Marketing Agents Masterclass: Two AI Marketing Agents Built Live
Cody Schneider returns to build two marketing agents live on screen — a cold-outbound machine that turns LinkedIn engagement into enriched leads, and an organic engine that turns internal conversations into a daily content pipeline.
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1 weeks ago
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Format
Interview
educational
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Big Idea
The argument in one line.
A marketing agent is just code on a cron job with a live data stream and a language model inserted only where judgment is genuinely needed, and the same waterfall-enrichment and human-sourced-content patterns can build both a cold outbound lead machine and a daily organic LinkedIn engine for a whole team.
Who This Is For
Read if. Skip if.
READ IF YOU ARE…
A founder or growth lead who wants a concrete, tool-by-tool blueprint for a cold outbound lead-gen system rather than another explanation of what AI agents are.
Someone running a small team who wants every teammate posting LinkedIn content without hiring a social media manager or writing every post by hand.
A builder comfortable running scripts in a coding assistant like Claude Code or Codex who wants to wire scraping and enrichment APIs together directly.
SKIP IF…
You're looking for no-code, drag-and-drop tools — this walkthrough assumes you're scripting API calls yourself.
You want detailed legal guidance on cold-email compliance — the hosts explicitly flag they aren't lawyers and only cover it at a high level.
TL;DR
The full version, fast.
Cody Schneider returns to build two marketing agents end to end. The first scrapes people who engage with LinkedIn posts from creators in a target niche, waterfalls those profiles through GetLeads, Apollo, and Origami to find verified emails and phone numbers, then runs cold email and LinkedIn DM outreach from burner domains, with an agent managing inbox replies and long-tail follow-ups. The second mines real conversations — 1:1s, sales calls, transcripts — into a daily LinkedIn content engine scheduled through Ordinal, letting an entire sales team publish without writing from scratch. Throughout, an agent is framed as code plus a live data stream, with an LLM used only at the exact decision point that needs judgment.
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Greg frames marketing agents as the new coding agents and brings Cody Schneider back for a second, more detailed episode.
02:27 – 04:26
02 · Agent Number One: Cold Outbound Agent
Cody previews the first build: a system that monitors LinkedIn posts from niche influencers, extracts engagers, and enriches them for cold outreach.
04:26 – 09:09
03 · Finding Creators in Your Category on LinkedIn
Cody demonstrates searching LinkedIn for niche creators and posts, explaining why a good search surfaces content your actual target customer engages with.
09:09 – 10:59
04 · Apify Explained and the API Maestro Actors
Apify is introduced as the scraping API layer; API Maestro's LinkedIn actors (post reactions, post comments, profile posts) are the specific endpoints used.
10:59 – 12:59
05 · Extracting Engagers Live in Claude Code
Cody runs a script in Claude Code that pulls a post URL and returns every deduped engager profile, pulling 63 raw engagers from one example post.
12:59 – 15:45
06 · Agent Versus Automation
Cody defines an agent as code plus a thinking loop plus a live data stream, and argues against giving a model broad standing access to run whole workflows.
LinkedIn profiles are waterfalled through GetLeads first, then Apollo, then Origami or Prospeo, moving from cheapest to most expensive provider.
17:13 – 21:40
08 · Compliance, Data Brokers, and What Stays Legal
Cody explains that buying contact data from brokers is legal in the US, while usage (CAN-SPAM, EU rules) is where compliance obligations kick in.
21:40 – 25:38
09 · Waterfall Enrichment: Million Verifier and LeadMagic
Found emails are validated through MillionVerifier before sending, and LeadMagic is added specifically for phone number enrichment.
25:38 – 28:33
10 · The Cold Outbound Infrastructure
Cody lays out the four-domain rule (cold email, marketing, transactional, business) and the roughly $200/month cost to send 10,000 cold emails.
28:33 – 31:41
11 · Software Factories and Marketing as Code
The conversation zooms out: marketing is treated as code, with a pipeline-warehouse-agent architecture where the agent reads from a warehouse and writes only through an API.
31:41 – 39:34
12 · Agent Number Two: The Organic LinkedIn Engine
Cody walks through mining real conversations (1:1s, sales calls, transcripts) into insights, drafting posts with an LLM, and scheduling them across accounts via Ordinal.
39:34 – 42:31
13 · The Power of Earned Media
Cody makes the case that free organic impressions carry the same dollar value as paid impressions, and that platforms increasingly pay creators directly.
