28:29David Ondrej · Tutorial
Tailscale, Clearly Explained (Beginner's Guide)
A private, zero-open-port network for every laptop and VPS you own — set up end to end by an AI agent instead of by hand.
July 13thThe argument in one line.
As AI agents write nearly all the code, the markdown context around a project (specs, conventions, and a self-validating 'meta harness') becomes more valuable than the code itself, because it is what lets an agent start every session already knowing what to do.
Read if. Skip if.
READ IF YOU ARE…
- You run or lead an engineering team already using Claude Code, Codex, or Cursor and want a concrete pattern for keeping long agent sessions coherent across days.
- You're building or scaling a content operation and want a repeatable, structured process for turning raw thoughts into finished posts without it reading like AI slop.
- You're evaluating whether to bring AI consulting into your company and want to see what 'multiplayer AI' looks like in a real engagement.
SKIP IF…
- You're looking for a beginner tutorial on prompting; this is a systems conversation between practitioners, not a how-to.
- You have no interest in enterprise consulting or agency operations; a large stretch of the middle third is about 10x's own business model.
The full version, fast.
10x is an AI consultancy that stays embedded with Fortune 500 clients rather than delivering a slide deck, using a framework of 'single-player' AI (a chatbot for one person) versus 'multiplayer' AI (redesigning a whole process for compounding leverage). Internally, the firm treats markdown as code: every engagement gets a project-management repo of epics, specs, and convention docs, validated by a custom CLI, and every new agent session opens with a 'benevolent prompt injection' of full project context. The same discipline powers their internal content machine, which turns a 20-minute AI-led interview into a scored, on-brand post instead of generic AI slop.
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Create a free account →Who's talking.
00:00hostDavid Ondrej
00:14guestAlex Lieberman
00:14guestDan Zakon
Where the time goes.
00:00 – 00:24
01 · Cold open
A compilation of the episode's sharpest lines: AI as an amplifier of agency, and the claim that engineering markdown will soon matter more than writing code.
00:24 – 00:58
02 · Meet Alex and Dan
Host David Ondrej introduces Alex Lieberman (Morning Brew cofounder, now at 10x) and Dan Zakon (10x's director of engineering), then opens with a direct question: how do you make an old, slow company AI native?
00:58 – 04:40
03 · Building a modern Bell Labs
Alex frames 10x as a full-stack AI consultancy that stays embedded with clients instead of handing over a slide deck, tracing the firm's founding thesis to the book The Innovators and the Bell Labs model of giving smart people autonomy plus direction, distribution, and resources.
04:40 – 05:53
04 · Sponsor: TopView Canvas
Ad read for TopView's AI video canvas and its limited Seedance 2.5 deal.
05:53 – 08:09
05 · The forward-deployed engineer
Alex and Dan describe the FDE role, embedding inside a client to solve custom problems fast, then reframe consulting as 'innovation as a service' that offers engineers diversity of work instead of years on one narrow problem.
08:09 – 12:01
06 · Single-player vs multiplayer AI
10x's core framework for clients: single-player AI is just a chatbot in front of one person; multiplayer AI redesigns a whole process so leverage compounds across a team, which is where 10x pushes every engagement.
12:01 – 20:07
07 · Context as code: the meta harness
Alex and Dan describe treating markdown documentation with the same rigor as code: separate repos per function, an SDLC redesigned around agents that can hold far more context than humans, and the moment they coin the term 'meta harness.' The talk pivots to why moats are shifting toward people and trusted distribution as code gets cheap to produce.
20:07 – 22:20
08 · Sponsor: DeepAPI
Ad read and live demo of DeepAPI, a scraping and research API pitched as a single key that gives any agent web search, deep research, and scraping.
22:20 – 33:26
09 · Inside the Content Machine
Alex walks through 10x's content pipeline end to end: a process layer in Git and a personal voice-file layer, the Oracle that scans Slack/Notion/Gmail/Linear/Git for ideas, a six-persona interview panel that interviews the writer, a matching writer's council that scores drafts, and a repurposing engine that spins one piece into ten.
33:26 – 35:35
10 · Training your voice, lowering the friction
Three ways to train the content machine on a new person's voice, why incentive plus lower friction is what actually gets non-writers posting, meeting transcripts turned automatically into tweet ideas, and a brief detour into why AI still can't edit video well.
35:35 – 50:20
11 · Live demo: the engineering setup
Dan shares his screen and runs 10x's internal CLI live: epics, specs, and convention docs, a shared skills library, the start hook that injects full project context into every new agent session, and '10x validate,' a linter for the entire SDLC. The pair debate whether the code repo or the context repo is actually more valuable, and land on agents increasingly holding humans accountable to the roadmap.
50:20 – 51:54
12 · Close
10x's hiring pitch, where to follow Alex and Dan, and a final live demo of DeepAPI scraping contact info for 50 AI-startup CTOs.
Lines worth screenshotting.
- AI amplifies whatever a person already is: it makes high-agency people look far more capable and low-agency people look far worse.
- The percentage of time spent writing markdown context for a coding agent should exceed the time spent writing or reviewing the code itself.
- 10x splits every engagement into two repos: a disposable code repo and a project-management repo holding epics, specs, and convention docs.
- A CLI called '10x validate' lints the entire software development process, not just the code, flagging things like a spec marked complete while its tickets are still in review.
- Every agent session opens with a 'benevolent prompt injection': a start hook that automatically feeds the agent a full packet of workspace context before it does anything else.
- If someone could steal only one repo, the code repo is the disposable one; the process, conventions, and business context are what's actually scarce.
- Single-player AI means putting a chatbot in front of one employee with zero workflow change; multiplayer AI means redesigning a whole business process so leverage compounds across the company.
- Most 'AI problems' enterprises bring to consultants turn out to be data problems: the company isn't data-ready enough to build agents on top of what it already has.
- 10x's content machine interviews the writer for about 20 minutes using AI personas modeled on Tim Ferriss, Joe Rogan, Larry King, Howard Stern, Michael Barbaro, and Barbara Walters, then never rewrites their actual words, only their flow.
- A six-persona 'writer's council' scores every draft out of 10; anything below a 9 gets sent back through another revision loop before it can ship.
- As code gets cheaper to produce, the remaining moats are people, because AI is 'a funhouse mirror' that only amplifies who someone already is, and trusted distribution.
- Ordinary meeting recordings get piped through a transcription pipeline so an internal conversation can become tweet-ready content without anyone deciding in advance to write about it.
