loop Is The Expensive Part 06:00 Business Examples: Stale Deals & Speed To Lead 07:30 The Five Parts Of Every Business Loop 09:00 Broken Loops In Slack & Telegram 10:30 Applying Loops To Recruiting & Ops 11:30 How To Run A Loop Audit 13:30 Why Loops Compound How to Connect IG: / ericosiu X: / ericosiu 👉 Get 25k Words of AI-Powered SEO Content: https://clickflow.com/ 👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe 👉 Single Grain - Ad Agency focused on innovation https://www.singlegrain.com

👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe Fable 5 and Mythos 5 are here — and this is huge for revenue. In this video, I don't just break down the announcement. I show you exactly how to turn these new Claude models into money-making machines for your business. Whether you're a founder, operator, or running a services business, these use cases will put you ahead of the competition starting today. These aren't theories. CEOs and engineers are already using these models today to compress months of work into days and recover six figures on single deals. If you spend just one day exploring this, you could be miles ahead of everyone else. Chapter 00:00 Fable 5 & Mythos 5 Are Out 00:13 The Mythos Class Model Explained 00:57 Autonomous Revenue Ops Engine 02:30 Visual Intelligence Competitor Analysis 03:09 Enterprise Security As A New Revenue Vertical 04:17 Build A Unified Intelligence Bot 05:01 Autonomous Research To Strategy Pipeline 06:08 Automated Implementation Accelerator 07:08 Why You Should Explore This Now How to Connect IG: / ericosiu X: / ericosiu 👉 Get 25k Words of AI-Powered SEO Content: https://clickflow.com/ 👉 Growth Newsletter for top marketers: https://levelingup.beehiiv.com/subscribe 👉 Single Grain - Ad Agency focused on innovation https://www.singlegrain.com

Most companies are approaching AI the wrong way. They buy subscriptions, run a few experiments, and wonder why nothing changes. In this video, I break down the exact systems we're using to help teams become AI-native, from skill repositories and internal AI training programs to operational standards that increase speed, alignment, and execution. I also share why the future belongs to "pods of one" and how organizations can create a culture where employees continuously level up instead of waiting for permission or training. Chapters (00:00) Why Most Companies Still Suck at AI (00:13) Building an Internal Skills Repository (01:44) Using Leaderboards and AI Gamification (02:17) The Rise of the Pod of One (03:39) How AI Apprenticeships Create Leverage (04:43) Command by Negation Explained (05:44) Why High Agency Teams Win (06:25) Creating an AI-Native Culture (06:43) Final Thoughts

Most companies are using AI completely wrong. They use ChatGPT in isolation, run random prompts, and wonder why nothing compounds. In this video, I break down the exact Single Brain system we use to connect agents like OpenClaw, Hermes, and NemoClaw into one unified intelligence layer that helps teams move dramatically faster. We cover how these AI fleets plug into Slack, HubSpot, Salesforce, Google Search Console, analytics tools, ad accounts, and internal data systems to create a compounding workflow engine that actually generates revenue. I also walk through real examples including AI generated ad creatives, automated reporting, scaling top performing campaigns into hundreds of variants, reducing operational costs by $500,000, and how one person with agents can outperform entire traditional teams. If you want to understand where AI agents are actually heading and how businesses are using them to create leverage right now, this is the framework. Chapters: (00:00) Why most AI adoption fails (02:06) Connecting all your business tools into one brain (03:22) The AI org chart of the future (05:20) Why most teams are still using AI wrong (06:31) Human timelines no longer work (07:10) Building ad creatives with AI agents (08:06) Scaling campaigns into 200 variants automatically (08:47) How $7,500 in tokens saved $500,000 (10:02) Why AI agents will replace traditional workflows

Here’s why most AI agent systems break once they touch real business operations. The issue is not intelligence. The issue is control. Most companies are building disconnected prompts with no evaluation systems, no approval layers, and no recursive learning loops. That works for demos, but it falls apart when agents start touching production systems, ad spend, customer data, or outbound communication. The better approach is treating agents like an operational command system. Hermes becomes the control tower that launches goals, evaluates outputs, routes approvals, stores learnings, and continuously improves future execution while humans stay in the loop for anything high risk. In this video I break down how the AI optimization lab works, why recursive self improvement matters, how approval gates protect revenue and reputation, the difference between safe autonomy and dangerous autonomy, and how to structure agents that continuously move the business forward without creating operational risk. Chapters: (00:00) The real problem with AI agents (00:54) AI optimization lab explained (02:00) Hermes as the control tower (03:26) Safe autonomy for businesses (04:56) Why approval gates matter (06:01) Human approval for risky actions (07:42) Recursive self improvement loops (09:20) Scaling autonomous systems (10:31) Using Hermes to grow revenue faster