42:31 – 43:59
14 · Closing Thoughts
Cody points viewers to graphed.com and his socials, and Greg asks for comments to decide the next topic.
Atomic Insights
Lines worth screenshotting.
A LinkedIn like or comment is a stronger buying signal than any firmographic list, because it shows real-time interest instead of a static snapshot from last year.
Ten to twenty well-chosen creator accounts in a niche typically cover about 80% of that niche's relevant audience.
Waterfall enrichment chains cheapest-to-priciest data providers — GetLeads, then Apollo, then Origami or Prospeo — so premium lookups are only spent on the hardest remaining contacts.
Sending 10,000 cold emails from a company's real domain can permanently damage that domain's deliverability, which is why cold outbound runs on burner domains instead.
Roughly $200 a month covers both inbox infrastructure and sending software for about 10,000 cold emails.
An agent, stripped of hype, is just code on a cron job with a live data stream, plus a language model inserted only where actual judgment is required.
Buying B2B contact data from an aggregator is legal in the US; what changes the legal picture is how that contact is subsequently used, and the rules differ from the EU.
Reads should come from a data warehouse and writes should go through an approved API — mixing the two is what gets marketing accounts flagged, not the presence of an agent itself.
The most repeatable content engine starts from real human conversation — sales calls, 1:1s, Slack threads — because a model asked to invent ideas cold produces generic, easily-flagged output.
A LinkedIn post can earn around $22 worth of impressions per thousand views for free, the same rate advertisers pay to buy that same attention on-platform.
A small set of proven posts can be reposted on a 90-day cycle for years, because most of the audience never saw the original run.
Founders who ask 'what do people already want to buy that they can't buy yet' outperform founders trying to invent demand from scratch — the same logic applies to content topics.
A topic-based or theme-based account can build and monetize an audience without anyone behind it adopting a personal brand.
Takeaway
Marketing agents are code with judgment inserted only where it's needed
WHAT TO LEARN
Two full marketing agents — one for cold outbound, one for organic content — both reduce to the same shape: a scheduled pipeline built on a live data source, with a language model called only at the single step that requires real judgment.
02Agent Number One: Cold Outbound Agent
Treat a social media like or comment as a live intent signal, not a demographic filter — it shows someone cares about a topic right now, which a static firmographic list can never show.
Cold email reply rates are falling across the board because AI-generated volume has flooded every inbox, so differentiation now depends on who you target, not how polished the copy is.
03Finding Creators in Your Category on LinkedIn
Ten to twenty creators or company pages in a niche typically cover about 80% of that niche's audience, so chasing full coverage past that point mostly adds cost for thin returns.
A company's own team usually already knows which creators and accounts its buyers follow, so that internal knowledge is a faster seed list than a fresh outbound search.
The same logic that finds outbound leads also fixes creative fatigue in paid ads: track a handful of human creators for outlier posts instead of asking a model to keep generating fresh ideas in a loop.
04Apify Explained and the API Maestro Actors
A single scraping API key covering LinkedIn, Twitter, and other networks turns public social engagement into structured data an agent can act on.
Vetting which scraper integration to use matters as much as the API itself — many listed integrations go unmaintained, so stability history is worth checking before building on top of one.
05Extracting Engagers Live in Claude Code
A working lead-scraping pipeline can start as a short local script a coding assistant writes by reading the API's own documentation, then get promoted to a scheduled cloud job once it's proven.
Deduplicating scraped profiles before enrichment avoids paying enrichment-tool fees twice for the same contact.
06Agent Versus Automation
The clean definition of a marketing agent is code plus a live data stream, with an LLM inserted only at the exact decision point that needs judgment, such as an ICP fit check.
Paying a model's per-token API price to run a repeatable process is wasteful — building software that calls the model only when a genuine judgment call is needed is cheaper and more reliable.
Giving a language model broad standing access to an ad account or CRM is the wrong pattern; the reliable pattern is software that reproduces the specific steps a skilled human already follows.
07Waterfall Enrichment: GitLeads, Apollo, Origami
Chain enrichment providers cheapest-to-most-expensive — GetLeads, then Apollo, then Origami or Prospeo — so premium lookup costs are only spent on the harder remainder of a list.
A single all-in-one aggregator like Origami can run the entire waterfall in one call, trading control for convenience.
08Compliance, Data Brokers, and What Stays Legal
Buying B2B contact data from an aggregator is legal in the US; the compliance risk shows up in what you do with it afterward — CAN-SPAM rules apply to email, and the US and EU treat this very differently.