- The fastest way to train a new person's voice into a content system isn't to interview them first, it's to let them start writing in someone else's already-trained voice and let it drift toward their own.
- A well-built project-management repo could arguably be reverse-engineered into a better product than the code repo alone, because code strips out the intent behind why a feature exists.
Context as code: markdown that outranks the code
CONTEXT AS CODE
As agents write nearly all the code, the highest-value engineering work shifts to writing the specs, conventions, and self-validating systems that keep an agent oriented across long sessions.
03Building a modern Bell Labs
- 10x treats its founding as building 'a modern Bell Labs': brilliant people given autonomy, but with direction, distribution, and resources supplied by the firm.
- Instead of a slide deck and a goodbye, the firm insists on staying embedded through implementation, because the value it argues it captures comes from ROI, not the strategy document.
05The forward-deployed engineer
- A forward-deployed engineer's job is to compress 'time to magic': show a client's leadership a fast, custom win so the rest of the org wants AI too.
- Reframing consulting as 'innovation as a service' is also a retention play for engineers, who get variety across clients instead of years on one narrow problem.
06Single-player vs multiplayer AI
- Single-player AI (a chatbot in front of one employee, zero workflow change) is usually the correct first step for most companies, even though it doesn't compound.
- Multiplayer AI means redesigning a horizontal process, like a sales function or a data pipeline, so leverage is created for an entire team, not one person.
- Most 'AI problems' clients bring in turn out to be data problems: they aren't data-ready enough to actually build agents on top of what they have.
07Context as code: the meta harness
- Splitting a code repo from a project-management repo of epics, specs, and conventions lets an agent parse project structure the way it would parse code.
- Because agents can hold far more context at once than a human can, the process built around them can plan further in advance than traditional agile ceremonies ever could.
- As code gets cheap for any engineer, or agent, to produce, the moats shift to what's actually scarce: people with agency, and audiences who trust them.
09Inside the Content Machine
- The system deliberately keeps humans at the 'first mile and final mile': picking the idea and supplying the original thoughts, while AI handles research, structure, and editing in between.
- A six-persona interview panel, each with a different questioning style, spends about 20 minutes pushing a writer for specifics before their raw thoughts become source material.
- A matching six-persona writer's council scores every draft out of 10; anything below a 9 gets sent back through another revision loop instead of shipping.
- One finished piece gets run through a repurposing engine that reuses the same skill-and-council process to spin it into roughly ten derivative posts.
10Training your voice, lowering the friction
- New writers can train their voice three ways: a dedicated voice interview, feeding in old texts and messages, or simply starting in someone else's already-trained voice and letting it drift toward their own.
- Getting non-writers to actually post requires both lower friction, letting the system do the work, and real incentive, like a $5,000 content contest.
- Meeting transcripts get piped through automatically so an ordinary internal conversation can become tweet-ready content without anyone deciding in advance to write about it.
11Live demo: the engineering setup
- Every agent session opens with a 'benevolent prompt injection': a start hook that force-feeds the agent a full packet of workspace context before it does anything else.
- '10x validate' lints the entire software development process, not just code, catching things like a spec marked complete while its linked tickets are still in review.
- A project-management repo built well enough could arguably be reverse-engineered into a better product than the code repo alone, because code strips out the intent behind why a feature exists.
- Every new agent session is designed to start as if it were already a senior engineer on the project: it knows the roadmap and what's next before the human says a word.
Terms worth knowing.
- Context as code
- Treating the markdown specs, conventions, and process documents an agent reads with the same rigor and structure normally reserved for source code.
- Meta harness
- The full scaffolding of markdown artifacts (epics, specs, conventions, logs) that surrounds a codebase and tells both humans and agents how the project actually works.
- Benevolent prompt injection
- A start hook that automatically feeds a coding agent a packet of project context the moment a new session begins, so it starts already briefed instead of from zero.
- Single-player AI
- Using an AI model as a personal productivity tool for one person, with no change to how a team or business process works.
- Multiplayer AI
- Redesigning a horizontal business process so an entire team or company gets compounding leverage from AI, instead of just one person.
- Forward-deployed engineer (FDE)
- An engineer embedded directly inside a client's business to build and ship custom AI solutions on-site, rather than handing over a slide deck and leaving.
- The Oracle
- 10x's content-machine feature that scans a person's recent Slack, Notion, Gmail, Linear, and Git activity, plus external sources, to surface ideas worth writing about.
- Checkpoint drift
- When a project spec's authored status (e.g. 'complete') disagrees with the status a validator derives from the actual state of its linked tickets.
Things they pointed at.
03:16bookThe Innovators (book on the history of computing and innovation)
20:07toolno-ai-slop skill (Peter Yang)
32:00toolComposio CLI (meeting transcription pipeline)
Lines you could clip.
00:04
“AI makes high agency intelligent people look really good. It makes dumb low agency people look worse.”
00:13
“The percentage of time that you spend on engineering the markdown should be higher than the percentage of time that you spend on actually executing the code.”
15:21
“It's not a code base anymore. It's kinda like a context base.”
40:19
“We're basically we'll call it, like, benevolent prompt injection.”
41:04
“I can spend, like, four hours just building really good plans and architecture documents, and then I can just kinda put my computer in the corner and go to bed and wake up with exactly what I built.”
46:02
“It's kind of like linting for your SDLC or your entire engineering process, and not just linting for the code.”
46:38
“I think there's tremendous alpha in actually having agents hold humans accountable.”
48:50
“We think of every session as the agent should start as, like, a senior engineer on the project. It should immediately know what's going on.”
Where the conversation goes.
00:00 – 00:58sparseIntro and cold open
00:58 – 04:40steady10x's consulting thesis and Bell Labs vision
04:40 – 05:53sparseSponsor: TopView
05:53 – 08:09steadyForward-deployed engineer role
08:09 – 12:01denseSingle-player vs multiplayer AI framework
12:01 – 20:07denseContext as code and the meta harness
20:07 – 22:20sparseSponsor: DeepAPI
22:20 – 33:26denseContent Machine deep dive
33:26 – 35:35steadyVoice training and content friction
35:35 – 50:20denseLive engineering setup demo
50:20 – 51:54sparseClose: hiring and DeepAPI demo
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metaphoranalogy
Everything you ever learned about building software is kind of thrown on its head. AI makes high agency intelligent people look really good. It makes dumb low agency people look worse.
