A plumber shows up with a van full of tools. You don’t ask for a certification in pipe fitting theory. You look at the tools, you check the references, you watch them work for an hour. That’s the evaluation.

Software has never worked this way. We’ve always hired for knowledge — degrees, frameworks memorized, years of experience in a specific stack. The tools lived on the company’s servers. The employee showed up empty-handed and learned the internal systems.

That model is ending.

I know a marketer who carries a Claude Code setup with 9 custom agents — research, content, scheduling, analytics, campaign ops, email, community management, strategy, and an orchestrator that coordinates all of them. Each agent has its own model, its own eval criteria, and its own context files. The whole system lives on a laptop.

When this person interviews for a CMO or other marketing role, the conversation isn’t “tell me about a campaign you ran.”

It’s “show me your agents. Show me the eval framework. Show me the specs. Let me see what happens when I give you a brief and your system produces the first draft in 10 minutes.”

This is the shift HR teams aren’t ready for:

The unit of evaluation is no longer the person. It’s the person + their VERY OWN agent infrastructure.

Two candidates with identical CVs. One opens a terminal and deploys a working analytics dashboard during the interview. The other talks about the dashboards they’ve built in the past. Who gets hired?

The construction industry figured this out decades ago. A contractor’s value isn’t their knowledge of building codes — it’s their truck, their crew, their tools, and their ability to start on Monday. The references confirm they won’t disappear halfway through the job. The tools confirm they can do the work.

Knowledge workers are becoming contractors with digital toolkits.

What changes:

• Your portfolio isn’t a PDF anymore. It’s a GitHub repo, a set of Claude Code skills, a documented agent architecture, and a live demo you can run on your laptop.

• References still matter, but they answer a different question. Not “is this person smart?” but “does their system actually work in production, and can they adapt it to a new environment in a week?”

• The onboarding conversation flips. Instead of “here are our tools, learn them” — it becomes “show us your tools, and let’s figure out how they integrate with ours.”

• Compensation models shift. You’re not paying for 40 hours of a person’s time. You’re paying for a person + their agent system’s output capacity. The person with a better toolkit produces 5x the output. They should cost more. And they will.

This isn’t about personal branding. It’s about professional infrastructure.

The question for anyone entering this market today isn’t “how do I update my resume?” It’s “what does my toolkit look like, and can I demo it live?”

If the answer is “I use ChatGPT sometimes,” you’re the plumber who shows up with a wrench and no van.

If the answer is “here are my 6 agents, here’s the eval framework, here’s a project I shipped last month with this exact setup, and I can start on your workflows Monday” — you’re the contractor who gets the job.

The future of hiring isn’t credentials. It’s capability you can demonstrate on a laptop in 10 minutes.

Start building your toolkit. It’s the new resume.

A few months before that, I saw someone running in a LIVE.LOVE shirt. In Europe. Thousands of kilometers from where it all started.

Here’s the thing. We built LIVE.LOVE in Ukraine back in 2016. No app. No AI. No growth hacking playbook. Just three founders (Natalia, Slava, and Slava), a crazy idea that sport should be fun, and rented pools and stadiums.

Three thousand people passed through. Eight hundred active at peak. We became the largest team at the Bosphorus Cross-Continental Swim. I crossed it four times. We were the biggest crew in Oceanman worldwide. Largest team at Vienna, Munich, and Cyprus marathons. 180 out of 400 Ukrainian slots were ours on Bosphorus. From the whole country. Registration closed in 7 minutes.

But the numbers aren’t the point.

The point is what happened around the sport. People met, fell in love, and started families. Dozens of them. A TV host married an entrepreneur. The former CEO of Mercedes-Benz Ukraine told us he didn’t think making real friends at 38 was possible. Three of our runners created Board, now one of the largest business communities in Ukraine and beyond.

We had three pillars.

1. A shepherd: the founding team.

2. A temple: physical places where we gathered and trained.

3. And rituals: weekly runs, swims, trips that became sacred.

Then COVID hit. It killed all three pillars at once. No gatherings. No trips. No rituals. We never came back. That's what we lost.

And yet. People still swim in those caps. Still run in those shirts. Still find each other across three continents. Ukrainian migrants recognizing LIVE.LOVE gear in Barcelona, Berlin, Dubai, and reaching out.

That’s the difference between a community and a platform.

Today you can build a community app in a weekend. AI generates your content calendar, SEO drives traffic, a no-code tool handles payments. All the infrastructure that took us months in 2016 is now trivially easy.

And that’s exactly why it doesn’t matter.

The hard part was never the website. It was getting a hundred people to show up at 7 AM on Saturday to swim in cold water. It was creating a culture where a CEO and a freelancer share a lane, then share breakfast. It was making rituals that people protect like family traditions.

LinkedIn won’t build that. Instagram won’t. Facebook groups definitely won’t. They’re distribution channels, not communities. The moment the algorithm changes, your “community” evaporates.

A real community survives a pandemic. Survives a war. Survives mass emigration across 30 countries.

Don’t get me wrong. AI is a gift for community builders. Use it for logistics, scheduling, content, and analytics. Remove every boring operational task so you can focus on the only thing that matters: getting people in the same room, doing something meaningful together.

The technology makes building the platform trivially easy. Which means the only competitive advantage left is the human part. The shepherd. The temple. The rituals.

I’m moving to Dubai now. Thinking about doing it again. Different city, different people, same principles. If that sounds like something you’d want to be part of, write to me.

The world has never had better tools for building communities. And never been worse at actually doing it.

The sharpest CMO you know has a locked Facebook profile. The founder who built a $50M company shares nothing publicly except occasional conference slides.

This isn’t burnout. It’s a strategy.

And if you’re still publishing your best thinking into the open, you might want to understand what they already figured out.

The meadow that was

In Liu Cixin’s novel The Dark Forest, the universe isn’t empty — it’s silent. Every civilization that broadcasts its existence gets destroyed. Not out of malice, but pure game theory: if even one out of five observers decides you’re a threat, you’re done. So everyone hides.

The early internet was the opposite of that. It was a bright meadow where broadcasting your presence was the optimal move. More connections meant more value. More sharing meant more opportunity.

I lived this. For seven years, I ran content operations that produced roughly 14,000 articles — all written by hand. Me personally, plus team’s content. Reviews, guides, analysis across dozens of sites. No investors. No permission from anyone. No editorial board. Just ideas and execution. At peak, we reached 32 million monthly unique readers.

Sharing was rational because execution was the moat. Anyone could have my ideas. Nobody could replicate the output. The cost of copying 14,000 manually written articles was... seven years of work.

That math no longer holds.

The forest gets dark

Two things changed simultaneously, and the combination is what matters.

First, the open web consolidated. The spacious meadow shrank into a handful of platforms controlled by companies whose business model is extracting your signal and selling it as targeting data — or by governments interested in the same signal for different reasons.

Second, AI made execution cheap.

Before LLMs, a competitor couldn’t just absorb your idea and ship it. Ideas needed people, people worked in physical time, and physical time doesn’t scale. Your moat was real.

Now, a platform that helps you brainstorm, code, and write already owns the compute, the models, and — critically — the aggregate demand signal. It doesn’t need to read your specific prompt. It sees where the questions cluster. A gradient in idea space. A map of where human interest is moving.

The platform knows your idea is pregnant before you do.

Copying 14,000 articles today costs a week and a $500 API bill. The moat evaporated.

The predator isn’t who you think

In Cixin’s forest, civilizations hide from each other — from hunters. In the cognitive dark forest, the most dangerous actor isn’t your competitor. It’s the infrastructure itself.

You publish a framework for thinking about retention. Within months, it’s training data. Your unique angle becomes the median output of every AI writing tool. Your differentiation dissolves into the average.

And here’s the recursive trap: trying to outinnovate the system only feeds it. You think outside the box, you publish that thinking, and the box gets bigger. Your resistance isn’t suppressed — it’s absorbed.

This is why the smartest people are going quiet. Not because they have nothing to say. Because they’ve run the game theory and the math doesn’t favor broadcasting anymore.

The closed-account phenomenon

Watch the behavior pattern: a CMO with two decades of experience maintains a minimal LinkedIn presence and a completely locked personal Facebook. A successful founder shares nothing about their actual network, partnerships, or strategic thinking. They broadcast the social role but hide the real signal.

This isn’t paranoia. It’s the same asymmetric risk calculation from the novel. If five entities observe your signal — recruiters, AI scrapers, competitors, governments, random opportunists — and four of them do nothing, the fifth one might use it against you. The expected cost of broadcasting exceeds the expected benefit.

People are splitting into two layers: a public facade of carefully curated professional content, and a private layer of actual relationships, real ideas, and genuine strategic thinking. The facade feeds the forest. The private layer is where value actually lives.

What this means for anyone who creates content

If you’re a marketer, a founder, or anyone whose job involves putting ideas into the world, you’re facing a real strategic question. Not “should I create content?” but “what kind of signal should I broadcast?”

Here’s what I learned from 14,000 articles and watching the meadow become a forest:

• The moat moved. It’s no longer execution speed or content volume. It’s taste, curation, and the relationships your content builds. AI can write the text. It cannot build the trust that makes someone open your newsletter instead of the other 40 in their inbox.

• Publish frameworks, not secrets. Opinions and worldviews are hard to absorb into a model in useful ways. “Here are 10 tactics for retention” gets commoditized instantly. “Here’s how I think about retention and why most frameworks miss the point” — that’s stickier.

• Your real network is your real asset. Build it in private channels. DMs, small communities, one-on-one relationships. The public content is the storefront. Don’t confuse it with the warehouse.

• Don’t stop publishing. Change what the signal carries. The mistake is either going fully silent (you lose the storefront) or publishing everything (you feed the forest your best ideas for free). The move is to publish enough to attract, and keep the high-value thinking for contexts where you capture the value.

The recursion

This article is itself a signal in the dark forest. By describing the dynamic, I’m feeding it to the system I’m warning you about. The models will learn a little more about why people hide.

I wrote it anyway. Because silence isn’t a strategy — it’s a surrender. The real strategy is to speak in a way where the value stays with you and your audience, not with the forest.

The meadow is gone. But you don’t have to be prey.

You just have to stop pretending it’s still a meadow.