Validate every email through a checker before sending, since a provider's own verification isn't always sufficient and bad addresses directly damage domain deliverability.
09Waterfall Enrichment: Million Verifier and LeadMagic
A worked funnel makes the economics concrete: 50 profiles might yield 32 verified emails from the first provider, another 10 from the second, and the remainder from a third — no single tool needs to find everyone.
Phone enrichment is treated as a separate specialized step rather than bundled into the same tool used for email.
10The Cold Outbound Infrastructure
Never send cold email from your primary business domain — a burst of 10,000 sends can permanently damage that domain's deliverability, so cold outbound needs its own burner domains and inboxes.
Keep four domain lanes fully separate: cold outbound, email marketing, transactional email, and the domain your team actually runs the business on.
Budget roughly $200 a month total, about $100 for inbox infrastructure and about $97-100 for sending software, to run around 10,000 cold emails.
11Software Factories and Marketing as Code
The most durable mental model right now is that the only real 'agent' is a coding agent — everything else it's asked to do becomes software that agent writes, not a standing autonomous process.
A reliable agent stack has three layers: a data pipeline pulling from every source, a queryable warehouse, and an agent that only reads from that warehouse and writes back through an approved API, never mixing the two.
12Agent Number Two: The Organic LinkedIn Engine
Feed a writing agent real source material — recorded 1:1 conversations, sales call transcripts, internal Slack or Notion — instead of asking it to invent ideas cold; an unprompted 'write good content' request produces generic output every time.
The actual bottleneck for scaling personal-brand content across a team isn't the writing, it's mining insights out of conversations that already happened.
A recurring interview cadence, a standing weekly 1:1 with each teammate structured only around 'what did you learn this week,' is enough raw material to run a daily content engine.
13The Power of Earned Media
Earned social reach has a real dollar value: at roughly $22 per thousand paid impressions, an organic post that gets even modest reach is generating value equivalent to a paid media spend.
A theme-based or topic-based account, rather than a personal or company-branded one, lets someone build an audience and funnel it to a business without requiring a personal brand.
Winning content isn't infinite — a small set of proven posts can be reposted on a roughly 90-day cycle for years, because most people never saw it the first time.
Glossary
Terms worth knowing.
Waterfall enrichment
Chaining multiple contact-data providers in order from cheapest to most expensive, so each provider only has to fill the gaps the previous one missed.
Apify
A scraping API platform that gives one API key access to pre-built 'actors' for pulling data off LinkedIn, Twitter, and other sites.
API Maestro
A publisher of LinkedIn scraping actors on Apify, used here for post reactions, post comments, and profile-post extraction.
GetLeads
A B2B contact database and API used as the first, cheapest step in a waterfall enrichment chain.
Origami
An enrichment tool that can also aggregate and run an entire waterfall enrichment chain in a single call.
MillionVerifier
An email validation service that flags addresses as good, risky, or bad before a cold send goes out.
LeadMagic
An enrichment tool used specifically to find mobile phone numbers for a contact.
ICP
Ideal Customer Profile — the target buyer definition used to filter which scraped leads are worth enriching and contacting.
CAN-SPAM
The US law governing commercial email, covering required disclosures and opt-outs for cold and marketing email.
Ordinal
A scheduling and analytics tool for publishing to and managing multiple LinkedIn accounts at once.
Airbyte / ClickHouse
An open-source data-pipeline tool and an analytics database, used together to build the data warehouse an agent reads from.
SDR-in-a-box
An agent setup that finds leads, enriches contacts, sends outbound, and manages inbox replies with no human in the loop day-to-day.
The video's entire thesis in seven words — a strong cold open for a repurposed clip.→ TikTok hook↗ Tweet quote
03:33
“Reply rates are down. Everything is down. Every marketing channel is down right now... the reason is just because AI slop is flooding the zone.”
Names the exact problem the rest of the episode solves, with a punchy 'AI slop' line.→ IG reel cold open↗ Tweet quote
14:10
“Everybody tried to put God in a box and give it access to a Facebook Ads account, and we realized that is not the right way to do this whatsoever.”
Vivid, quotable metaphor that also carries a real warning about over-scoping agent permissions.→ newsletter pull-quote↗ Tweet quote
15:00
“You should not be paying Anthropic. You should not be paying Chat GPT to do an API call. You should be paying them to make the software that uses CPU to do the API call.”