The percentage of time that you spend on engineering the markdown
should be higher than the percentage of time that you spend on actually executing the code. At a point very soon, no one, including all engineers, will read code or write code. This is Alex Lieberman,
the cofounder of Morning Brew and Tenex, and he's joined by Dan, the director of engineering at 10x. In this podcast, we talk about how any company can become AI native, why markdown files are worth more than your code, and what is the last remaining mode that AI won't kill. If you want to see how people on the cutting edge of AI actually work, watch until the end.
This is the David Andre podcast. Enjoy. Alex and Dan, I have a question.
How do you take a company that's old and slow moving and make it AI native? I think the answer to the question is kind of like, how do you take anyone
who has a set of existing context and get them to do something different? And the answer is meet a company where they're at, where their systems are at, where their people are at, where they are in their AI transformation journey. Because if you were to reimagine what a consulting business for Fortune 2,000 companies looks like in 2026, it has to be a full stack partner.
Like, historically, some of these biggest consultancies in the world only did one thing. They only would basically be told a problem. You would have a group of consultants and their manager work on a solution to that problem as a slide deck for a few months.
They'd meet up with the client. They would deliver the solution via this 150 page deck. They'd give them the deck, and they'd say, okay.
Peace out. See you guys later. And then the client would would rely on having to solve the the problem themselves.
Our view is in a post AI world, you need to touch every part of the value chain in a company's transformation, developing their strategy, doing nonengineering work to drive change management and everything that needs to happen around the tech to actually make the tech valuable, and then actually forward deploying into a company.
To to your question, there are a lot of companies, a lot of Fortune 500 businesses where the first thing we do is literally taking their c suite or their executive leadership team and teaching them how to use Claude Cowork or Claude Code. And then for other companies who come to us and say, hey.
We want to build back office agents for invoicing AR, AP, HR, etcetera. We go and we meet them where they're at with that problem. So, really, it's like, how do you decrease the friction to as close to zero as humanly possible?
I see. So I guess that's one of the, like, biggest separators
of, like, an average consultant where he, like, gives you something and then you have to figure it out and a great consultant that, like, you know, helps you implement it. What are some other things? Because a lot of people want to be an AI consultant.
You know, people tried starting AI consultancy in 2425. Yeah. What are some other separators?
I would say Arman,
my cofounder and co managing partner, myself, we did not dream of being consultants. Like, neither of us are consultants. Our actual vision for 10x is we wanna build a modern day Bell Labs and for different reasons.
I have realized about myself, if I'm not home with my wife, my daughter doing something else, it has to be worth it. And to me, worth it means solving really hard problems that are on the frontier of technology with the smartest, most driven people I've ever worked with.
For Arman, I think it was in high school or college, he read a book called The Innovators. The Innovators, it's an exceptional book. Talks about the history of computing, literally from Charles Babbage and Eva Ludlace all the way to effectively AI.
And in this book, it talks about the arc of innovation and what were the raw materials that made kind of these centers for innovation successful, whether it was Bell Labs with the transistor, whether it was DARPA with the Internet, whether it was Xerox PARC.
And the short answer is the entire time it is effectively brilliant people who are given a great deal of autonomy, but they are given direction, distribution, and resources.
And so what we basically said is, if we want to have all five of those ingredients and we are we do not desire to raise hundreds of millions of dollars from the start, how do we do that? And elegantly, the answer starts looking like helping the biggest companies in the world solve their hardest problems with AI.
By solving these problems, enterprises are telling us their biggest challenges that they're that they're navigating within their business. They are paying us to do so. We're so we're cash flowing the business.
So our view is in helping enterprises end up on the right side of history, we are getting all the raw materials we need to effectively build an applied AI lab where we incubate some of the most consequential technologies in a post AI world. I see. CDance 2.5 just came out, but most people are gonna be paying for every generation.
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And remember, that sixty day unlimited CDENS 2.5 deal is for ultra annual users. So So click the first link in the description and give TopView a shot. One of the most interesting roles you could possibly have as an engineer is this kind of, like, applied AI forward deployed
engineer because you you can accelerate the speed to solving very hard custom fit problems for enterprises. And enterprises previously could only have these one size fits all solutions.
Now you can go in and and kind of find the the gnarliest problems possible in enterprises and solve many of them in kind of this very rapid succession. And so if you can reduce that time to magic very early on in in one of these engagements and just show the c suite or whoever the stakeholder is how much leverage their teams and their orgs can get with AI, it it becomes this cascade of everyone else in the organization kind of wants in, and and we get to
build more and more for them. So it's basically like innovation as a service. Yeah.
That's exactly right. I think consultancy is, like, underselling it. You know?
I think that's totally true. And I think that works to our advantage because, honestly, I think, unfortunately, the best technical talent has never made its way to consulting.
Because, like, one way to sell consulting is you go into a company. They tell you a problem. Usually, the answer is firing people.
You deliver them a deck. You walk away. You never see the fruit of your work, and then you move on to the next client.
Or you could position consulting as a huge company that has messy, complex, and really important challenges to solve.
They don't have the answer, and they need an a team of people to basically provide them innovation as a service. And not just innovation as a service, but innovation and implementation, like, actually driving ROI and growth through the insights they provide.
The cool thing about it is you don't have to just do this with one company forever, which is oftentimes a thing that I think, uh, creates boredom or plateauing for engineers in any org. It's like you work on one very specific challenge for a very long time. Consulting by design
offers you diversity of work. Yeah. So, you know, that ADHD is satisfied, and you still work for the same company.
You don't have to career hop, but you can have different challenges. Yeah. Exactly.
And there's a few common questions
that when we talk to a client, they ask us, and we need to provide thoughts on. One question is is, like, how should we even be thinking about where there's opportunity for AI in our business?
Like, again, we meet people at different parts of the journey. And I would say half the time, it's a c level person reaching out to us saying, guys, I'm in this group chat with a bunch of c suite friends, and they're all talking about all this cool AI shit they're doing.
And I have FOMO. And I don't know what we should do with AI, but we need AI. Can you help me with AI?
And one of the initial frameworks we provide is this idea of single player versus multiplayer AI. And so the idea is that single player AI is if I was like, hey, David. You traditionally would use Google to put in a query and get an answer.
I'm gonna put GPT or Claude in front of you, ask that same query, and see what you think about the answer. Zero behavioral change.
You just do that. And that is valuable. And and in the context of an enterprise, what that looks like is an enterprise and this is usually the first step that enterprises take in their AI transformation journey is they sign a large token agreement with one of the labs.