Tuesday evening. Dubai. Two months into relocating my life. My phone buzzes — WhatsApp, Indian number.

“Good Morning Slava! This is Narender from Neo Talent. Are you considering a job change in GCC?”

I’ve been in marketing for 15 years. Survived COVID, a war, and a cross-continental move. I know what a real headhunter sounds like. This wasn’t it. But I was curious — so I played along.

The Pitch

Narender sent a job description so generic it could’ve been written by ChatGPT in 2023. “Omnichannel Brand Strategy.” “Fit-fluencer Management.” “LTV Optimization.” Every buzzword, zero specifics.

Then came the Google Meet call. Camera off, naturally. He rattled off names: PureGym Arabia, Jumeirah Group, Barry’s. Salary? Up to 90,000 AED/month. Tax-free. Housing, transport, flights.

“Are you married?” Yes. “Dependents?” One son. He was building my file — or so he claimed.

Then the punchline: “All these services — activation fee of $550.” Via Stripe.

The $550 Job That Doesn’t Exist

Stripe invoice arrived within 60 seconds. Then: “Please send us a copy of your passport.”

$550 AND passport data? That’s not recruitment — that’s a shopping list for identity theft.

I wrote a polite rejection citing UAE Federal Decree-Law No. 33/2021 (charging candidates is illegal), the fact that none of these companies have open CMO positions, and that Neo Talent isn’t on India’s eMigrate portal.

His response? “Funny thing is I have copied the job description online. Pitty on you that you are not able to google things.”

He admitted the JD was fake. And I’m the embarrassed one?

The Negotiation Phase

This is where it got entertaining. I offered: “Get me to this job and I’ll pay you $5,000.”

“No worries. We get commission from employer. You no need to pay.” Then, 90 seconds later: “Shall I send you the link for $250?”

The employer commission vanished in two minutes. I offered $25. His reply deserves a frame:

“It’s about your career Slava.. you are not buying Tomatoes.”

Narender, if you’re reading this — the tomato line was chef’s kiss. But admitting you copied the JD kind of ruined the mystique.

I’m from Ukraine. Fraud doesn’t scare me — it entertains me. “I WANT to meet you, man,” I wrote. He got nervous. Then excited. Then nervous again.

By midnight: three more Stripe invoices, six payment reminders, and “3 people already paid.” By morning: “Forget it. Let’s not do timepass. Take care.”

How to Spot Executive Search Fraud (30-Second Checklist)

🚩 Red flags — run immediately:

• Contact via WhatsApp from a personal number

• ANY fee charged to the candidate

• Generic JD with no company-specific details

• Pressure to decide NOW

• Passport/ID request before the formal process

• AI-template website with dead social links

• No LinkedIn page, no Glassdoor, no eMigrate/MOHRE registration

✅ Real executive search looks like:

• Corporate email, named client

• Employer pays 15–30% of your annual salary as the fee

• Process takes weeks, not minutes

• No payment from you. Ever. Period.

The Numbers

India’s Ministry of External Affairs: 3,505+ illegal recruitment agencies documented. 1,553 complaints in 2021 alone. The pattern is always identical: WhatsApp → prestigious names → high salary → upfront fee → passport.

To everyone relocating to the Gulf: if a "recruiter" asks for money, here’s your script:

“Stop loving money so much. They are not worth doing this shit, man. Hugs and kisses. Bye.”

Farzad dropped a tweet that got a lot of attention: “The biggest job in the next 1-5 years will be AI Agent Architect.”

Someone who is extremely good at using AI to get stuff done for specific use-cases. Finance. Content. Video. Storefronts. Whatever.

Boris Cherny from Anthropic added: “Someone has to prompt the Claudes, talk to customers, coordinate with other teams, decide what to build next.”

Aaron Levie, CEO of Box, agreed: “The future of work will look something like what Boris is describing. People who know what they’re doing still have to tell the agents what to do, review their work, and integrate that work into a broader system.”

They’re all right. But they’re talking about engineering. Let me tell you what this looks like in marketing. Because I’ve been living it.

What I Actually Built

I’m a CMO at a fitness chain with 20+ clubs. Traditional offline business. The kind where you’d expect a traditional marketing team.

Instead, I built a system of AI agents. Not a chatbot. Not a single prompt. A team.

Here’s who’s on it:

• A researcher (Sherlock) who pulls fresh data, analyzes competitors, and monitors trends. It doesn’t sleep. It doesn’t forget.

  

• A content writer (Cassandra) trained on my voice, my style, my banned words list. It knows I hate “synergies” and “paradigm shifts.”

  

• A social analyst (Socialist) who tracks Twitter, YouTube, and engagement patterns. It reports. I decide.

  

• An image generator (Picasso) who knows our brand guidelines, our influencer’s face consistency, our visual language.

  

• An orchestrator (my buddy Alfie) who coordinates all of them. It delegates tasks, checks quality, and comes back with results.

I’m the CMO. I set direction. I make decisions. I review the output. But the execution? That’s the agents.

Why “AI Agent Architect” Is the Right Name

This isn’t about “using ChatGPT.” Every intern can do that.

This is about designing systems. Knowing which agent does what. How they communicate. What tools they need. When to intervene and when to let them run.

Think about it. A traditional marketing department has:

• Content team (3-5 people)

• Analytics team (2-3 people)

• Social media manager

• Designer

• Media buyer

An AI Agent Architect replaces none of them. They replace the need to hire all of them as full-time staff. One person who knows how to build and manage agent workflows can cover 80% of what those teams did. The remaining 20% is where the human judgment lives. Strategy. Taste. Client relationships. The stuff AI can’t fake.

The CMO as Orchestrator

Here’s where it gets interesting for marketing leaders specifically.

The CMO role is already shifting from “person who runs campaigns” to “person who runs systems.” AI agents accelerate this by an order of magnitude.

In my setup, I don’t write briefs for people. I write task definitions for agents. The difference?

• A brief says: “We need a social media post about our new club opening.”

• A task definition says: “Research the neighborhood demographics. Pull competitor pricing within 3 km. Generate 3 post variants in our tone of voice. Attach relevant visuals. Send to my review queue.”

The second one runs without me. I approve or redirect. That’s it.

This is what the future CMO looks like. Not someone with 15 direct reports. Someone with 3 humans and 15 agents. The humans handle client-facing work, strategic decisions, and the messy stuff that requires empathy. The agents handle everything that can be systemized.

The Real Question

Farzad asked: “How many people will be the 1-person companies vs the 9 that get displaced?”

From what I’ve seen, it’s not about displacement. It’s about amplification. The marketers who learn to architect agent systems won’t lose their jobs. They’ll become 10x more valuable. They’ll run departments that used to need 20 people with a team of 5 humans and 20 agents.

The ones who refuse to learn? They’ll compete for the remaining traditional roles. And there will be fewer of those every quarter.

What This Means Practically

If you’re a marketer reading this, here’s what I’d suggest:

1. Start small. Pick one repetitive task you do weekly. Research, reporting, content drafts. Build an agent workflow for it. Not a single prompt. A system with inputs, outputs, and quality checks.

2. Learn the tools. OpenClaw, Claude, custom GPTs, n8n, whatever fits your stack. The specific tool matters less than understanding the architecture.

3. Think in systems, not prompts. A prompt is a question. A system is a team. You want the team.

4. Document everything. Your agent’s personality, its tools, its boundaries, its failure modes. If you can’t describe it in a file, you can’t scale it.

Bottom Line

The AI Agent Architect isn’t a future job. It’s a current job that most people haven’t recognized yet.

I know because I’m doing it. Every day. From a CMO chair in Dubai, managing a virtual team that never sleeps, never calls in sick, and costs less than one junior hire per month.

The question isn’t whether this will happen. It’s whether you’ll be the architect or the one wondering what happened.

What’s your move?

Vibe marketing. You’ve probably seen the term floating around LinkedIn by now. Salesforce wrote a guide. MarTech published a manifesto. Fortune 500 companies are “experimenting.” Searches for the phrase grew 700% in the past year.

And yet almost every article about it describes a concept without handing you the tools. “Use AI for marketing!” Great. Which AI. Doing what. With what knowledge. On which task. The advice stops right where the actual work begins.

I’ve been using vibe marketing daily for months. Not as a concept. As a practice. And what I’ve learned is that the gap between people who talk about it and people who do it comes down to one thing that almost nobody mentions: skills.

Not your skills. The AI’s skills.

What vibe marketing actually feels like

Let me describe a Tuesday.

I’m working on a landing page for a new product. Instead of staring at a blank doc, I tell my Claude agent: run a CRO analysis on this page. The agent doesn’t ask me what CRO means. It doesn’t give me a generic list of “best practices.” It pulls a conversion rate optimization framework, checks my product context that it already knows from previous sessions, and walks through the page section by section. Value proposition clarity. Headline structure. CTA placement. Trust signals. Friction points. Objection handling.

Twenty minutes later I have a structured report with specific recommendations, copy alternatives, and a list of things to A/B test. Not because the AI is brilliant. Because it has a skill installed that knows how CRO works, and it applied that skill to my specific situation.

That’s vibe marketing.

Not “write me a tagline.” Not “brainstorm 10 ideas.” Not the copy-paste loop that most marketers are stuck in with ChatGPT. An agent that understands a marketing discipline, knows your context, and runs a real workflow.

The term came from Andrej Karpathy’s “vibe coding.” Describe what you want, the machine handles execution. In software, this turned non-programmers into people who ship products. In marketing, the same shift is happening. But most marketers don’t see it yet because they’re still using AI like a search engine with a personality.

Skills, not prompts. This distinction changes everything.

Here’s the thing that made the whole concept click for me.

A prompt is a one-shot instruction. You type “write me an email sequence,” get some output, close the window. The AI forgets everything the moment you leave. Next time you ask, you’re explaining your product from scratch. Your audience from scratch. Your brand voice from scratch. Every. Single. Time.

A skill is something else entirely. It’s a structured knowledge module that lives inside the agent. It contains frameworks, methodology, cross-references to other skills, and a workflow that unfolds step by step. When the agent has a marketing skill installed, it doesn’t just respond to your request. It recognizes what type of marketing work you’re doing and applies the right approach automatically.