A contrarian, specific claim about token economics that cuts against the 'token abundance' consensus.→ TikTok hook↗ Tweet quote
18:20
“It is fully legit to get these emails. What you do with those, that's where things, from a compliance standpoint, change.”
Directly answers the question every viewer building this will have, in one clean line.→ newsletter pull-quote↗ Tweet quote
26:40
“If you send 10,000 cold emails from your real domain, you will nuke the deliverability of the business URL, the actual domain that we use to run our company.”
Concrete stakes and a specific number make this a strong standalone warning clip.→ IG reel cold open↗ Tweet quote
30:00
“The only agent is a coding agent, actually. Everything else is just software that's being made by the coding agent.”
A crisp, repeatable one-liner attributed to Cody's cofounder that reframes the whole 'agent' conversation.→ TikTok hook↗ Tweet quote
35:00
“If you look at my Twitter post as an example, or even my LinkedIn, it is the exact same thing remixed every ninety days. Full stop.”
A candid, slightly self-deprecating admission that undercuts the myth of constant fresh content.→ newsletter pull-quote↗ Tweet quote
40:50
“It's like $22 per thousand impressions is the average. Every post that you get, even with an account that's like 500 followers, you can get a thousand impressions. That's like $20 that you just put into your pocket for free.”
Turns an abstract 'organic is valuable' claim into a hard, memorable dollar figure.→ IG reel cold open↗ Tweet quote
43:36
“Distribution is more important than product.”
Cody's own Twitter bio line, shown as the closing card — short, quotable, and debatable enough to spark comments.→ TikTok hook↗ Tweet quote
39:34 – 43:59steadyEarned media economics and closing
The Script
Word for word.
Read-along
Don't just watch it. Burn it in.
See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.
17px
metaphor
It's true. Marketing agents are the new coding agents. Just like coding agents were such a big deal and people were able to create software on demand, deploying marketing agents are so important because you're able to get customers on autopilot.
So how do you actually set them up? What do they look like? Well, this has gotta be my most requested episode in a long time.
I bring back Cody Schneider and he shares all the sauce. How you can use Codex or Claude Co to to build these. What are the other 20 tools that you need for the marketing infrastructure in order to deploy these marketing agents?
And by the end of this episode, you're gonna get your creative juices rolling around some of these growth tactics that are gonna help you stand out, that are gonna help you get customers, so that whatever it is you're building, you don't have to worry too much about traffic, you don't have to worry about too much about revenue, and you can focus on building an incredible product while your marketing machine is running.
Enjoy the episode.
Welcome to Greg Eisenberg's podcast called Sip Baby. I'm your cohost or guest today. Not cohost.
I'm never the cohost. I'm Cody Schneider. I'm gonna be your guest today.
And today, I'm gonna teach you how to build an AI agent that does cold outbound both on email and on LinkedIn. This is based off of the comments from last video. If you wanna learn other go to market motions, you need to comment below right now.
Do it right now. It also helps us for the algorithm. So you're supporting the show.
It keeps the lights on here.
Welcome to the show, Cody. Marketing agents are the new coding agents.
We only shared one marketing agent last episode, but the people aren't satisfied with one. So you needed to come back on. You came back quickly.
And by the end of this episode, you're not gonna share one end to end marketing agent. Right?
You're gonna share two marketing agents, how people could set it up, so by the end of this episode, people can go
stop the video and actually go set this up and actually get customers to their Vibe Coded startup. Right? This is exactly what I'm promising you today.
You're gonna have two of these in the wild. I'm gonna teach you everything that you need to know. I'm also gonna share all the tools that you need.
There's no gatekeeping here. I spies people that do this. Don't buy a course.
Literally DM me. I'll teach you anything. I'll just make a public video for everybody.
So let's do it, g. Alright. Let's run it.
Awesome, man. Alright. So today, we're gonna build a system that basically monitors LinkedIn posts of influencers within your niche, within your category, and then it's gonna go and extract the engagers from those posts.
Um, and then we're gonna do what's called a waterfall enrichment to find the emails, uh, and even potentially the phone numbers of these people so that you can then go and do an outbound motion to them, um, doing cold email and then also, uh, doing LinkedIn DMs. So that's what your is gonna happen.
And then I'm gonna, uh, teach you how to basically have it so you can have an agent that's wired up to both of those inboxes, like managing those inboxes, say, for example, answering questions or trying to push them into, like, booking a demo with you as an example. So, yeah, man.