So either with OpenAI or with Anthropic, they're like, hey. We'll commit to $510,000,000 worth of token spend, and we just need to make sure that we have, you know, secure instances of using Codex and GPT or Claude Coding Cowork at our company.
And the first step of single player is literally just putting the power of a state of the art model in front of your people and then ideally teaching them how to actually use these things in their day to day workflows. That is really valuable.
And I would actually argue for most companies, that is where most companies should start. But that is single player. Like, people are only getting leverage from use of the technology for their own work, but there is no compounding effect.
Multiplayer AI is this idea of where you look at horizontal technologies or processes in your business whereby reinventing them or reimagining them, you're not just creating leverage for one person. You're either creating leverage for the entire company or an entire function. So that could be everything from building, you know, a an SDR or sales agent for a sales org that helps every additional every seller get back time that they were spending before on, like, the logistics and the minutiae of their job versus actually talking to clients.
Or we say this all the time. Most clients come to us with an AI problem, and it turns into a data problem where we're build we're doing some form of data engineering for clients because they are nowhere close to being data ready to actually build any form of agents or solutions on top of. Again, another example of multiplayer AI where you get that right, the leverage created for the entire organization is massive.
And so our framing is we do both single player and multiplayer AI at 10 x, but where you're really gonna get exponential benefit and where it's gonna be really hard and painful and messy for you and where we can be most supportive is on the multiplayer side. Yeah. I wanna share a tip we do at our much smaller company is that, like, I I have everybody work at different GitHub repos.
Right? So we have one for, like, hiring, one for brand deals, one for YouTube videos. And then anything you do instead of just chatting in the Cloth web app or ChatGPT web app, I force everybody to chat in Codex and Cloth and, you know, save their work in these markdown files, in these repos so that anybody else, they can load up their own Cloth core with Fable.
And, you know, even if that's not their area of business that they work in, they already have all those project specific skills, all those markdown files, all that context. And, you know, it's it's a complete game changer because even people who, you know, half a year ago were not technical and not really using AI as much as they could, now they do.
And, you know, sharing those skills is really is really magical because somebody who doesn't work in that, they they feel like they can do it because, know, they have their own fable. Fable reads those files, gives them the brief, and now it's, like, a form of multiplayer. Totally.
And one one thing I'd just add, and we can up to you, whether we talk about this now or later on, but I think, like, this is exactly how Dan and the engineering team at 10x think about, like, our entire SDLC
and this this idea of context as code. Like, it's interesting because engineers have long thought of being really organized and methodical in the way that they structure files and information when building software.
In traditional knowledge work, that is not the case. So I think actually in a lot of ways, nontechnical knowledge workers are playing catch up to get to a place how engineers have always thought about kind of like setting
up the scaffolding of how they work. And I think with engineering at 10x, we're just taking it to another level of of how kind of careful and prescriptive we are with the context we provide to coding agents that we work with. Yeah.
We can touch on it bit more. Go ahead, Dan. Yeah.
I mean, I can I can go into the demo a bit later? But I would I would think of it as this new kind of problem where previously you were solving this coordination problem between just humans and humans. And so the way that, like, agile development worked was just a ton of ceremonies.
You would meet every morning. You would talk about it, talk about the work, and then you would have retros at the end of every week, and you just fill up the calendar with as many meetings to to convey information as possible. But now it's really a coordination problem between humans and other humans and agents as the third party.
But agents are kind of, like, in this main character spot where they're the ones actually doing the the work on the ground. And so I think that it is, like, so much more important now than ever to be a written first culture and kind of what you're saying, David, along the lines of storing up all this markdown so that both humans can get on the same page about what they're working on across different projects, but also so that agents can be very quickly briefed on what's going on.
And then like Alex said, at ten x, we're kind of taking this really far to the extreme where if you think through from first principles, like, would you redesign the SDLC for agents? Agents can maintain coherence across so much more information at the same time than humans ever could.
And so you can structure, like, bigger and more complex blueprints that lay out more of the architecture, more of the plans so much further in advance than you ever could before and and than you ever could in these, like, agile human to human ceremonies. And so we have these very concrete structures and types of
markdown artifacts that we write for all of our projects and and structure it for everything that we're working on so that agents can easily validate. I mean, kind of like Alex is saying, we treat this context as code, and I'll show this in a bit. But agents can immediately pull all the right context at the right moment and kind of keep all this in sync as a new form of, like, software machine, so to speak?
Yeah. I think with each project I start, like, I find myself keeping more and more stuff in the code base. Like, literally, have, like, slash docs slash marketing.
I have a, you know, user feedback file where any user feedback, I just put there, and it it's Margaret files. I have a slash doc slash external. All of my, you know, deployments and stuff that's external to the code base is stored in there because, again, who knows if the agent needs it?
You know? Maybe there's, like, some deployment issue with the front end hosting.
If he knows what what it's configured like, what what variables are there, maybe he finds a missing ENVQ or something like that. So, yeah, I think the value is there, and, really, we need a new word for it because it's not a code base anymore. You know?
It's like in the past, used to be just code, but now it's it's kinda like a . Yeah. We we can we we should we should call it something like a .
Meta harness. Okay. Yeah.
Yeah. That that's what we called internally at 10 x, and I think the like, it's what are we trying to achieve? It's like coding agents offer speed
that was never possible. But the longer running the task, the more that you risk entropy and diversion from plan. And so the question is is how do you minimize entropy and diversion while optimizing for the speed that is kind of the power of this technology?
I also wanna talk about the content machine you guys have because this is one of the most unique systems. I think we will see more and more companies doing it where not just the founder and, you know, the marketing guy, but, like, more people are creating content. So tell us more about that.
I mean, and this will definitely resonate with you given that you kind of have a a foot in both worlds, both, like, in building software, but also, obviously, you you've seen the power of content yourself. Like, one of our let me start with, like, one of our highest level theses at 10x is that in a post AI world, the number of moats remaining in business is shrinking.
Like, I think what what are moats today? It's like network effects to some extent, data to some extent.
I would argue people think this is kind of bullshit. I think people is more of a moat than ever before because AI is just like AI is a . It makes high agency intelligent people look really good.
It makes dumb low agency people look worse. And so I think people is emote than ever before. And then I would argue trusted distribution is, uh, more emote than ever before.
Right? Like, you you build a piece of software, and likely, there are literally millions of other engineers that can build that piece of software pretty quickly now.
What is going to earn you dollars from from an audience? It's the fact that you have people who trust you, and that trust gets turned into dollars in the bank.
