Most AI agent content is hype. This isn’t.

I’m Mike, host of Lone Wolf Unleashed.

This week I walk you through how I implemented my first AI agent team using Paperclip — an open-source multi-agent platform — and more importantly, the methodical approach that made the result controllable rather than chaotic.

The short version: I started with a target operating model in Obsidian, not with the tool.

People, process, technology, on a page.

I handed the architecture to Claude, spun up a “CEO agent” in Paperclip, and let it form a team — CTO, business analyst, content writer, marketing manager, clips publisher — that now runs my content pipeline from Asana through Descript, SharePoint and into Metricool.

Architecture before automation.

Human-in-the-loop, non-negotiable.

You’re still the master of your business.

Listen to hear me walk you through it.

Chapters

00:00 First AI agent team, implemented

00:30 Why Paperclip (and going from local to cloud)

01:18 Starting with a target operating model in Obsidian

02:03 Handing the architecture to Claude

02:22 The CEO agent spawns the team

03:40 The content workflow: Asana → Descript → SharePoint → Paperclip → Metricool

05:04 Matching agents to models: Opus, Sonnet, Haiku

05:56 Routines instead of manual triggers

06:40 What took the time (spoiler: it wasn’t the agents)

07:34 Human-in-the-loop and run history

09:10 You are the master of your business

10:54 The 80/20 rule for phase-one builds

Resources: lonewolfunleashed.com/resources

Mentioned in this episode:

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Check out the "Websites Made Simple" podcast with Holly Christie at https://websitesmadesimple.co.uk/

This podcast is part of the Podknows Podcasting ICN Network

Are your AI tools burning through tokens faster than ever? You're not alone — and in this episode I'm sharing the framework that's changed how I manage knowledge and query AI at scale.

I'm Mike from Lone Wolf Unleashed — I help solo founders build business systems so they can switch off sooner and live larger. Today I'm walking through Andrej Karpathy's wiki methodology, how to implement it in Obsidian, and why it reduces AI token consumption by up to 85% compared to traditional approaches.

I also cover how I've applied this directly to Lone Wolf Unleashed — building a target operating model, setting up agent teams in Paperclip, and designing a content production architecture that closes the gap between production and distribution for a solo operator.

If you're hitting your AI limits, spending too much on token-heavy workflows, or just looking for a smarter way to manage your business knowledge — this one's for you.

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Chapters

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00:00 — Introduction: Token limits and why this matters for solo founders

00:43 — How Andrej Karpathy's wiki methodology works

02:50 — Setting up a wiki ingest workflow in Obsidian

03:33 — Why the wiki is 80–85% more token efficient

04:38 — Visualising knowledge connections with Obsidian's graph view

05:56 — Replacing a team of analysts as a solo operator

06:43 — Target operating models and the 5Ps framework

07:50 — Introducing Paperclip and automated content production

10:13 — Building an AI Business Analyst assistant

13:00 — What this means for your business and your life

14:21 — Resources and wrap-up

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RESOURCES

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Wiki resources and setup guide: https://lonewolfunleashed.com/resources

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CONNECT

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Website: https://lonewolfunleashed.com

Email Mike: mike@lonewolfunleashed.com

Mentioned in this episode:

You might also like...

Check out the "Websites Made Simple" podcast with Holly Christie at https://websitesmadesimple.co.uk/

This podcast is part of the Podknows Podcasting ICN Network

Your AI tool isn't broken. It's just full.

Hi, I'm Mike Fox, host of this podcast, "Lone Wolf Unleashed." I help solo founders systemise their businesses so they can switch off sooner and live larger. This week I'm pulling back the curtain on a real data project: 103,000 rows, a client locked into Microsoft Copilot, and a categorisation task that would've taken weeks to do manually.

Here's what I worked through — and what you can take straight into your own business.

The context window is the AI's working memory. Once it runs out, the quality of your outputs tanks — or the conversation just stops. Understanding this constraint is the difference between AI that saves you hours and AI that wastes them.

Working within real-world limitations (not every client is on Claude), I built a strategy to break down a massive data set into token-efficient chunks, set up a structured workflow for Microsoft Copilot to process them in sequence, and then used a manager-agent review layer to QA the outputs before any human had to.

The same principles apply whether you're running Claude, ChatGPT, or whatever tool your organisation has decided is the one. The constraints change. The framework doesn't.

What you'll learn:

• What a context window is and why it limits what your AI can do with large data sets
• How to make your data and your prompts token-efficient before you send them
• A practical chunking strategy for splitting large Excel or CSV files across multiple AI sessions
• How to use a manager-agent role to review and QA your AI outputs
• Which model settings to use for heavy analytical tasks

If you're using AI to make decisions — not just write emails — this episode is for you.

Resources, frameworks, and tools: lonewolfunleashed.com/resources

Mentioned in this episode:

This podcast is part of the Podknows Podcasting ICN Network

You might also like...

Check out the "Websites Made Simple" podcast with Holly Christie at https://websitesmadesimple.co.uk/