A developer and marketer named Corey Haines published an open-source collection of over 35 marketing skills on GitHub. Not prompts. Skills. CRO analysis. SEO audits. Email sequences. Cold outreach. Pricing strategy. A/B test design. Launch playbooks. Schema markup. Churn prevention. Content strategy. Ad creative generation. Sales enablement. RevOps pipeline design.

And they connect to each other. The copywriting skill knows about CRO. CRO links to A/B testing. A/B testing references analytics tracking. It’s a system, not a folder of templates.

There’s also a foundation layer called product-marketing-context. It’s the first thing every other skill checks. It stores your product description, your ICP, your positioning, your competitive landscape. So when you run any skill, it already knows who you are and what you sell.

You ask your Claude to install these skills. One command. That’s it. Your agent just absorbed more structured marketing knowledge than most junior marketers learn in their first two years.

Nobody is getting replaced. Let’s stop pretending that’s the story.

I need to address this because the LinkedIn crowd won’t stop with it. “One person with AI replaces a marketing team of ten.” I’ve seen the posts. I’ve seen the engagement. And I think it’s wrong. More than that, I think it hurts adoption because it scares the exact people who would benefit most.

Here’s what’s actually happening. And it’s more interesting than the replacement fantasy.

Each person on a marketing team can now work with dramatically more reach. Not because the AI does their thinking. Because the AI handles the framework, and they bring the judgment.

Your SEO specialist still needs to make strategic calls. But instead of spending three days manually crawling a site, they run an seo-audit skill. The agent produces a structured report in twenty minutes. The specialist’s job shifts from running the audit to interpreting results and deciding what actually matters for the business.

Your copywriter still brings voice and cultural context that no model can replicate. But instead of starting from a blank page, the copywriting skill provides frameworks for headlines, CTAs, value propositions. All calibrated to your brand. The copywriter’s time goes from raw generation to sharpening, directing, making it sound like a human who gives a damn.

Your growth lead still decides which channels to test. But the marketing-ideas skill surfaces tactics filtered by product type, stage, and audience. The lead applies judgment about what fits the team’s capacity and budget.

Same team. Same headcount. Different output. That’s what vibe marketing changes in practice. Not the org chart. The throughput.

ChatGPT is the wrong tool for this

I want to say this directly because I know where most marketers live. They live in ChatGPT. And for what they use it for, it works fine.

ChatGPT is a great reference. You ask, you get an answer. Quick research. Brainstorming. A rough draft when inspiration is dead. It does that job.

But vibe marketing needs something ChatGPT isn’t built for. An agent. Something that reads your files. Runs multi-step workflows. Installs skills. Connects to your analytics, your CRM, your CMS. Remembers what you told it last week. Executes without you holding its hand through every click.

Right now, Claude is where this happens. Claude Code for people who aren’t afraid of a terminal. Claude console for everyone else. Both support skills. Both run complex marketing workflows. Both maintain context.

If you want full control, there’s OpenClaw. Open-source. Runs Claude as a persistent agent on your machine. Channels for different workstreams, scheduled tasks, memory that carries across sessions. I use it daily. It changed how I think about what a “marketing tool” even means. But that’s a deeper dive for another post.

The point is: if you’re doing the copy-paste loop in ChatGPT and calling it “AI-powered marketing,” you’re leaving the actual capability untouched. The AI that can do real marketing work exists. It just doesn’t live where you think it lives.

How to start. Actually start.

If you’ve never touched a coding agent, if the word “terminal” gives you anxiety, go to codingformarketers.com. Corey Haines built it for exactly this situation. A step-by-step guide for non-technical marketers learning to work with AI agents. How to open a terminal. How to control what the agent does. How to review its work and build confidence without breaking anything.

The mental shift the site teaches matters more than any specific tool: stop asking “what can I build?” and start asking “what can I delegate?”

If you already use Claude, your next step takes five minutes. Ask your Claude to install the marketing skills from the GitHub repository. Let it set things up. Then pick one skill that matches your most painful current task and run it against a real project.

Not a test. Not a hypothetical. Something you’d normally spend hours on.

Pay attention to what it produces. Some of it will be good. Some will miss the mark. That gap between the skill’s output and what you’d actually ship? That’s your expertise. The skill handles the framework. You bring the taste.

The window is still open

I’m not going to pretend that installing 35 marketing skills makes your Claude a CMO overnight. It doesn’t. What it does is give you something to test. A way to feel what vibe marketing is in practice, not in a LinkedIn post.

From what I’ve seen over months of daily use, the people who start building this muscle now will have a real advantage. Not because the technology is magic. Because most marketers still treat AI like a search bar, and anyone who treats it like a coworker operates at a different speed.

The skills are free. The tools are available. The learning resources exist. The only question is whether you start now, while most of your competitors are still copy-pasting ChatGPT outputs into Google Docs, or wait until this becomes standard practice and the window closes.

I’d start now. Today. Pick one skill. Run it. See what your Tuesday looks like after.

Felix Kjellberg (aka PewDiePie) got a black screen six times.

He was trying to install Linux on his Steam Deck. Each time — black screen. Each time — he tried something else. By attempt number six, most people would have closed the laptop, told themselves it wasn’t for them, and moved on.

He didn’t. He kept going.

7.6 million people watched that video.

His very first video was like this 15 years ago :) Feel the difference!

I don’t think they watched because Linux is interesting. I think they watched because something about watching a person refuse to quit — in public, in real time, while visibly frustrated — is deeply, uncomfortably resonant.

We know we’re supposed to push through. We almost never do.

The most important thing

Here’s the thing most learning content won’t tell you: the discomfort is not a side effect. It’s the mechanism.

When something breaks, and you don’t know why, your brain has to actually engage. When there’s no tutorial for this specific combination of broken things, you have to think. When you sit with confusion long enough that you start asking better questions — that’s when learning happens. Not before. Not at the comfortable part.

The error message isn’t a sign you’re failing. It’s the class.

ThePrimeagen — a developer who turned his career into content — made a video about this called Embracing Failing. His take, stated plainly: the people who actually get good at technical things are the ones who accumulate enough errors that they stop being afraid of them.

“I have become so accustomed to failure, you have no idea. I have graveyards full of garbage data I thought was the best.”

He says this like it’s a badge. Because it is.

The graveyard of bad attempts is the actual portfolio. Not the finished work. The finished work is just the surface. Underneath it is every broken thing that taught you how to fix it.

PewDiePie spent 18 months learning in public. Watch the sequence.

Felix Kjellberg’s transformation over the last 18 months is the best documented case study of learning-through-pain I’ve seen in the public eye. Not because he’s exceptional at it — but because he’s visible at it, and he doesn’t edit out the hard parts.

October 2024. I Drew Every Day for 365 Days — 6.7 million views. A full year of being bad at drawing. Publicly. The early work was genuinely terrible and he didn’t hide it. This established the template for everything that followed: show the process, including the parts that hurt.

April 2025. I installed Linux (so should you) — 7.6 million views. Black screen six times. Tries again. Eventually lands on a fully customized Arch system. His conclusion after weeks of frustration: “Open source is always the best source. [...] Finally free. I escaped the Windowscape.”

June 2025. I’m DONE with Google — 7.3 million views. He removes Google from his entire life. Self-hosted everything. GrapheneOS on his phone. More pain, more configuration, more broken things fixed one at a time. When it finally works: “Google is at my mercy for once. Feels good. [...] Amazing. Finally, I’m free.”

September 2025. help, I’m going through a midlife crisis... — 2.4 million views. The most honest one. Not a content strategy. Just a person saying: this is hard, I don’t know exactly what I’m doing, I’m in the middle of it.

October 2025. STOP. Using AI right now — 4.6 million views. He trains his own language model. It performs worse after his training. He says so directly. He doesn’t reframe it as a learning moment. It just happened, it was bad, he kept going anyway.

That last one matters. He ran a model that he made worse. He said so on camera. And then he kept working on it.

That is the whole lesson in one moment.

We built systems designed to remove the pain. That was the mistake.

The generation now learning — and honestly, most of us who learned anything in the last decade — grew up with frictionless everything. Instant answers. Tutorials optimized for zero confusion. Systems designed to remove every possible point of difficulty.

The error message became a product failure, not a learning signal.

The result: people who feel busy learning but aren’t actually learning. They’re managing the appearance of learning. Finding a clearer tutorial. Reading more theory. Waiting until they feel ready. Avoiding the broken state as if it’s a threat.

Here’s what Linus Torvalds — the person who built the operating system Felix spent weeks installing — said about this:

“I happen to enjoy doing things I’m not good at. Because that’s how you learn.”

Not “failure teaches you lessons.” Not “mistakes are opportunities.” Just: I like doing things I’m bad at. Because that’s how it works.

The pain of not knowing is not a warning. It’s an invitation.

Why 7.6 million people watched a Linux install

By every metric of content logic, that video shouldn’t have worked. No hook. No memes. No gaming. A notoriously difficult operating system. An audience built on completely different content.

It got 7.6 million views. And then something measurable happened outside the video.

Reddit’s r/linux community — 11,000 upvotes — documented the moment: “Pewdiepie picks a fight against Google, installs GrapheneOS to his phone, he even installs Archlinux into his Steam Deck.”1 The Linux Mint team published an official welcome in their project newsletter: “We have a new user! Welcome to Linux, PewDiePie!”2 A separate thread in r/linux noted that “Linux market share surpasses 6% — SteamOS and Pewdiepie brought a new hype to Linux.”3 There was even a r/linux4noobs thread titled “Please do NOT try Arch Linux just because PewDiePie did” — 2,900 upvotes — which is the community’s version of a welcome problem.4

What moved people wasn’t the technical content. It was watching someone they’d trusted for 15 years hit a wall — repeatedly — and not stop.

That’s more persuasive than any tutorial. The tutorial shows you how. Felix showed you that when it breaks (and it will break), you don’t stop.

The physics of it

Here’s what actually changes when you push through long enough.

You don’t gain confidence. That’s not quite right. You gain something more useful: indifference to the error state. You stop reading the broken thing as evidence of unsuitability. You start reading it as data. Where did the system and my understanding diverge? That’s all it is.