And so we've always had this view that we're building a media company on top of 10x. And this media company is gonna be a combination of, like, brand driven content, like newsletters and deep, uh, editorial that is both technical and nontechnical, but then also talent driven content where we're literally hiring.
David before David's became big, that's like we're bringing on, like, dozens of those to our business. But we also wanna turn our employees at 10x into creators. And so the thesis the the high the question I asked myself was, like, how can I turn engineers or strategists at 10x into content creators?
And starting with text based content because I think it's just way easier to leverage AI for that versus video and multimedia right now. And same thing with myself. Like, I don't have all day to create content, but I started as, like, the first best marketing channel for 10 x.
And so I needed to make sure that I kept my content output going. And so the idea of the content machine was how do I build a system that allows for content abundance, that enables full time people to still create if they have thirty minutes per day, but is not an AI slop canon.
That was the prompt. What I can do is why don't I share my screen and kind of take you through what this system is?
And then feel free to just ask questions throughout. So, basically, I I wrote this post on it. I think it's just a good overview, and then we can I can actually run the machine if we want while we're talking through things, and I can even have it run when Dan goes through his stuff?
But, basically, the way the content machine is set set up is there's a process layer, and then there's a personal layer. The process layer is everything that lives in Git for my team, and that is basically the the pipeline, like, all of the steps that the content machine goes through to take almost like the assemble the the the factory line from knee I need an idea of what to create content about to finished idea that it has been distributed on x LinkedIn or another platform.
And then there's the personal layer, which is the files that live on your computer, which is basically the codifying of your voice, content lessons that you've provided to the content machine in the past, and any other important information about you that informs the way the content machine outputs in a way that is representative of you.
But the one big thing I'll share about how you can create an AI system that puts out content that isn't slop at a time when I think the models are still, like, pretty bad at writing.
Like, like, you're the like, they're they're good, but they always stink of AI. Yeah. They're they're the same patterns.
Exactly. And and I think, like, you know, I've seen people create these skills. Like, this guy, Peter Yang, created, like, a no AI slop skill that, like, takes all of the tells.
But I think with every change in models, there's gonna be new tells, and it's gonna be kind of this constant game of cat and mouse chasing the new tells. But my view is is the way you can guarantee no matter how good the models are, that you can guarantee it not being slopped is that the AI kind of helps with everything other than what the human is needed for, obvious.
And what I would argue the human is most needed for, not just in a content process, honestly, in, like, most processes, even with building software,
is the first mile and the final mile. Yes. I call it the origin.
I actually I actually made a short about this. I said, like, the the number one difference between, like, AI content that's trash and AI content that is valuable is if the origin is human. Exactly.
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or click the second link below the video. And so what I would do, uh, if there are steps in a con right. If you think about what a content process is and this is how I built this whole machine is I literally drew out on a piece of paper what is every single single step in the content process.
And it's like, step one, have to think of an idea. Step two, I thought of an idea. Step three, I research the idea.
Step four, I get all of my thoughts out. Step five, I turn it into a cohesive draft. Step six, I edit that cohesive draft.
Step seven, I'm pissed. I don't think it's as good of a draft as it should be. I rewrite it.
Step step seven, I finish the draft. Step eight, I post it. And so, like, I wrote this out, and I was like, let's rebuild this to be AI first and keep the human where the human needs to be.
And my view is the human always needs to select the idea that they wanna create content about. They need to provide their own words about this content idea.
So if I was to say, you know, hey, David. What are your thoughts on Kimi k three? To actually have you create a good piece of content around it, I would wanna interview you for thirty minutes after you've done research into the model, what makes it kind of, like, the elegant solutions to problems, uh, around memory that it solved.
You would give me your thought for thirty minutes. Those thoughts become effectively the the transcript or the markdown file that the entire content machine operates on. And, basically, the goal of the content machine is to give you ideas to to pick from and then take the words that you've given and not change your words, but just make sure your words flow, that they transition well, and that you have an editor who's checking what you've said to add in places where you need to provide more context because you didn't share it with your initial thoughts or where you need to make other tweaks.
And so this is basically the process. It is in the content machine, you do creator select first because all of our, uh, employees at 10x have used the machine.
You first select, like, who you are, which then loads in the files that are specific to you around your voice and your content lessons. Then we have this thing called the Oracle, which runs, and the Oracle scans your last seven days on Slack, Notion, Gmail, Linear, Git, and it hunts for spikes.
And spikes are basically how much point of view is there, how much story potential is there, how much emotional intensity is there, how much lessener framework is there, and how much depth is there. In addition to pulling ideas from internal sources in your company, it also pulls from external sources.
And I actually have I use slash last thirty days, which is Matt Van Horn's skill, uh, for doing this. So, basically, there's a social sweep across Reddit, X, YouTube, Hacker News, etcetera, on the topics that you think you wanna create an idea around.
This basically produces a list of, like, 15 ideas. All of those ideas get added to a Notion database that is created whenever someone spins up the content machine for the first time. It's called the vault.
And so, you know, let's just say I go through the content machine, and I pick one idea. The other 14 ideas may not be bad. I just don't wanna use them now.
They get added to this database. Then let's just say I pick the idea around Kimi k three. Let's say I like, you know, I'm not deeply technical, but I wanna have a perspective on it.
I run I could do an intermediate research step where it researches the the, um, original white paper. It also tells me what are current developments, what's already being said in market, what are contrarian angles, and what are open questions only I, as Alex Lieberman, can answer.
Once I have my thoughts, I have an interview panel interview me. And so this is probably the most important step in the whole content machine because this is the idea of instead of Alex interviewing David and like, a human needs to interview a human. I have basically these six personas, Tim Ferriss, Joe Rogan, Larry King, Howard Stern, Michael Barbaro, and Barbara Walters.
Each has a different skill. So, like, their way of questioning is codified in six separate skills.
They ask me questions one at a time. They force me to be specific. Anytime I have a vague answer, they push back.
We probably go for twenty minutes, and the way they ask me is over text in Claude code. I yap to text my answer. That gets turned into the full markdown file with the transcript, the key stories, the core insights I share, the quotable moments.
Then I tell it what format I want it written in, whether it's LinkedIn post, x thread, long post, playbook, podcast promo, etcetera. It finds the skill, which is my codified version of how have I written let's say it's a LinkedIn post.
How have I written LinkedIn posts in the past? And what have been my best performing LinkedIn posts? Yes.
And what was the format Right? Of takes the raw markdown file.