That's that's really it. The I don't know if there's any other, like, specifications on the high level. I think the only thing to mention with this is, like, the strategy around this.
So right now, cold email is getting decimated. Reply rates are down. Everything is down.
Actually, every marketing channel is down right now. Let's be let's be real. The reason is just because, like, AI slop is flooding the zone, and it's becoming just red ocean everywhere.
But the way that we have found that you can stand out is you have to look for signals or triggers that basically show that people are hand raising, saying, hey.
I want I want this thing. I have an interest in this thing. Right?
And a great way to do this is with these LinkedIn engagements. They're basically when they like content, that is a a a hand raise or a signal that I am interested in this, you know, specific thing.
And from that, we can use that as a way to measure, okay. Is this my target customer that I'm trying to sell to? And not just, like, their firmographics or their demographics or their psychographics, which is, like, what we would traditionally use for for outbound.
Um, this is specifically like, no. They they have a propensity or an interest in this topic, and we are gonna go and now get in front of them.
Okay. So how do we actually do this? And this is an exact strategy that we implement for, you know, the companies that we're working with.
So I'm gonna teach you that right now. So let me screen share, and I'm gonna walk through it. So the first thing that you're going to want to go to do is literally go to LinkedIn and find influencers within your category.
So last episode, we talked about AI for WordPress or AI WordPress. And so I'm just gonna use this again as an ex the example, you know, like, target demographic that we're going after. So on LinkedIn, what I would go do is I would go try and find people that are talking about WordPress development potentially.
Let's see what comes up with that. Development. And I would try to find posts.
This might actually be a terrible category, so I we might have to explore something entirely different. But I would try to find posts or creators that are talking about these specific topics, like, on a daily cadence.
Right? So like this, like again, just for this example today, this is probably gonna be, like, a lot of, like, just not good signal.
So a better way to look at this is, uh, we'll say we'll do AI from or AI marketing. Right? Um, let's see what comes up, and we're gonna try these find these posts here.
So
And what makes a good search? Like, why why was AI for WordPress not good, and why is AI marketing better?
Yeah. So it's really just like, is the content that's being served what your target customer would be interacting with? Like, that's what you're trying to get down to here.
Right? So, like, how I would be going through this and, honestly, I use the the for you page of all of these algorithms is so good now that it's like, it's gonna show you the content that's relevant. Right?
Like, this is literally an exact like, perfect, like, perfect example. First one that comes off.
It's like, awesome. People trying to do some type of video editing for it's probably for marketing. Everybody that's potentially engaging with this is like a target customer.
Right? So I would say, okay.
Cool. I'm gonna find these creators, and then I'm gonna build a spreadsheet of all of them.
Right? Like, of these people, um, that I'm going to try, uh, that I'm gonna source these leads from. So, right, I would build this spreadsheet out, and we'll just do a handful of these, like, from my own feed.
It can even be business accounts, and I think this is the thing that people don't realize. Like, if there's business accounts that's that people would be interacting with that would be your target customer, that can work as well.
Right? So it can be literally Clay. And we're gonna do the posts from Clay, and we'll just keep going down on this.
So MCP,
it's probably too broad. And you're doing this manually. Like, you're not using agents to do this.
Why? I I wouldn't even
typically, the company knows who is interacting. Like, when we're working with a business. Right?
They know who their, like, their target customer is interacting with. Right? So you can all you need is typically, like, ten ten to 20 of these, and you have more than enough to be able to, like, source the lead volume that's necessary to actually make this, like, a viable channel.
You I'm using the feed here because, like, what it's going to show you is what is most, like, relevant to you. So it's probably going to be stuff that's, you know, in your the niche that you're in.
But you can also use the search for this as well. We used to do this where we'd, like, do the search and we'd find the trending posts from that peer in reality, it's like there's a handful of outliers within any niche, and everybody is engaging with those handful of outliers.
If you just monitor those outliers, you're actually going to get, you know, 80% surface area coverage for that entire industry.
You don't need more than that. Right? Or or it's it's it's it's just like the marginal return of trying to go for all of it.
It's not it's not there for for that sis. This is the same idea with, um, we do this a lot. Like, we try to solve entropy this entropy problem with, uh, within, like, ads, paid ads in particular, where it's like, if you just have the agent, like, go in this loop, it'll just kinda make the same ideas over and over again.
How do you solve for that? Well, you find human creators, like 10 of them on Instagram, and you track the content that they're they're publishing.