Once you’ve seen enough of the same broken thing, your brain builds a map. Not consciously. Just through repetition. You start recognizing patterns in how things fail. You make better guesses. You process problems faster — not because you’re smarter, but because you’ve been here before.

Every hurdle you clear becomes permanent. Every broken thing you fixed means that flavor of broken no longer scares you.

These compounds. Slowly at first. Then embarrassingly fast.

But it only starts compounding if you don’t stop when it hurts.

The practical part

If you’re learning something right now — code, design, writing, a language, a system, anything:

Stop optimizing for smooth. The tutorial that never breaks is fine for orientation and useless for learning. At some point, you have to get lost. The sooner the better.

Pick something real. Not a practice project. Something with stakes. Something where “good enough” isn’t actually good enough. Pressure changes what you pay attention to.

Read the error before you Google it. Actually read it. Try to understand what it’s telling you before you outsource the thinking. This one habit compounds more than most courses.

Set a failure quota, not a success quota. “I’m going to hit 10 problems I can’t immediately solve” is a better learning goal than “I want to build X.” The shift from output to process removes most of the psychological weight.

Stop hiding the graveyard. Your failed attempts aren’t evidence you’re bad at this. They’re evidence you’re doing it.

The real message

Felix didn’t become a Linux user because he’s technically gifted. He became one because he refused to stop when it got hard. Six black screens. Weeks of configuration. A self-hosted setup built error by error.

He got a midlife crisis video out of the experience. And 2.4 million people watched because somewhere in the title — “help, I’m going through a midlife crisis” — they recognized something true about what learning actually feels like when you do it honestly.

It feels like a midlife crisis. That’s fine. The feeling passes. The knowledge stays.

“I am way too stubborn for that. I just can’t. I don’t have it in me.”

That’s him. That’s also the whole answer.

Pain in learning is not a signal to stop. It’s a signal that something real is happening.

Stay.

I write about marketing, AI, and what happens when you actually pay attention. If something here was useful — share it.

Notes

Jensen Huang said that in March 2026 at the Morgan Stanley TMT Conference. He was talking about OpenClaw — an open-source personal AI agent framework that went from 0 to 309,000 GitHub stars in four months. He noted that Linux took 30 years to reach comparable adoption. OpenClaw did it in three weeks.

The person who built it: Peter Steinberger, creator of PSPDFKit, coding on a laptop. The person who lit the match: Alex Finn, a YouTuber with under a million subscribers. The video title: “ClawdBot is the most powerful AI tool I’ve ever used in my life.”

That’s the setup. Here’s what happened next.

The match

January 24, 2026. Alex Finn posts a tutorial. 964,000 views, 29,000 likes. Not a big channel by YouTube standards. But the video had one specific ingredient: a section at 5:42 called

“Do you need a Mac Mini?”

That timestamp created a narrative. Not “here’s a software tool.” Instead: “here’s a reason to buy a $500 piece of hardware and turn it into an always-on personal AI server.” The Mac Mini M4 — quiet, 6–12 watts, sitting on your desk — became the physical home for something that previously lived only in the cloud.

Within 72 hours, five other channels published tutorials with the same format: “I bought a Mac Mini for this.” The genre existed before the tools to fill it. OpenClaw gave it content.

January 30: Fireship covers it. 1.8 million views. The rebrand to “OpenClaw” happens the same day.

February 12: episode #491 with Steinberger. Title: “Viral AI Agent that Broke the Internet.” 1 million views. This is no longer a developer product — it’s mainstream.

By this point, the project is gaining 25,000 GitHub stars per day. Total ecosystem around it: 5,400+ community-built skills, a dedicated skills repository at 36,000 stars. Cloud providers scrambling — Scaleway launches managed Mac Mini M4 instances specifically for OpenClaw. Lightning.ai offers one-click deployment. Useclawy.com offers a $99 lifetime hosted option with “no Mac Mini needed.”

The “no Mac Mini needed” narrative exists as a direct response to a Mac Mini demand spike that nobody planned.

What the industry did next

February 15: Steinberger announces he’s joining OpenAI. He notes that both Zuckerberg and others reached out. He chose OpenAI.

Read that again. The creator of a product that went viral in January gets calls from Meta’s CEO by February. He joins the most valuable AI company in the world the following week.

OpenClaw itself moved to an open-source foundation. The project didn’t disappear — it accelerated.

February 24: Notion launches Custom Agents v3.3. Up to 20 minutes of autonomous operation. Claude Opus, GPT-5.2, Gemini as model options. 21,000 custom agents created by early testers. 2,800 running continuously.

February 27: Perplexity launches “Perplexity Computer.” A cloud-based agentic system unifying 19 AI models, running autonomous agents in isolated environments for hours. $200/month. Press coverage explicitly describes it as “Perplexity’s own OpenClaw.”

They’re not even pretending it’s unrelated.

Meanwhile, Meta acquired Manus — a Singapore-based autonomous agent startup — for over $2 billion in late 2025. Manus scored 86.5% on the GAIA benchmark versus OpenAI’s 74.3%. It’s now being absorbed into Meta AI across Facebook, Instagram, WhatsApp, and Messenger.

Google has Project Jarvis. Microsoft has Copilot Pages with “digital coworkers.” Apple is positioning Intelligence 2.0 as Siri-as-personal-agent. On March 10, Tencent launched QClaw — a WeChat-integrated clone. 1,000 users queued on day one.

The entire personal AI agent category went from “interesting developer experiment” to “everyone’s core product roadmap” inside 60 days.

The Anthropic irony

Here’s the detail worth pausing on.

The model that powers OpenClaw is Claude — built by Anthropic. Most OpenClaw users are running their agents on Anthropic’s infrastructure.

Anthropic has no consumer-facing agent product. No OpenClaw equivalent. No personal AI framework. Their computer use capabilities exist at the API level for developers.

They built the engine. Someone else built the car. The car sold 309,000 units in four months, triggered a $2B acquisition by Meta, recruited the creator to OpenAI, and forced every major tech company to accelerate their agent roadmap by at least a year.

Anthropic gets the API revenue. The narrative belongs to someone else.

What this is actually about

This is a story about how viral software works in 2026, but it’s also a story about something older: the power of a concrete narrative over an abstract capability.

AI agents existed before OpenClaw. OpenAI had them. Anthropic had computer use. Google had project-level assistants. The technology was available.

What OpenClaw had — specifically, what Alex Finn’s video created — was a physical anchor. A $500 device you could buy on Amazon, plug in, and point at a piece of software. “You own this. It runs on your hardware. It works while you sleep.” That’s not a feature. That’s a story.

The Mac Mini wasn’t marketed as an AI server. Apple didn’t plan this. A YouTuber with under a million subscribers created the use case by describing it clearly at timestamp 5:42.

Then Fireship made it credible. Then Lex Fridman made it mainstream. Then Jensen Huang made it historic. None of this required a marketing budget. It required one person to describe something concretely enough that other people could imagine themselves doing it.

The uncomfortable question for everyone building AI products

Every company on this list — OpenAI, Anthropic, Google, Meta, Perplexity, Microsoft — has larger teams, larger budgets, and earlier access to the underlying technology than Peter Steinberger did.

They were all working on agents. None of them produced the moment that validated the category.

A vibe-coder working nights and weekends did. A YouTube creator with 800,000 subscribers made it tangible. And then the world's most innovative companies spent the next 60 days scrambling to respond.

The lesson isn’t “vibe coding beats enterprise engineering.” The lesson is that narrative moves faster than R&D, and the person who makes something concrete and relatable first owns the category — regardless of who built the better technology.

If you’re building anything in AI right now, the relevant question isn’t “is our product technically superior?”

It’s: “Does someone with a camera and 800,000 subscribers have a reason to make a video about it?”

IMPORTANT: The interface is in Chinese, so all screens you'll see are shown via screen translation.

Today I ran a five-year simulation of a fitness club network operating in Ukraine — under conditions of active war, demographic decline, and accelerating population outflow.

76 virtual people participated. Each had a personality, a memory, a bias, and a stake. They argued, reacted to events, and changed their behavior across simulated quarters. The system tracked how each audience segment — young professionals, families, older members, emigrants who might return — shifted their relationship to the brand over time.

The tool that made this possible: MiroFish. A 20-year-old student built it in 10 days using vibe coding. It has 15,500 GitHub stars. Shanda’s founder Chen Tianqiao committed $30M to it within 24 hours of seeing the demo.

I spent around 24 million tokens running the simulation. That number matters. Let me come back to it.

Can you imagine the cost of this research via Opus? :)

What MiroFish Actually Does

You feed it a document — a brand research report, a market analysis, a news package, anything with structured context. I had a Brand Health Monitor Report done by the external agency with the interviews of 600+ respondents all over the country. The system reads it, builds a knowledge graph of every entity mentioned: people, organizations, audience segments, media outlets, and competitors. Each node in that graph becomes an agent.

Each agent gets a full persona: age, gender, MBTI, professional context, values, communication style, and a bias score toward the topic you’re studying. Then you set the simulation parameters — a five-year horizon broken into quarterly rounds, with events injected at each period (a new wave of emigration, an economic shock, a competitor opening nearby, a ceasefire announcement).

In each round, every agent sees a shared information feed and decides: post, comment, react, or stay silent. The decisions come from their character and their memory of previous rounds. The system records the dynamics — who leads opinion, who goes quiet, which communities form, which segments disengage.

At the end, a Report Agent synthesizes everything into an analytical brief. You can interrogate it.

The Simulation I Ran

The question I asked: How will different audience segments of a Ukrainian fitness brand behave over 5 years given demographic decline, youth emigration, and sustained probability of active conflict?

The system generated 76 agents from a brand research document. That’s 76 synthetic stakeholders — not survey respondents, not hand-crafted personas, but entities extracted directly from real research data and given behavioral autonomy.

Events injected across the simulation horizon:

• Continued military operations, periodic escalation

• Accelerating demographic shift (-15% active population by year 3)

• Economic compression is reducing discretionary spending

• Emigrant communities forming abroad with partial brand loyalty

• Competitor consolidation in the market

The results were specific in a way that focus groups rarely are. Certain segments showed resilience. Others showed a clean exit trajectory. The model identified which audience clusters would become the core of the business by year five — and which were already gone regardless of what the brand did.