It refines it for flow, but not changing my words into that format. Then I have a writer's council or an editor's council.
It's the same idea as the interview panel, but for editing. So Morgan Housel, Tim Urban, Sean Purry, Greg Eisenberg, David Perrell, and a slop detector, they go through and they score it. If it is under a nine out of 10, it goes through a revision loop.
If it's above a nine out of 10, it's done. And then I get the piece, and then I can run it through a repurposing engine where let's just say I finished this long form piece on my, like, my takes on Kimmy k three, why it's important, what people should know. I can then have the repurposing engine turn it into 10 pieces of derivative content, but it goes through the same process where it pulls the skill for what is my my way of creating an X article.
What is my way of creating a LinkedIn article? It creates it, and it runs it through the writing council again. So all of these pieces go through the same flow.
And then there's a distribution piece where, actually, I get a UTM tag for each. I can auto publish it from the machine, and we can, uh, monitor performance. And the whole idea is by monitoring performance, then the machine gets better in the future of writing posts because it knows what top performers are.
And then the final piece is at the end of every session, I provide feedback on what I think the content machine did well with creating a piece of content, and it turns that into a lessons file. So every time it rewrite it it writes a piece of content in the future, it checks contentlessons.md
to make sure it's not making the same mistake again. And how many examples do you usually need in these, like, human written articles, LinkedIn posts until you feel it's good?
Yeah. It's a good question. So
I don't know the exact answer because I'm I'm a unique case where I have a lot of prior content. Like, you know, to train this, I had 350 podcast episodes.
I have, like, 10,000 posts across social. You definitely don't need that many. I would say let's say Dan wants to set up the content machine.
And let's say Dan has never posted on X or LinkedIn before. He basically has three ways for how he can train his voice for the content machine. Way one is there's actually like, the content machine will run a voice interview where it will interview him to get his voice out and turn that interview into his voice file.
That's number one. Number two is he can take basically feed anything that shows his voice, whether it's past text messages, emails he sent, or Slack messages, that turns into his voice file. Or the third is, and I've recommended this to people, is just start with me as the persona.
Literally have it use my voice files. And then over time, as you give it feedback, it'll just naturally morph into how your you want your voice to be different from my voice, but at least you have a voice you feel comfortable with. Dan, what's your been your experience with this content machine system?
I mean, it's great. I think that
it is nice to start with Alex's voice because he's trained his content machine or his voice on so much history of what works well and what doesn't on on social. And then kind of around the company, everyone's just whisper flowing there, takes about whatever the latest model is or or trend in agent engineering and just just translating it and getting it out.
And I think since Alex kinda rolled out the content machine internally, we've seen this huge boost in the amount of people at 10 x actually posting content, and I expect that to just increase
over time. And, David, two other thoughts here is, like, my view is it is generally really hard to get people whose full time job is not creating content to create content. And I think the way you need to do it is you have to decrease friction, and you need to provide incentive.
So Content Machine was decreasing friction, and I think we can decrease friction even more. So what that could even look like is you know how I say we're hire yeah. Exactly.
It's like we're we're hiring full time creators in the future. I could imagine our full time creators, they partially act as producers for our team where they go, they record conversations, and just feed it into the content machine. Or imagine Dan's on an inter like, we're in a few hours, we're talking about a piece of internal IP that we're building at 10 x right now.
If that whole meeting gets recorded and then we just run the content machine on a hook where anytime there's a new meeting recording in Notion transcripts, it just auto creates the piece of content. And then the second is incentive to basically, in my mind, go from cold start of no one creating content to getting people to build a habit.
We ran a competition where we were we gave away $5,000 over the course of a month to people to create content.
And we ran different games where it wasn't just about whose content got the most impressions. It was also, like, whose content did the best job of educating an audience on a topic or whose content did the best job of taking actual work we're doing with a client and storytelling it to the world.
So I think you have to do both incentives and lowering friction to make this possible. Yeah. I think, uh, one
actionable thing we do is, like, when we have a Google Meet, we have a notetaker there. Everything is automatically pulled through Composeo CLI. And, literally, transcripts like, if you're already discussing something, whether it is, you know, the more senior people at the team or somebody coaching a new person, that is, like, content opportunities.
Right? And then you can have, like, a frontier model like Fable, analyze, okay, these things, private, super private, never share. But, like, these 10 things, you could create tweets about them.
And that's another way we're reducing friction where, like, you would have that call anyway, but you don't even realize usually, like, the the internal conversations are the best content. You know? Like you said, if a model like QMK three drops, what do you think about it?
Are we really gonna use it? Would our competitors are using it? Right?
Like, these kinda you know, it's almost like this locker room talk, but, like, for a company Exactly.
Where people are sharing their hottest takes, but then again, to open the phone, you know, write the tweet. It doesn't sound as good. Totally.
And and I think look. We're doing with this this with writing first.
We also think there's a lot of opportunity to do this in video. Like, one of our engineers at 10x, CJ, had like, he's a longtime fan of Theo's content, and he's been like, I would love to do just, like, my livestream around, like, the five biggest topics in AI and engineering right now with my perspective. And I think the hard time right now the hard thing right now is AI is not really good at video editing.
And so you either have to have the internal resources or you have to pick a format where you really don't have to edit. But I think that'll be kinda like the next step of
how do we build workflows that help on all mediums of content, not just text. You mentioned briefly Matt Van Horn, and you had a nice interview with him. And you said something, which is a great hook, by the way, that one of the most productive engineers you know cannot read code.
So I've noticed the same pattern actually where, you know, some people who you would think they would be insanely proactive with AI because they're, like, fifteen years of experience in great software companies were actually very slow to adopt it. So tell us more about this pattern of people who cannot read code are usually more effective with agents.
Like, first of all, do you think people should read code anymore? Like, I feel like this was a very hot take by Matt. He's not an engineer,
but he was he was saying this as a generalized statement. And I was like, I wanna have a follow-up episode where I host a debate because I know there are gonna be people who take the other side of the trade here. But he was like, I think in at a point very soon,
no one, including all engineers, will read code or write code. What do you agree with him or no? I definitely agree with the idea that, like, the amount of code that you read is going down over time.
But I think what you're outsourcing is the thinking and the processing over all that code, but you can't really outsource the understanding. And so in order to structure the plans and the code base and everything in the right way, I I still think, and I actually, like, am more and more bullish on this over time, that it matters a ton how deep you are in the fundamentals of engineering.