You look for the outliers. And then from that, you you typically can get signal of, oh, here's this new hook format or here's this new topic.
I can just pull that. I can remix that, and that's the way to do this. So alright.
I find a handful of these these these companies. And then from that, what I'll go and do and just for the sake of, you know, the example today, we'll use Louise as an example.
So we'll say everybody that interacted with this post. We're gonna use this post as an example.
So once I have these people, I need to use Appify, and I will find the actual one that we like.
And what's Appify for people who don't know? Yeah. So Appify is a scraping API.
So I can use a single API key, and then I can use it to scrape LinkedIn. I can use it to scrape Twitter.
I can use it to scrape all of these different channels. So it's a way for me to get data into the context for my agent so that it can have, you know, awareness and and have that context for it to make decisions on or make content based off of, etcetera. So okay.
So the one that you're gonna wanna use or the one that we like, we've worked with him, like, a decent amount because it's the most stable connections. There's tons of these, and the challenge with Apify is finding good ones that are actually, uh, like, being monitored and being maintained.
And so this, uh, this guy, API Maestro, has a ton of these for LinkedIn. You can see all of these here.
It's all of these different functions that you can do. So how Appify functions is you get an API from Appify, and then this enables for you to be able to have your coding agent, like ClogCode or Codex, call from, uh, app or call the app through the Apify API to one of these endpoints that are here.
So, uh, for example, you can do this post scraper. Uh, for the one that we're going to do, it's going to be engagements. So let me find that.
Post reactions on LinkedIn. I believe this is it.
This is exactly it. Yep. So post comments and then post reactions are the two that you're going to use.
And what this enables you to do is everybody that has engaged with that post. So the post that we are just looking at here. So everybody that's interacted with this and commented on this, we're gonna be able to pull this out.
I'm gonna show you how you can actually do this in Clogcode right now. So I'm just gonna spin up a terminal real quick, and let me reshare my screen.
And so I have that I have that Apify API key in already saved locally within the directory that I work out of for, uh, all of my growth work.
And if you don't know what I'm talking about here, I have a whole video on my, uh, channel that's basically a crash course into how to do this. It's called go to market engineering or marketing engineering. And it will walk through the entire setup process.
Takes about ten minutes. But, basically, this Apify API key is shared here, and I've already written this script. I had the agent go and read.
How do I use this endpoint to pull out all of the posts and comments information? The all all the people that have interacted with this.
So I can give it this post URL, and I can say, extract the engagers using, uh, the Appify API key, and it's going to go and run that process process for me.
So this is how I would go and build this automation or build this agent as I would basically take this code and I would deploy it into the cloud. And I would say, okay. On a daily cadence, I want you to check for net new posts.
So that is where I would look at the profile posts. So this is the profile post scraper. So I would extract the post URLs from this person.
Right? So every net new post daily is getting extracted. And then from that, I'm then extracting the engagers using that API endpoint as well.
Right? So right now, as you can see, the duped by public profiles, they're 63 raw, and it's about to pull all of those contacts out. So once I have those contacts, this is this is done, man.
Like, game over. As long as you have the LinkedIn profiles, you can go and find the email addresses of them. You can find the phone numbers of them.
You can find everything that you need on the cold outbound, and I'm gonna show you that right now. What are the tools to actually go and use to do this? Um, so let me just show you, though, again, just the, uh, the final completion of this.
And what makes this an marketing agent versus a marketing automation? Yeah. So the agent component of this is that it is running on a cron job daily.
And then you're gonna have an agent that's later on, we'll have it responding to the inbox. And this is this blurry line. Right?
Like, what is an agent? People ask me this every sales call.
And the answer to all of this is, like, it's how I think about it personally is it's something that's doing a job to be done. Right? So the job to be done here is finding leads and outbounding to those leads and then responding to those leads as the like, asking questions or, again, like, driving them deeper into the pipeline.
Um, in reality, though, g, like, what is a mark like, what is a marketing agent? It's it's code. It's maybe some thinking loop, and it's a live data stream.
Right? That that is really how, like, this functions. And the thing that you can make you know, extend this further with is, like, what you're who you're outbounding to.
Um, you want it to basically do an ICP fits or a is or or a a target customer segment fit. So before it even does this enrichment that we're about to do, you would be like, okay. Agent, research this person and the company that they're at.
How many employees do they have? All of these things. And then based off of what we find, if it fits this customer profile, like, it's you're gonna have the agent basically think through that, right, using an LLM.