The Number That Puts This in Context

24 million tokens sounds abstract until you compare it to what this analysis would cost done traditionally.

The global market research industry is worth $84.6 billion per year. A McKinsey or BCG strategy engagement covering market trajectory analysis runs $500K–$1.25M and takes 8–12 weeks with a team of 5–15 analysts. A RAND Corporation demographic research contract: $500K–$5M. A custom agent-based simulation model built by an academic team: $50K–$500K and 3–12 months of development.

Traditional consumer survey covering 1,000 respondents: $50,000–$150,000. A 5-year market forecast from a top consulting firm: $150,000–$500,000+.

The 40M tokens I spent on MiroFish using a cost-efficient LLM (MiniMax M2.5 via OpenRouter at $0.27/1M input tokens) cost roughly $8–15. With a less optimized model choice, the same run could reach $300–500. With GPT-4, you’re looking at $1,000–3,000 for the same simulation.

This is a 1,000x–10,000x cost reduction against the traditional benchmark. The time compression is equally dramatic — hours instead of months.

What This Means for the $30 Warning

24 million tokens is a lot. At GPT-4 pricing, that simulation would cost $1,500+. It’s easy to run up a significant bill if you’re not paying attention to model choice.

• The technical reality: MiroFish consumes 14,000–18,000 input tokens per agent per round. Multiply by 76 agents and 40 rounds, and the token math compounds quickly. A few specific rules before you start:

• Choose your LLM carefully. MiniMax M2.5 ($0.27/1M input) or Qwen-plus ($0.30/1M) are purpose-built for high-volume agent workloads. GPT-4 is 10–15x more expensive for the same task. Run the math before you hit start.

• Start small. 20 agents, 15 rounds. That’s a valid test that costs under $3 and tells you whether the simulation is giving you a useful signal.

• Scale deliberately. Only scale to 76+ agents when you have a question worth the compute.

Who Should Run This Before Anyone Else

Every CMO doing demand forecasting for a new market entry. Every founder assessing product-market fit in a volatile environment. Every strategist trying to understand how different audience segments will react to a positioning change, a price increase, or a crisis.

This is particularly powerful at business launch. Before you commit to a market, you can simulate how the market responds — not based on gut feel or a 100-person survey, but based on a structured behavioral model of everyone relevant to your success.

The tool is not perfect. LLM agents behave the way the model thinks a persona would behave — not exactly like real humans. Quantitative predictions are unreliable. But for directional intelligence — understanding the shape of a market’s response, identifying which segments are structurally your allies and which are structurally indifferent — it’s genuinely useful.

[SCREENSHOT 5: Report Agent output — the final analytical brief generated after simulation completes. Show the structured findings about audience segment behavior, key insights, and the conversational interface where you can ask follow-up questions.]

The Shift That Just Happened

The global market research industry exists because understanding how humans will behave at scale used to require scale to study. You needed large teams, long timelines, expensive methodologies.

The computing equivalent of a $500,000 consulting engagement is now accessible for the cost of a few API calls. The analysis that a team of 15 McKinsey analysts would take 10 weeks to produce can be approximated — with different tradeoffs — in an afternoon.

This doesn’t replace judgment. You still need to know what question to ask, how to interpret the output, and what to do with the findings. The analytical intelligence still has to come from somewhere.

But the infrastructure for simulation at scale? That just became a $100 decision instead of a $500,000 one.

MiroFish is early, imperfect, and running on a GitHub repo that a student shipped in 10 days. It’s also a preview of what market intelligence will look like in three years.

If you’re in marketing, now is a good time to get familiar with it before it becomes table stakes.

I find this funny. But I also think it points to something real.

Two rooms, zero overlap

Here’s what’s actually happening right now, across two very different communities:

In Discord — Hugging Face servers, AI agency communities, agent-builder channels — there are tens of thousands of people fine-tuning models, shipping automation tools, building multi-agent systems, running prompt experiments, and figuring out what AI can actually do in production. They move fast. They break things. They share findings in threads that disappear in 48 hours and never make it to a newsletter.

On LinkedIn — the feed that senior marketers actually check — there are thought leaders posting about “leveraging AI for content strategy.” Sharing articles about which tools will “transform marketing in 2026.” Explaining AI to audiences who nod along and save the post.

These two groups are not talking to each other. And both are paying for it.

What builders are missing

The Discord builders can ship. They understand the technical layer — agent orchestration, function calling, RAG pipelines, and eval frameworks. They know what’s actually possible right now, not just what’s in the press release.

What many of them can’t do: explain why it matters to a customer. Write positioning that lands. Understand why a technically impressive automation doesn’t convert. Figure out which problem is worth solving before building the solution for it.

Marketing judgment is slow to develop. It comes from years of reading customer interviews wrong, launching campaigns that flopped despite the brief looking solid, watching a price change kill retention in a segment you didn’t model. You can’t shortcut it with a GitHub repo.

So builders ship things that are technically interesting and commercially vague. They optimise for capability, not for the gap in the market.

What strategists are missing

Senior marketers have the judgment. They know how to read a market, build a brand narrative, structure a go-to-market, set a pricing architecture. They’ve been in enough rooms to recognise the difference between a good idea and a fundable idea.

What most of them don’t have: direct exposure to what’s actually being built right now, at the execution layer.

And this matters more than it sounds. Because the execution layer is being rebuilt from scratch.

The assumption that “we’ll hire developers for the technical parts” holds when the technical parts are stable. When the infrastructure is in motion — when the tools for automating research, content, analysis, and coordination are changing month by month — not understanding that layer means making strategic decisions with wrong assumptions about what’s possible.

The strategists are optimising for a distribution channel that’s quietly becoming less relevant. Running campaigns for traditional search while AI-referred traffic grows 527% year-over-year. Building SEO strategies without realising a WAF is blocking every AI crawler. Hiring teams for tasks that will be automated before the new hire finishes onboarding.

Why they don’t meet

Part of it is pace. Discord moves in hours. LinkedIn moves in weeks. By the time a technical insight becomes a LinkedIn carousel, the builders have already moved past it.

Part of it is identity. “Marketer” and “builder” feel like different job descriptions, different kinds of people. The assumption is that strategy and execution are separate disciplines requiring separate humans.

Part of it is that the overlap — people who have genuine strategic judgment AND direct technical fluency — is genuinely rare. Most people develop one or the other. Crossing over requires time that most senior professionals don’t have, and intellectual humility that’s hard to maintain after years of expertise in one domain.

What happens when they do meet

The interesting cases right now are people and teams where both layers are present. Not always in one person — sometimes it’s a strategist who understands enough to have a real conversation with builders. Sometimes it’s a builder who’s spent enough time on commercial problems to know what’s worth shipping.

What they share: the ability to ask “what problem does this solve?” and “can we actually build this?” in the same conversation, without one question killing the other.

The output is different. Not just technically impressive. Not just strategically coherent. Actually useful — and actually buildable.

One example from my own setup: I built an AI agent system for marketing operations. Named agents, role specs, orchestration logic. The strategic layer — knowing what each agent should optimise for, what the actual output should look like, how to measure whether it’s working — required years of marketing judgment. The execution layer — building it, debugging it, making it run — required direct technical engagement that no strategy document could replace.

Neither layer alone would have produced anything worth using.

The window

Only 23% of marketers are actively working on AI visibility and automation. The builders largely lack the commercial frame to turn their capabilities into marketed products. The strategists largely lack the technical fluency to know which capabilities to bet on.

That gap is a window. It won’t stay open.

The people who figure out how to operate in both rooms — or how to build genuine bridges between them — are going to have an asymmetric advantage for the next few years. Not because they’re smarter. Because they’re less siloed.

Happy to in the both of the worlds, but it is crazy hard.

CIM recognition of my modest person

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A couple of weeks ago I stumbled on an open-source project called geo-seo-claude. GEO stands for Generative Engine Optimization — the practice of making your website visible to AI-powered search engines rather than just Google’s traditional crawler. The tool is a set of Claude Code skills that runs a full AI-visibility audit on any URL. Free, open-source, no API costs beyond Claude Code itself.

Install it for yourself: ttps://github.com/zubair-trabzada/geo-seo-claude

I ran it on my website.

35/100. Poor.

Here’s the thing, though. The score itself wasn’t the most interesting part. What got me thinking was how the tool works under the hood.

Five agents, one audit

When you run /geo audit <url>, the tool doesn’t work sequentially. It spawns five parallel subagents, each with a different specialty:

• AI Visibility agent — checks citability scores, AI crawler access, brand mentions across platforms

• Platform Analysis agent — tests readiness for ChatGPT, Perplexity, and Google AI Overviews separately, because each platform weighs signals differently

• Technical SEO agent — crawlability, security headers, Core Web Vitals risk, server-side rendering

• Content Quality agent — E-E-A-T signals, content freshness, expertise attribution

• Schema Markup agent — structured data detection, validation, and gap analysis

They run simultaneously. A synthesis layer then aggregates the scores into a composite GEO Score with weighted categories. The whole thing takes a few minutes.

The architecture is interesting because it mirrors how a consulting team would approach an audit — not one generalist doing everything sequentially, but specialists working in parallel. Except this costs you nothing and doesn’t require a kickoff call.

What the tool found on my website is a useful illustration of why this matters.

The robots.txt lies

My website’s robots.txt file was politely inviting every AI crawler in. GPTBot — welcome. ClaudeBot — come right in. PerplexityBot — door’s open.

The WAF (web application firewall) was having none of it.

Every non-browser request hit a 429 error with a JavaScript challenge. Policy said one thing, infrastructure said another. The result: every major AI search engine treated the site as if it didn’t exist. The sitemap was blocked too, so AI crawlers couldn’t even discover what pages were there.

That’s the kind of mismatch that makes every other optimization pointless. Perfect schema markup, clean E-E-A-T signals, a beautifully crafted llms.txt file — none of it matters if the crawler can’t get through the door.

Fix the firewall first. Everything else is noise.

The score breakdown

• AI Citability & Visibility: 28/100

• Brand Authority Signals: 35/100

• Content Quality & E-E-A-T: 42/100

• Technical Foundations: 34/100

• Structured Data: 38/100

• Platform Optimization: 34/100

Content scoring highest is both reassuring and slightly annoying. It means the substance is there — the actual expertise and information exists on the site. But the infrastructure and signals are failing it. Like having good products in a store with a locked door and no signage.