And so I think that if you would take a an engineer who has really bad tendencies and and maybe pre AI was not a fantastic coder, but they really lean hard into AI, that's actually going to, like, amplify those bad tendencies times a 100.
But maybe if you have someone who is less of an engineer but is more organized and cares more about the structure and and knows how to pace things better and is just better at working with the agents, maybe they'll actually have better results than that person who had the 100 x negative outcome. But then I think the the best case scenario is someone who deeply understands the full stack of engineering and can
push the AI to to its limit. So can you guys share your setup, your enchanting engineering setup at the next? Yeah.
Definitely. So
maybe as a a preamble, we already kind of talked about the the meta harness and and the idea of constructing a a packet of context as code for every single engagement that we have and for every project that we're working on.
And so I think the most important thing here is defining all the the sets of artifacts that the agent is gonna be writing or that we're gonna be exchanging between humans and agents, defining the the structure of those things, and then being able to validate and and easily pull from those artifacts so that the agent can kind of self correct.
So I'm going to just go ahead and share. So the the big pattern from what you're saying sounds like the thinking of, like, a architect is way more important than specific,
like, you know, develop this feature or learn the syntax. I do think that is true. Yeah.
And and I think so one thing you'll see here, if we so we're in this workspace where we actually typically will separate our code repos from our, what we call, our project management repo, which has all of these has all of these artifacts in there.
And so this file that we're looking at right now is what we'll call it's one of the types of artifacts. It's called a convention art artifact, which will have this conf prefix.
It has some metadata at the top so that the agent can parse all of these artifacts and and find them. But then it's an index of all the other conventions that we have in the repo.
And so this is the type of thing that will really keep an agent, like, on rails and following all the really good coding patterns. And so I think it's super important that you know what you're doing in terms of engineering and and good patterns when you build up all these conventions files.
But if you build them in the right way and then you have this system to validate against the conventions, you you can worry a lot less about the quality of the code because you know that it's being followed, um, all over the place. I'm just gonna run, um, some commands to start to kinda show the CLI that we use to manage all these artifacts.
So we're in this, um, workspace right now in in 10 x process, and I'll just say, um, 10 x context.
And right now, I'll say the mode will be for the operator, which is for the human, but we're we've designed this CLI to be both agent facing and human facing. And so right off the bat, this is the type of information that the agent can just immediately get.
It's going to report about the artifacts that exist. So it'll have we have six epics.
This is one of our types of artifacts that defines kind of what we're building and the milestones to get there. And then 20 specs, which are the the way more detailed technical documents about the exact architecture and ticket by ticket, how we're gonna build the thing.
And then you can see we have 35 docs, which includes those convention documents, and then it's also reporting what version of the CLI we're on. We also have all these shared skills that we kind of share across the team and allow people to create their own channels of skills.
And then when skills are kind of good enough or they are, um, sort of central to the process, they'll get merged into main. So I can just show here.
We have if we go 10 x skills status, you can oh, 10 x.
It's a typo. 10 x skills status.
You can see that we have all these skills that correspond to every single one of our artifacts. So if you're creating one of these structured artifacts, the agent knows exactly that structure and and how to how to build that artifact in the in the right way.
You can also see if we do 10 x context and then we make the mode for the agent, it's gonna list out much more detailed information for for the agents to see, and it will actually list out every single one of these artifacts.
So it knows exactly where to find all the information about the epics, the architecture, the different conventions, and a log of all the recent stuff that's happened in the repo. So that would include it it's basically always writing back to this log after it's doing any significant work.
And we have all of this running on a start hook. So every every agent session that anyone at 10 x is running, we're basically we'll call it, like, .
Whenever the agent wakes up, it's getting this kind of packet of context that's telling it all about its workspace and every artifact that it has access to.
Maybe I'll pause there and and Yeah. I have so many notes. Yeah.
Okay. So I wanna first think when you open this up, like, I noticed there are separate repos. Right?
I wanted to ask you guys, is it true for you that, like, over time, the context repo
is actually becoming more valuable than the code repo? I certainly think so. I think, like, the percentage of time that you spend on, like, engineering the markdown should be higher than the percentage of time that you spend on actually executing the code.
I can spend, like, four hours just building really good plans and architecture documents and kind of setting up this whole project management repo. And then I can just say, like, slash goal slash 10 x process slash execute project spec.
And I can just kinda put my computer in the corner and go to bed and wake up with exactly, uh, kind of what I built. And then the beautiful thing is that with this system, it's all all of the tickets have been moved to the right column in linear.
Everything has been written back appropriately to the to the repo. You have just, like, full, um, trail of what it did. And so I I definitely think that the the information and kind of the meta harness and all the markdown is is a lot more important than than the code.
Would you agree, Alex? Yes. I totally agree.
I mean, if I think about just the highest level as, like, an a nonengineer,
what is valuable here? It's just that, like, there's just a set of norms and customs that keep an agent on the rails.
And then the whole issue that people navigate, right, when building software is session to session memory or context.
And so I think this bill benevolent prompt injection that Dan talks about is kind of our solve for making sure the exact right information is provided at the exact right time without lapses so agents can work on long running tasks. Yeah. My question was, like, uh, even deeper.
Like, if if imagine, like, if someone, you know, hacked your system and, like, they could only steal one repo because, like, personally, you know, my code is like, okay. It's already being written by agents. Right?
Like, I I I don't care. Anybody could replicate it. But but all the other stuff, like the core business insights.
Yeah. The process, the ideas, the customer insights, like the the marketing strategies I have, the reason I think it will work.
Like, those are the things that I still value because, you know, they're human. Well, I think in general and, Din, I'd be curious of your thoughts as well. It's like just think about, like, traditional markets, like, markets in terms of supply demand.
It's like there's gonna be the most demand for where there's the least supply, and there's more supply of code than ever before.
There's way more scarcity of, I would say, unique process or business customs or internal knowledge that creates alpha.
And so, of course, that is gonna, over the long term, be more valuable. I would definitely agree with that. I would also think of it through the lens of, like, if you had to reverse engineer someone's product, would you rather just have their entire code base, or would you rather have the the project management repo?
And I think, first of all, the code base becomes really hard for the agent to digest. Even if the code base is, like, the ultimate source of truth of what exists in the project today, it's sort of devoid of all the meaning and structure of, like, why this feature exists and how it relates to everything else other than the, like, direct code links.
And so I actually think you could take a project management repo and probably reverse engineer or even, like, build a a better product because it contains all that intent and conventions and the architecture and the way that you run the system and and all that kind of, like, built in, which is a total,
like, flipping things on its head from the way that engineering used to be. I I was gonna say one thing. And, David, you may find this interesting.