If it fits this customer profile, then it goes into this enrichment. Then we're actually gonna cold email them. So that's where that thinking loop could potentially be here as well.
But, really, the the blurriness between all this I think about it as software anymore. Like, to be transparent. Like, everybody the thing a different way to say this is, like, everybody tried to put God in a box and give it access to a Facebook Ads account, and we realized that is not the right way to do this whatsoever.
The right way to do this is, like, what was the human doing? They were running this very specific process with, like, media buying. They were researching ad creative angles.
They were making new ad creative. They were testing the new ad creative, and then they were, like, pruning the losers, promoting the winners. Right?
Like, that is what the a top media buyer does. Okay. How do we go and make a piece of software that does that exact same thing?
So when you hear agents, really just think software with potentially a thinking loop.
Like, you shouldn't be paying a different, like, way to think about this, and this is something I'm obsessed with right now. You should not be paying Anthropic. You should not be paying Chad GPT to do an API call.
You should be paying them to make the software that uses CPU to do the API call. Why are you paying this tax on tokens every time that you're trying to do this marketing activity?
That's ridiculous. Build the software that does the solution for you, not tokens burning every time that you're trying to do the action. So, anyway, um, okay.
So we've got these LinkedIn URLs. And what do we do with them now? So we're gonna do what's called a waterfall enrichment.
And so we're basically gonna use these LinkedIn profiles to go and find the email addresses and then the phone numbers of these individuals. So how do we do this? The first thing that we're gonna use in a tool stack is called getleads.io.
Um, so this is a database of, uh, it's basically, they aggregate all of these b to b contacts, and you can access it via their API.
Um, the emails that we don't find within GetLeads, we're then gonna use some we're gonna waterfall down to something like Apollo, And then you could take this even further down into something like Origami. It's another tool that we have been using and experimenting with.
Also, their team is just doing awesome work. Like, Finn and his whole team is incredible. So, anyways, for GitLeads, let's go back to our our terminal right now.
So, again, this is me hands on keyboard doing the process to teach it to you. But everything that I'm doing right now, this is all just going to be code under the hood. And once it's code, I can deploy that into a cloud system As long as it has the necessary data that it needs and the necessary access that it needs, it can go and run this operation autonomously.
And then you're just there basically jockeying the agent or modifying the system. Right? So we're building a system here.
So from here, what I would then go to is say, use the GetLeads API to find the emails and phone numbers.
And, like, dumb question. Yeah. That's legit.
Like, you know, like, it's not gray to get these people's emails. It's, like, fully legit.
It is fully legit to get these emails. What you do with those, that's where things, like, from a compliance standpoint change.
You can cold email technically in The United States. You can also add people to a email newsletter, to be and be CANSPAM compliant.
There's, like, tons of, like, thing you basically have a checklist of things that you have to do. With this said, though, um, like, this is one of these like, on the cold email side and the contact lookup, um, you're basically just buying data from a data broker, which is is legal.
Right? That that is accessible. So these companies, how they do this is they basically are buying all these lists and then aggregating them from all of these different data brokers.
That whole piece is is a whole other shady network. But this, uh, like, what we're talking about here, you know, on the spectrum of, like, black hat to white hat is pretty far on that white hat side. So
Cool. Yeah. I mean, I don't think anyone would,
you know, mistake you for a lawyer also. Oh, totally. Take this with a grain of salt.
You know? And and, like, there's also different compliance rules within United States. Research.
Exactly. Exactly. The within, you know, The United States versus, uh, like, the EU has totally different compliance pieces.
Exactly. Um, but with that said, like, the, you know, the finding of people's information and then, like, reaching out to them, uh, they're you you can do this, basically.
It's kind of the high level. But, again, this I I we don't have time today to go into all of the the specifics about, like, all this the the finite details here.
So once I found this, um, each of these individuals and then the emails, from there, what I'm then going to do is validate these emails. So I would send it to a software called MillionVerifier.
So MillionVerifier, um, enables me to, uh, basically check if the email is good, risky, or bad. Um, you know, more technical terms would be, uh, like, good, catch all, um, you know, risky, etcetera.
Um, the the reasoning for this so the reason you have you wanna do this is the emails that come out of these providers, so out of GitLeads, out of Apollo, out of Origami.