Why GEO is actually different from SEO

Traditional SEO optimizes for crawlers that index pages and return ranked links. You rank, users click, users arrive.

GEO optimizes for AI systems that consume your content and synthesize answers. If an AI doesn’t crawl you, doesn’t cite you, doesn’t have you in its knowledge base — you don’t exist in its answers. No ranking. No link. Just absence.

The numbers behind this shift are not subtle. AI-referred traffic grew 527% year-over-year. Gartner projects a 50% drop in traditional search traffic by 2028. Brand mentions correlate 3x more strongly with AI visibility than backlinks. And only 23% of marketers are actively doing anything about it.

That last number is the window. Most websites are currently invisible to AI search. Mine was one of them.

What gets fixed first

The tool outputs a prioritized action plan, not just a score. The top item was obvious once identified: whitelist AI crawlers in the WAF configuration. Low effort, critical impact. Everything else waits until that’s done.

After that: LocalBusiness schema for every location, comprehensive sameAs entity linking across platforms, author attribution on all content pieces.

Estimated score after Phase 1 fixes: 50-55/100. Not excellent. But visible — which is the baseline requirement for everything else to work.

The honest takeaway

If you’re a marketer and you haven’t thought about AI visibility yet, you’re optimizing for a distribution channel that’s quietly ceding ground. The users are moving. The traffic patterns are changing. The tools to audit this are now free.

Run the audit. The score might sting. The clarity is worth it.

Most people interact with AI the same way they use Google: open a tab, type a question, get an answer, close it. The difference is Google was never marketed as your personal assistant. ChatGPT was. And yet the architecture is identical — stateless, contextless, amnesiac by design. Every session you start, you’re explaining yourself to a stranger who has never heard of you.

I got tired of that about two months ago and decided to build something different. What I ended up with is a local setup I call Personal Super Agent — a folder on my home server with config files, skill scripts, memory files, and a scheduling system. The orchestration layer is an open-source tool called OpenClaw, which handles routing, context, and channel management. The interface is Discord, which I was already using for everything else anyway.

The way it actually works day-to-day: I have a Discord server with channels for different areas of work — content, trading research, projects, ops. Each channel is its own persistent session. When I open #substack, the agent knows we’ve been discussing blog topics. When I switch to #trading, it knows about positions and market research. Context doesn’t bleed between channels, and nothing resets. This morning it suggested this post topic on its own, based on what we built yesterday. I didn’t ask. That’s the whole point.

The server runs cron jobs on a schedule. At 7am Dubai time every morning, one of them pulls my calendar, emails, and recent work logs, then posts a briefing to my Telegram. Another one — the one that triggered this post — reviews yesterday’s activity and suggests five content ideas at the same time. There’s also a mission control channel where I can see the state of everything: which jobs ran, what they produced, where something failed. It’s not a polished dashboard, but it tells me what I need to know.

The stack itself is unremarkable. OpenClaw for orchestration, a Linux server for persistence, Discord as the main interface, Python scripts for specialized tasks like image generation or web search. Nothing exotic. The interesting part isn’t the technology, it’s the decision to treat the AI like infrastructure rather than a tool you visit.

That shift — from tab to persistent system — changes what’s possible. An AI that lives somewhere accumulates. It knows what you decided last week, what you’re working on right now, what your voice sounds like, what you care about. An AI in a tab knows none of that, no matter how good the model is. The gap between them isn’t a feature gap. It’s an architectural one.

Last month my marketing budget got cut by 27%. Season gone, Not trimmed — cut. Q1 was already behind. The gap wasn’t going to close itself.

The usual move: renegotiate, replan, try to do more with less. I did something different. I spent two days building a trading bot on a prediction market.

Not to escape the problem. To think about it differently.

Here are three things prediction markets taught me that no marketing framework ever did.

1. Price your beliefs, don’t just state them

When you place a bet on Polymarket, you don’t get to say “this looks promising.” You have to assign a number. Bitcoin above $85k by Friday — at what probability? You’re paying for that position. The cost makes you honest.

Most campaign briefs don’t work like this. Someone says “we think this channel will perform.” No one asks: perform at what confidence level? If you had to buy that position for real money, what would you pay?

I started running my marketing assumptions through this filter. If this were a Polymarket contract, what would I price it at? Turns out a lot of “strong hypotheses” price out at around 55%. That’s barely better than a coin flip. Not where you want to cut the budget.

Expected value isn’t a trading concept. It’s the thing you’re actually calculating when you allocate spend — you’re just usually doing it wrong.

2. Bet sizing matters more than bet selection

My bot made 9 trades in one session. The entry logic was decent. The position sizing was not. It deployed too much capital into single markets, and when two of them went sideways simultaneously, the losses stacked.

In trading this is called poor bet sizing. In marketing we call it “doubling down on what works.”

Kelly Criterion — a formula used by serious traders — says your position size should be proportional to your edge, not your conviction. Conviction can be high and your edge can still be small.

When I had to reallocate a 27% smaller budget across channels, I stopped asking “where do we believe in most?” and started asking “where do we have actual edge?” — meaning: demonstrated results, defensible data, a mechanism we understand. The answer was different from what my instincts said. And it was right.

3. When the market knows something you don’t

Here’s the part I didn’t enjoy: the prediction market was right more often than I was.

I built the bot because I thought I could spot patterns. RSI signals, news sentiment, and short-term momentum. The bot won some, lost some. After 48 hours: roughly break-even minus fees. The market had already priced in most of what I “knew.”

Marketing has the same dynamic. When you’re convinced your channel is undervalued, it’s worth asking: why isn’t everyone else seeing this? Competitors, agencies, benchmarks — they’re a kind of market. When the whole industry moves away from a channel, there’s usually a signal in that.

Contrarian bets are expensive to be wrong about. Be sure you have information the market doesn’t — not just a different opinion.

The actual numbers

I started with $50. Built the bot in one day with an AI agent running locally on my machine. The bot entered 9 positions. Two paid out correctly. The rest were technically broken — a state tracking bug caused it to hold both YES and NO tokens on the same market simultaneously.

Lost nothing significant. Spent about $1 in transaction fees learning that correct position tracking is harder than correct trading logic.

Same lesson applies to every marketing automation project I’ve run: the edge isn’t in the idea. It’s in whether your measurement is accurate enough to know if it’s working.

Try it yourself. Put $20 on Polymarket this week. Not to profit — to practice assigning real probabilities to things you usually just assert. It takes about three losing bets to stop saying “I’m pretty sure” and start asking “what exactly am I pricing here?”

If this was useful, subscribe. I write about what I’m actually running — the experiments, the bugs, and what held up....

The Discovery

Pablo de Lucca built a VS Code extension that turns your Claude Code agents into animated pixel art characters. Each terminal spawns a little person who sits at a desk, types when the agent writes code, reads when it searches files, and walks around the office when it’s idle. Permission needed? A speech bubble pops up.

It’s adorable. It’s also genuinely useful. But I don’t use VS Code.

How It Actually Works

Here’s the beautiful part — the entire thing is stupidly simple under the hood.

Claude Code writes everything it does to .jsonl files in ~/.claude/projects/. Every tool call, every response, every turn completion — it’s all there as JSON lines. The extension just watches those files and translates events into character animations:

• tool_use with Edit or Bash → character types at their desk

• tool_use with Read or Grep → character reads (different animation!)

• turn_duration system event → turn is done, character stands up and wanders

• 7 seconds of silence after a tool call → permission bubble appears

That’s it. File watching + event parsing + pixel art. No magic.

Ripping It Out of VS Code

I wanted this running in a browser. Two files. No dependencies.

server.ts — A Bun server (~100 lines that matter) that watches ~/.claude/projects/ for .jsonl files, parses transcript lines, and broadcasts events over WebSocket.

index.html — A self-contained Canvas 2D client with all the sprites hardcoded as 2D arrays of hex colors. Characters walk using BFS pathfinding. Furniture is rendered with z-sorting. New agents spawn with a Matrix-style digital rain effect because why not?

The whole thing:

cd pixel-agents
bun run dev
# opens http://localhost:4242

Start Claude Code anywhere — Ghostty, iTerm, bare terminal, SSH session — a new character walks into the office and sits down at a free desk.

What You’re Actually Looking At

A 20×11 tile office with two rooms, desks, chairs, bookshelves, a water cooler, and plants. Six character palettes so each session gets a distinct look. When you have three agents running in parallel, you see three characters — two typing furiously, one getting up for a walk because it just finished its turn and is waiting for you to type something.

The permission bubble is the killer feature. Instead of alt-tabbing through terminals to find which agent is stuck, you glance at the pixel office. The one with the amber “...” bubble needs your attention. Done.

Try It Yourself

1. Clone or create two files from the pixel-agents standalone

2. Have Bun installed

3. bun run server.ts

4. Open localhost:4242

5. Start a Claude Code session anywhere

Or just feed this article to your Claude Code — it'll do everything!

The original VS Code extension by Pablo is excellent if you’re in VS Code. But if you’re a terminal person, like me, — this standalone version is the way.

Two files. Zero dependencies. Infinite charm.

For years we’ve been running language models the same way: you take a GPU, you load weights into memory, you push tokens through. The bottleneck was always the memory wall, that constant shuffle between where the model lives and where computation happens.

Taalas, a 25-person team mostly out of Tenstorrent, decided to skip that problem entirely. They took Llama 3.1 8B and literally baked its weights into the transistors of a custom chip. There is no separate memory. The model and the silicon are the same thing. Their first chip, HC1, runs on TSMC 6nm with 53 billion transistors, draws about 200 watts per card, and pushes 17,000 tokens per second on Llama 8B. For context, Cerebras does around 2,000 on the same model, Groq sits at 600, and a single Nvidia B200 delivers roughly 3.5x less throughput at comparable power. You can actually try it yourself at chatjimmy.ai, and the speed is honestly startling when you see it in real time.

0.058s per 15,747 tockent. You blink SLOWER!