I don't know if, Dan, there's anything to show for this. But one thing I've like, as I learned more about our process that I always found interesting is this idea of how do you make more and more things outside of, like, the pure code verifiable.
And I feel like the whole, like, validator step you have is just, like, a really interesting piece of this pie. Yeah. So I can I can go ahead and say 10 x validate?
Let's see what that'll give me. And so, immediately, we're we're finding basically these warnings that it's going through all of the artifacts that we have in in this project management repository.
And then there is a a really long rule set that kind of aligns with our just SOPs for how we like to write software and all the steps in the process of taking, um, customer requirements or a problem that we're trying to solve all the way to, like, a landed PR or a landed product.
And so in this exact in this example, basically has, um, the checkpoint status is drifted in this project spec.
And so what that means is that the authored status where it says complete as the status of the checkpoint is disagreeing with the derived status of in review, and that derived status is basically computed from the rules that we've set. And so in this case, it might mean that, um, the status of that project spec says it's complete, but the project spec actually has tickets in inside of it that are not yet complete or the tickets are in review.
And so you can't resolve the full projects to complete it if not all the specs are if not all the tickets are completed.
And so you can just kind of extrapolate from there and multiply that rule out, and we have kind of hundreds of these types of rules. It's kind of like linting for your SDLC or your entire engineering process and not just linting for the code. And and this it also takes advantage of this property of agents, which is that the agent can use the CLI.
And so the agent can constantly be figuring out what's the most important thing to do next, what is drifted, and how can I how can the agent fix it? And so it's this, uh, very nice, like, self improving
loop. Yeah. I think there's tremendous alpha in actually having agents hold humans accountable.
Right? Like, if you say, like, these are the priorities of the company long term, these are the biggest projects for this week, and then, like, somebody gets distracted by, like, a cool idea from Twitter. It's like, listen.
This is this is not on our road map. This is not, you know, top five priority. Why are you working on this?
I think people are need to be, like, way more comfortable giving the agents the things they're great at. Right?
Because with each generation of models, the agents are actually better than humans at more things. Everybody understands it's already, like, digesting loads of content.
You if you take a whole book, 10 books, and, like, read it instantly. Right? But, like, consistency, predictability, all these things, humans are really bad at.
Humans are kinda random. They're creative. You know, they like to switch from task to task.
But agents, I think, will be used more, like, to hold us accountable. Like, for me, like, I have many skills where, like, build as much as possible yourself, and then once you're blocked by something like me, walk me step by step, and, literally, I just send it screenshots, and it helps me do some setting in Supabase or something.
Where, like, normally, know, I would just probably take way longer, and the agent would would be, like, writing
a little bit of code, then it's blocked by me, then then, you know, speak more about this. You know, not just building for humans, but building for agents. Because you mentioned that earlier, and I think this is where the world is headed.
Yeah. I think I think you kind of have to just think so creatively about the way that software should be written. Now it's kind of like everything you ever learned about building software is is kind of thrown on its head.
And I think we usually I I hear this, like, negative framing a lot of times, which is, like, the agent is deficient at coding or or work working with the code base because every session starts from it starts from zero knowledge of the code base, and you have to kind of, like, build up its context.
And so only the human can, like, maintain this coherence across many different sessions. And I think if we think about it from the perspective of, like, the things you said, consistency, structure, how can we, like, make use of all the advantages of agents and then just solve those deficiencies?
We can get a very interesting result. And so one thing we do is basically we think of every session as the agent should start as, like, a .
It should immediately know what's going on. So does the hook. Yeah.
So the the hook and then if we I'll just change this to low so it goes a little faster. If we use this 10 x process skill, so it knows what to do, and I just say, like, should I work on next?
It kind of immediately see, it runs that hook with with all the context that you saw earlier, and then it's gonna use some 10 x process commands and pull in kind of the exact things to work on next, and it can also pull in the the architecture.
And so it just solves very naturally that problem of, like, the agent doesn't know what it's doing when you start a fresh session. Now not only does it know what it's doing, but it's following this extremely consistent and rule based process so we can have kind of, like, level up the humans that are working this way as well.
Did you guys open source any of these? It's kind of an open discussion of what parts of this we we will open source or not.
So
maybe maybe in the near future, we'll we will do something like that. I will think about it. You don't have to start with everything, but, like, you know, when I released my skills repo, it quickly became my number one GitHub repo, and there are way more popular skills repo than mine.
So I think this is a very good strategy to get some motion. Yeah. Definitely.
Appreciate it, guys. Thank you for your time. Where should people go?
If people want to learn more about what we're doing at ten x, 10x.co.
We are hiring exactly one shit ton of people, cracked engineers, AI strategists, full stack creators, you can go to 10x.co/careers.
And if you wanna just follow along with the content we're putting out, follow me on x at Business Barista because not only am I creating content, but I'm amplifying all the content by folks like Dan, CJ, and other people in the company. So you'll see kind of everything from the what we call WWE for nerds from my account.
Alright. Awesome. I'm gonna put all those links below the video.
So, again, guys, thank you for your time, and have a great day. Thanks so much. Thanks so much.
Alright. So here are the results from the scrape. As you can see, it found the AI companies valued at over $1,000,000,000, and it found who the CTO is and started looking for the two forms of contact for the CTOs.
And, this is just a single use case. You can imagine hundreds of different things you can do with deep API. In fact, if you go to the docs, you can look at API reference and see all of these different endpoints.
And, again, all of these are available through a single API key. No setup required. No configuration or authentication.
You get all of this right off the box. And now, as I mentioned, we're opening up Deep API to the public, and we're giving every new user $1 of free credit. So just go to deepapi.co and give it a shot yourself, see how it works, try scraping something, try accessing some difficult website, doing a deep research, looking for more clients, whatever you want.
You know? Try it yourself. Just go to deepapi.co.
It's completely free. We give you $1 to play around and test it yourself, or it's also going to be the second link below the video. So click on that and try deep API right now.
The Hook
The bait, then the rug-pull.
Alex Lieberman and Dan Zakon run their AI consultancy, 10x, on markdown before code: every client engagement gets its own project-management repo of specs and conventions, checked by a self-linting CLI that briefs coding agents the instant a session starts. This episode walks through that system end to end, plus the internal "content machine" that turns a 20-minute voice interview into a scored, on-brand post.
How they asked for the click.
MENTIONED ON CAMERA