Here’s why Google’s new design.md standard could completely change how brands create content with AI agents. Right now most brands exist in formats AI can’t consistently understand. Your landing pages, ads, decks, and creative assets are scattered everywhere with no persistent design memory. Google’s new design.md format changes that by giving agents a structured way to understand your visual identity and generate assets that actually stay on-brand. In this video I break down how design.md works, why Google is trying to make it the default standard for AI-generated design, how we’re using it internally with agents, and why this becomes massively important for marketing teams trying to scale creative output without losing consistency. Chapters: (00:00) Why AI currently cannot “see” your brand (00:22) Google’s new design.md standard explained (01:06) Why Google wants to own the format (01:37) Real examples using ClickFlow and Single Grain (02:21) How agents generate branded assets automatically (02:43) Why open standards matter more than lock-in (03:23) The massive impact on marketing teams (04:04) Sales decks and personalized design workflows (05:01) The GitHub repo with reusable design systems (05:24) Using inspiration from top-performing websites (06:13) Why design.md could become the industry standard (06:29) How revenue agents change creative production

Here’s how one person can now run cold email infrastructure that used to require an entire team. Most outbound systems break because there are too many moving parts. You need lead sourcing, email verification, inbox warmup, campaign management, copywriting, optimization, and reporting all happening at once. In this video I show how agents inside a “single brain” system handle most of that work end-to-end while a human stays focused on judgment, strategy, and approvals. I also walk through how we’re using OpenClaw, Instantly, Whisper Flow, and recursive scoring systems to rewrite campaigns, manage infrastructure, QA sequences, and launch campaigns in parallel without needing multiple operators. Chapters (00:00) Why cold email used to require a full team (00:32) How the “single brain” system works (01:18) Reviewing Instantly campaign performance (02:09) AI rewriting and scoring email sequences (03:06) Why humans still need to stay in the loop (04:21) Incentives, personalization, and reply rates (05:41) Running multiple campaign workflows in parallel (06:28) Managing lead distribution and infrastructure (07:07) Reviewing campaigns inside Instantly (08:05) Fixing ICP targeting and send settings (09:01) Live feedback and campaign optimization (10:07) Why one person can now operate like a full outbound team (10:49) How companies are building “world brains”

Here’s the real state of OpenClaw right now. OpenClaw became a critical part of how our team operates, but over the last couple months the reliability has noticeably dropped. Messages fail, automations break, gateways hang, and teams start losing trust in the system when it stops responding consistently. In this video I walk through Peter Steinberger’s public apology, the exact issues we’re seeing inside Slack and Telegram, why reliability matters more than features, and how we’re thinking about Hermes vs OpenClaw moving forward. I also break down the “brain vs execution” model, why competition between the two is actually healthy, and why I still believe autonomous agents are the future despite the current issues. Chapters (00:00) Is it over for OpenClaw? (00:46) The reliability problems we’re seeing (02:08) Peter Steinberger’s apology (04:20) Why SSR matters (secure, stable, reliable) (05:05) The single brain + agent fleet setup (06:34) Real Slack failures inside our team (08:05) Telegram failures and broken responses (09:09) Hermes as the alternative (10:41) Brain vs execution model (12:03) Why OpenClaw still matters (13:34) Website deployed using OpenClaw (14:52) Final thoughts on the future of agents

Here’s why the “AI will cause mass unemployment” narrative is probably wrong. Every major wave of technology has triggered the same fear, and every time it’s played out differently. AI doesn’t just replace jobs, it shifts them. It removes repetitive work, increases productivity, and creates entirely new roles that didn’t exist before. In this video I walk through real historical data from radiology, agriculture, spreadsheets, and ATMs to show how job displacement actually works, why demand often increases, and how AI acts as a multiplier rather than a replacement. Chapters (00:00) The mass unemployment narrative(00:22) Radiology example (AI vs jobs)(01:08) AI as a demand multiplier(02:06) Drivers and task vs job thinking(02:28) Agriculture automation (tractor era)(03:46) Spreadsheets and job evolution(05:25) ATM prediction vs reality(05:42) Creative destruction explained(06:36) Why AI likely creates more opportunity

Here’s why I spent $7,500 on AI tokens in a single month and why it was worth it. Most people hear that number and think it’s insane. But that spend replaced work that would’ve cost way more in headcount, made our team significantly more effective, and even uncovered $500K in savings that I acted on within days. This isn’t just “AI cost” it’s leverage across sales, coaching, product, and operations. In this video I break down what that spend actually gets you, real examples of how it’s used inside the company, the ROI behind it, and how to think about cost vs speed when choosing models. Chapters (00:00) Why I spent $7,500 on tokens(00:38) What that spend actually buys(01:41) Using AI to coach your team(03:23) ROI breakdown and savings(04:19) Product and dev leverage(05:07) What the first 3 months look like(05:41) The “single brain” effect(06:07) Frontier vs cheaper models(07:53) Why you need to start now