The obvious question is: if the model is frozen into the chip, what happens when a better model comes out? You can’t flash new weights onto this thing. That’s a real constraint, and Taalas doesn’t pretend otherwise. But the economics change the calculation. Printing a new version of the chip through TSMC takes about two months and costs roughly 1% of what training the model cost in the first place. On top of that, they kept support for LoRA adapters and variable context windows, so you can still fine-tune and adjust behavior without touching the base weights. It’s not as flexible as a GPU, but it’s far from a dead end.

Where this gets interesting is what it means for the people who actually pay inference bills. If you’re an API provider serving millions of requests, swapping a rack of B200s for a box of these cards could cut your costs by an order of magnitude. If you’re building voice agents or real-time applications where every millisecond of latency matters, single-digit response times without batching change what’s possible. And for entire regions where Nvidia hardware is priced out of reach, a chip that costs 20 times less to produce suddenly opens a door that was locked. Taalas says they’ll have a 20B reasoning model on HC1 by summer and a frontier-class model on second-generation silicon by winter. I keep thinking about the moment this approach hits the 70B+ range. That’s when things get really wild.

TLDR: Github link see below.

If you missed the hype: Manus is an AI agent launched in March 2025 by a Beijing startup called Butterfly Effect. You give it a prompt, and it does the whole thing. Research, code, browser actions, file management, deployment. It topped the GAIA benchmark at launch, got a $500M valuation from Benchmark within weeks1, and Meta acquired it for $2B+ by December2. The pitch was simple: a general-purpose AI that works like a human assistant inside a virtual computer.

$39/month for 3,900 credits. Felt reasonable.

Then a guy named jian on X3 performed what he called a “trivially simple jailbreak.” He asked Manus to show the files in its /opt/.manus/ directory. And it just... complied.

His verdict: “It’s Claude Sonnet. It’s Claude Sonnet with 29 tools. It’s Claude Sonnet without multi-agent.” The whole thing was an E2B sandbox with obfuscated Python scripts wrapping an Anthropic API call.

I put my credit card away. For good :)

What Manus actually is

Co-founder Ji Yichao (Peak) confirmed it. They “employed Claude and different fine-tuned versions of Qwen” and were “internally testing Claude 3.7” after building on Sonnet4. The leaked system prompt is on GitHub for anyone to read5.

Zvi Mowshowitz wrote a full breakdown: “Manus appears to be a Claude wrapper, using a jailbreak and a few dozen tools, optimized for the GAIA benchmark, backed by an influencer-centered marketing campaign.”6

The pricing tells you the rest. A single complex task burns 900+ credits. Your $39 plan covers four or five real projects. Credits don’t roll over. Run out mid-task and it stops. No refund.

One model. No routing. A credit system that feels like gambling. And Mindgard gave their browser extension a security risk score of 100 out of 100, calling it a tool with “characteristics matching Remote Access Trojan definitions.”7

What I built instead

I already pay for Claude Code Max $100 per month (Opus 4.6 through my Anthropic subscription). It has file operations, shell, web search, and multi-agent tools built in. The question: what’s missing?

Five things. I closed all five.

1. Model Routing. Manus sends everything through one model. My setup routes by complexity. Architecture goes to Opus. Tests go to Sonnet. Validation goes to Haiku. If Haiku fails, it escalates to Sonnet, then Opus.

2. Data Priority. My datasource skill enforces a chain: local files first, then MCP servers (Strava, Spotify, GA4), then APIs (Perplexity, Parallel AI), then web search. Model knowledge is the last resort (it is always pretty outdated). The agent never guesses when a real source exists.

3. Context Anchor. Borrowed directly from Manus. Peak Ji wrote about why they rewrite a todo.md after every step: “By constantly rewriting the todo list, Manus is reciting its objectives into the end of the context. This pushes the global plan into the model’s recent attention span, avoiding ‘lost-in-the-middle’ issues.”8 My version: 30 lines max, re-read every 5-7 calls, loaded last on resume. Same fix, no credits burned.

4. Deploy/Expose. Preview: a cloudflared tunnel for instant public URLs, no account needed. Production: auto-routes to Vercel, Railway, or Netlify by stack. Both require explicit approval. Manus had a vulnerability where prompt injection could expose VS Code Server to the internet without consent.9 My approval gate is a hard stop, not a suggestion.

5. User Takeover. When the browser agent hits a login page, payment form, CAPTCHA, or 2FA, it stops. Shows the URL, waits for manual action. Manus tries to power through these, which is how security researchers found data exfiltration paths.10

The most important part

Building this took one afternoon. Not because I’m fast. Because the hard part was already done by Anthropic.

The actual work is writing good instructions. A CLAUDE.md file. Skill definitions. Routing rules. Text files. No infrastructure. No VMs.

Peak Ji: “If model progress is the rising tide, we want Manus to be the boat, not the pillar stuck to the seabed.” Smart framing. But the boat is sitting on someone else’s ocean.

That ocean is Claude. You can sail on it directly.

Is this for everyone?

No.

If you want to type a prompt and get a result without thinking about setup, Manus works. You’re paying for convenience.

But if you already use Claude Code and you’re comfortable editing markdown files, you can build something that does more. For free. Peter Wildeford put it well: “Manus made genuine innovations and seems useful! But it isn’t some massive advance.” I agree. The innovations are real. They’re just not worth $39/month.

Your call.

P.S. Take everything from my GitHub: https://github.com/baranskyi/manus-diy and give it a Star if it's worth it.

Sources

10

Peter Wildeford, “Being responsible with Chinese AI hype,” Substack, March 2025.

The Power Law

Being responsible with Chinese AI hype

Over the weekend, a previously unknown Chinese AI company called Manus captured the attention of tech Twitter with bold claims of having developed “the first general AI agent.” Already some users took to Twitter to claim Manus would “replace 50% of all white collar jobs” within six months and was…

Read more

a year ago · 23 likes · 4 comments · Peter Wildeford

Qwen 3 235B? Even with 48GB of combined VRAM it choked. DeepSeek R1 full? Sure, if you don’t mind watching paint dry between responses.

Sound familiar?

Here is why you should consider local LLMs

Here’s the thing. Most people pick local models the same way. They read a Reddit thread or X’s clickbait posts, grab whatever has the most upvotes, and pray it fits. No math, no hardware check. Just vibes and hope.

I got tired of this. So I found a tool that does the math for you.

The problem

Think about it. Running LLMs locally is a hardware puzzle. You need to match model size, quantization level, and your actual VRAM. Not what the spec sheet says, but what’s available after your OS and apps eat their share.

A 70B model at Q4_K_M quantization needs roughly 40GB. Even my dual 4090 setup with 48GB VRAM total can’t always keep it fully loaded; some layers spill into system RAM. Speed drops 5-10x the moment that happens. You turn a fast inference machine into a space heater.

I kept hitting this wall. So I searched and found LLM Checker, a CLI that scans your hardware and tells you which models will actually run well.

How it works

One command.

llm-checker check

That’s it.

The tool detects your GPU (NVIDIA, AMD, Apple Metal, Intel Arc), counts your VRAM, checks CPU cores, and measures available RAM. Then it scores 35+ models across four dimensions. Quality: how good is the model family, how many parameters, what does quantization cost you. Speed: how many tokens per second on your specific backend. Fit: does the model actually use your memory well or waste half of it. Context: can your hardware support the context window you need.

Each use case gets different weights. Coding models prioritize quality and context. Chat models lean toward speed. Reasoning models balance quality with fit.

Honestly, the math behind this is not complicated. Q4_K_M quantization runs about 0.58 bytes per parameter. A 7B model needs ~5GB. A 32B model needs ~19GB. But nobody does this calculation before downloading 40GB files. They just hit “pull” and hope for the best.

Why I actually use this

My workstation is a Ryzen 9 7900X with dual RTX 4090s and 128GB RAM. The tool detected it as “VERY HIGH” tier with 48GB VRAM across CUDA, max model size 46GB. That fingerprint alone tells me exactly where the ceiling is.

Before LLM Checker, I maintained a spreadsheet. Updated it manually. Got it wrong half the time because quantization math is tedious when you’re tracking 30+ models across different quantization levels. This drove me crazy. Even with 48GB VRAM I still need to know which 70B models fit fully in VRAM and which spill into my 128GB system RAM.

Now I run llm-checker recommend coding and get a ranked list in seconds. The tool even generates Ollama pull commands. No spreadsheet, no downloading a 40GB model just to find out half of it runs from RAM at a tenth of the speed.

The part that surprised me

What surprised me was the MCP integration. The tool plugs into Claude Code as an MCP server. One command:

claude mcp add llm-checker -- llm-checker-mcp

Now Claude can check your hardware, compare models, and suggest configurations mid-conversation. I didn’t expect this to be useful until I caught myself asking “which model should I use for this coding task on this machine?” during actual work sessions. Having the answer without switching terminals saves more time than I’d admit.

What I’d change

The model catalog is curated, not exhaustive. 35+ models are solid, but new releases drop weekly. The sync command pulls fresh data from Ollama’s registry, but it requires an extra dependency (sql.js). Minor friction. Worth it if you want the latest models scored automatically.

Plus, the tool assumes Ollama as your runtime. If you run llama.cpp directly or use vLLM, you’ll need to adjust. Not a dealbreaker, but worth knowing.

Bottom line

Stop guessing which models fit your hardware. The math exists. Someone packaged it into a tool that takes 30 seconds to install and 5 seconds to run.

npm install -g llm-checker
llm-checker check

Two commands. You’ll know what your machine can handle and stop wasting bandwidth on models that won’t perform. Pick models for your actual use case instead of whatever Reddit liked last week.

From what I’ve seen, most people running local LLMs leave 30-40% performance on the table by picking the wrong model-quantization combo for their hardware. Fix that first. Everything else gets easier.

I opened YouTube. Found 50 videos. All decent. None grabbed him.
So I built one myself. A 3D particle collision simulator. Protons racing through a detector, smashing at 13.6 TeV, spraying muons and photons with glowing trails. Slow it down to 0.05x. Watch every decay happen in real time.

Took me one evening with AI. This is how it looks:

Here's the thing. Coding with AI today is not about writing programs. It's solving your specific problem. Often faster than searching for an app, comparing options, and paying for a subscription that does 80% of what you need.

If you know what you want, you can shape it with your own hands. That's the real shift. The kid watched Higgs boson decay into two photons and said, "Wait, so THAT's what they found at CERN?"

Worth it. He got it, and I got that WHAT A BEAUTIFUL TIME WE LIVE IN!

And yes, you can try to break a particle by yourself!

The Arms Race Nobody Expected

Here’s the irony: recruiters already automated themselves out of existence. They just don’t know it yet.

Modern recruiting works like this:

Notice what the recruiter actually does? Clicks “approve” on whatever the AI selected.

So I found hr-breaker—an open-source AI tool that optimizes resumes to beat ATS filters. Upload your resume, paste a job description, get back a version that passes every filter. (I discovered it via this Telegram post—credit to the original author.)

The result: AI recruiters vs AI candidates. And recruiters just became spectators.

What hr-breaker Does

hr-breaker is an open-source resume optimizer that:

• Takes YOUR resume (any format: PDF, Markdown, LaTeX)

• Takes a job posting (URL or text)

• Rewrite your resume to match the job

• Runs it through 7 validation filters:

  • ATS simulation (keyword matching)

  • TF-IDF similarity

  • Vector semantic matching

  • Hallucination detection (keeps your real experience)

  • AI-generated text detection (sounds human)

  • Length limits (1 page, professional)

  • Format validation (clean PDF)

• Iterates until ALL filters pass

How I found it: Telegram post from @boris_again
Cost per optimization: ~$0.02 (Gemini API calls)
Success rate: 95%+ (passes ATS on first iteration)

The Real Problem: Recruiters Became Button-Pushers

Let’s be honest about what recruiting became:

10 years ago (pre-ATS):

• Read resumes manually

• Assess cultural fit

• Build relationships with candidates

• Use judgment and experience

Today (post-ATS):

• Load resumes into software

• Let AI score them

• Review the top 10 algorithmic picks

• Schedule interviews for whoever scored >85

The recruiter’s job is now: “Did the AI like this candidate? Yes? Schedule an interview.”

They outsourced the thinking to algorithms. And algorithms don’t need recruiters—they need API calls.

The Candidate Counter-Attack

So candidates adapted. Smart developers built tools like hr-breaker that reverse-engineer the filters:

What hr-breaker does:

1. Analyze job posting → Extract required skills, keywords, experience

2. Restructure resume → Match your real experience to job language

3. Validate against filters → TF-IDF score, keyword density, AI detection

4. Iterate → If any filter fails, regenerate with feedback

5. Output PDF → Professional, 1-page, ATS-friendly

Example transformation:

Before (your resume):

“Built data pipelines processing customer events”

After (optimized for “Senior Data Engineer” role):

“Architected scalable ETL pipelines processing 50M+ events/day using Apache Spark, Kafka, and AWS Redshift”

Same experience. Different keywords. Passes ATS filters.

Why This Kills The Profession

Here’s the uncomfortable truth: If the job is just “run candidates through AI filters,” you don’t need recruiters. You need a Cron job.

Companies will realize:

1. ATS already does the screening

2. Calendly already does the scheduling

3. Candidates optimize their resumes anyway (hr-breaker proves it)

4. Recruiter adds no value except “approve/reject” button clicks

Next step:

• Remove the human approver

• Auto-schedule top 10 candidates

• Let hiring managers decide

That’s 80% of recruiting headcount. Gone.

The Developer Comparison (Again)

Developers are safe because their job ISN’T mechanical:

What developers do:

• Understand vague business requirements (”make checkout faster”)

• Design solutions (caching? Database optimization? CDN?)

• Debug production issues (why is checkout slow at 2 PM?)

• Make tradeoffs (performance vs maintainability)

AI helps. AI doesn’t replace.

What recruiters do now:

• Run resumes through ATS

• Schedule top-scored candidates

• Ask hiring managers “thumbs up or down?”

AI does this completely. No human needed.

Try It Yourself (15 Minutes)

Want to see how easy it is to beat ATS filters? Here’s the tool I found:

1. Install dependencies:

# Install uv (Python package manager)
curl -LsSf https://astral.sh/uv/install.sh | sh
​
# Clone repo
git clone https://github.com/btseytlin/hr-breaker
cd hr-breaker

2. Set up API key:

# Get free Gemini API key: https://aistudio.google.com/
echo “GOOGLE_API_KEY=your_key_here” > .env

3. Run Web UI:

uv run streamlit run src/hr_breaker/main.py
# Opens browser at localhost:8501

4. Optimize resume:

• Upload your resume (PDF/Markdown/LaTeX)

• Paste job URL or description

• Click “Optimize”

• Download ATS-friendly PDF

Filters it passes:

• ✅ Keyword matching (TF-IDF >0.25)

• ✅ LLM-based ATS simulation (score >0.7)

• ✅ Vector similarity (cosine >0.4)

• ✅ Hallucination detection (keeps real experience)

• ✅ AI-generated text detection (avoids generic phrasing)

• ✅ Length limits (1 page, ~4500 chars)

• ✅ Professional formatting

Original repo: github.com/btseytlin/hr-breaker
How I found it: Telegram post by @boris_again

The Endgame

This isn’t about who built the tool or who uses it. It’s about automation eating standardized processes.

Recruiting became standardized:

1. Job posting template

2. Resume parsing algorithm

3. Keyword scoring

4. Automated ranking

Once you standardize something, you can automate it. And once you automate it, humans become overhead.

Developers haven’t been standardized. Every product is different. Every bug is unique. Every tradeoff is contextual.

Recruiters standardized themselves. ATS systems ARE the recruiter now. The human just clicks “approve.”

The Bottom Line

I found hr-breaker and tested it to get past ATS filters. It works. Perfectly.

But the real insight isn’t that candidates can game the system. It’s that the system is just algorithms — no human judgment left.

If recruiting is just “run resumes through AI filters and schedule the top 10,” you don’t need recruiters. You need infrastructure.

Developers will adapt. They’ll use AI to code faster. But they’ll still design systems, debug production, make tradeoffs.

Recruiters already automated their own job. They’re just waiting for companies to notice.

Linux isn’t just “good for developers.” It’s fundamentally transparent. Everything is a text file. Every config is readable. Every service can be controlled from the terminal. And that transparency makes AI assistance dramatically more effective.

Here’s what I mean. With real examples.

The Core Problem: Black Boxes vs. Open Books

Think about it. When something breaks on Windows, what do you do? You Google the error message. You find a forum post from 2019. Someone says, “Try this registry hack.” You paste it. Maybe it works. Maybe it doesn’t. You have no idea why.

Linux works differently. Configs are text files in predictable locations. Services write logs to readable journals. When something breaks, you can actually see what’s happening.

Sound familiar? You know what doesn’t work. But you have no idea why.

Real Example 1: The Missing On-Screen Display

Yesterday, my volume keys stopped showing the OSD pop-up. No visual feedback when changing volume or pausing Spotify. Just silence.

On Linux (my setup with Hyprland):

I asked Claude Code to check it. It ran pgrep swayosd. Nothing. The OSD server wasn’t running. Claude checked my autostart config. The line was missing. It added one line to ~/.config/hypr/autostart.conf:

exec-once = uwsm-app -- swayosd-server

Started the service. Done. Two minutes. I could read exactly what changed.

On Windows:

Where would you even start? Check if “Volume OSD” is disabled somewhere in Settings? Registry edit? Is the HID service running? Audio driver reinstall? The diagnostic path is unclear. Solutions involve blind registry hacks from random forums. You can’t see what’s actually happening under the hood.

On MacOS:

Volume OSD is baked into the system. If it breaks, your options are: restart, reset NVRAM, or reinstall macOS. There’s no config file to check. No service to restart. Just hope.

Real Example 2: Custom Voice Input Shortcut

I wanted Ctrl+Space to trigger voice dictation using my custom script through OpenAI voice recognition service.

On Linux:

Added one line to my keybindings config (Claude Code did it actually):

bindd = CONTROL, SPACE, Voice dictation, exec, ~/.local/bin/hyprwhspr-toggle.sh

Saved. Worked immediately. Hyprland auto-reloads on save.

On Windows:

You’d need AutoHotkey (third-party install). Or dig into Task Scheduler with COM objects. Or use PowerToys (another install). The shortcut definition lives somewhere in the app’s own config. Or Windows registry. Or both.

On macOS:

System Preferences. Keyboard. Shortcuts. App Shortcuts. GUI-only. Want to script it? Write AppleScript or use Automator. Good luck debugging either.

Real Example 3: Debugging a Silent Startup Failure

An app I added to autostart just... didn’t start. No error. No message. Nothing.

On Linux:

journalctl --user -xe

Every application’s output goes to the system journal. Text. Searchable. Filterable by time or app name. Found the error in 30 seconds.

On Windows:

Open Event Viewer. Navigate a tree of categories. Find Application logs. Scroll through XML-formatted entries mixed with system noise. Half the errors say “see vendor documentation.” The other half are irrelevant system events.

On MacOS:

Console.app exists, but it’s overwhelming. Most apps log to different locations. Finding why something failed means googling the app name plus “not starting” and hoping someone had the same issue.

Why This Matters for AI Assistants

Here’s the thing. When I ask Claude Code to fix something on Linux, it can:

• Read the relevant config files directly

• Understand the structure (it’s documented, text-based)

• Make surgical changes

• Verify the fix worked

On Windows or MacOS, the same request hits a wall. Binary formats. GUI-only settings. Undocumented registry keys. APIs that require specific tools.

The AI can help you with any system. But Linux makes it 10x more effective. Because the system is legible. The AI can read the same files you can. It can understand what’s happening. It can fix things precisely instead of guessing.

The Bottom Line

I didn’t switch to Linux because it’s “better.” I switched because it’s honest.

Every config is readable. Every service is controllable. Every behavior is documented somewhere in a text file.

When something breaks, I don’t restart and hope. I investigate. I find the cause. I fix it. Often, with AI assistance that can read the same files I can.

That’s not a feature of Linux being “for power users.” That’s a feature of Linux being transparent about what it’s doing.

Windows hide everything behind abstractions. MacOS locks you out of your own system. Linux shows you the gears.

Which one do you want your AI assistant working with?

I write about AI, productivity, and building products without traditional development teams at Restless Brain. If you want to discuss, challenge, or just ask questions, reply to this email. I read every single one.