I think they do some checks, like, a little bit deeper, though. So, you know, I I I don't know much as much about this, but I know for sure with GitLeads and Apollo, it's, like, do the second verification. You're basically only wanting to send cold email to valid emails.
Because if you send to invalid emails, you're going to basically just run into deliverability problems. And probably right now, you're asking yourself, like, okay. Cool.
How do you send these cold emails? I'm gonna show you that in a second. Bear with me.
So we've done that waterfall enrichment. We found the emails. We found the phone numbers.
And when I say a waterfall enrichment, what is happening here is we're taking that list of 50 and just to use the spreadsheet as an example. So say we have, you know, 50 that we have, uh, 50 LinkedIn URLs that we found.
And on GetLeads, maybe we only find, you know, 32 emails of those people.
Right? So that next cohort, so those other 18 that are left, I'm then going to send those 18 to Apollo.
So of those 18 that I send, maybe I only find 10. And then those eight, that's when I would send that to something else like Prospio or Origami or these other enrichment tools.
And the reasoning behind this is you're you're starting with what is the cheapest, most accurate, and then moving your way down into the more expensive, uh, validation tools.
Um, but from this, you can pull out basically from a list. Like, you know, this is the way that you get to, uh, you know, an 80% fine rate, etcetera. And you can chain as many of these together as you want.
Um, it just, you know, depends on your budgets that are available, etcetera. And there's also aggregators of this, like Origami as an example, like, aggregates this waterfall for you. So you can just send them a LinkedIn profile, and it's gonna, like, waterfall through the options that are available.
Okay. So the other other thing to throw in here that will be valuable to your team is a software called Lead Magic. So this is one that I we use a lot for, like, mobile phones in particular.
Um, but same strategy here. Uh, just basically, you know, another enrichment tool, uh, but specifically on the phone number side, we we've we've used a a decent amount. So once I have that contact information, I now need to go and actually build this outbound motion.
So on the cold email side first, how do we go and do this? We need to buy inboxes. So a couple different ways to do that.
I can use a tool called InboxKit. I can use Instantly AIs. It prebuilt, uh, like, emails that you can buy from them, Or I can use, uh, a company called HyperTide, which is the partner that we use and we work with.
They are some of the best in front my opinion. So when you're buying these emails, uh, you're buying or you're really, what you're doing is you're buying inboxes and domains that are burner domains that enable you to send cold email, um, not from your core domain.
And the reason that you have to do this is so that you don't burn the deliverability of your core domain. So what do I mean by that?
If you send from, you know, your exact domain and, you know, say we send 10,000 cold emails from that, We will nuke the deliverability of the business URL, the actual domain that we use to, you know, run our company.
Right? You don't wanna do that. So, typically, what you wanna do on the marketing side is have this have this separation.
So you have domains that are for your cold email. You have domains that are for your email marketing. You have domains that are for your transactional marketing.
So this would be or sorry. Our transactional email. So this would be email that's being sent directly from the product to a customer.
Imagine, like, a password reset as an example. And then you wanna have your business, you know, domain email, which is what your team actually uses to run the company, etcetera. Um, so with HyperTide as an example, um, we we have a partnership with them, so it's a little bit different.
But, uh, we can send about 10,000 cold emails just to give a a, you know, kind of the cost breakdown here. We can send about 10,000 cold emails with them for about a $100 a month in infrastructure costs on the inbox side.
It's about the same for majority of these. So inbox kit as an example is very similar pricing. They also run, like, sales all the time.
So look for those on the domain side. So you basically buy the domains, and then you're paying a subscription to have these inboxes hosted for you. And then on Instantly side, um, you can typically get started with this $97 a month tier.
So in total, you know, out the door to get going on this, the infrastructure cost can be in that range of about $100 to get started or sorry. About $200 to get started for the sending, uh, software and then also the inboxes. So, again, just to reiterate this because I know I've talked through a lot.
I'm pulling the lead list from LinkedIn. I'm finding these people. How do I know that these are people that I wanna reach out to?
It's because they're engaging with content that I know my target customer would be interested in. And so these people are basically hand raising that they are would potentially be my target customer.
Which is insane, by the way. Right?
Which is insane. Because find this. Right?
Yeah. Yeah. It's impossible to find this.
And so the so I'm finding these people. I'm then doing a waterfall enrichment to find all of their contact information.
And then once I have their contact information, I need to actually be able to send to them. So I'm getting inbox infrastructure to be able to send, and then I'm sending with a platform, like, instantly.
And then on the LinkedIn DM side, what I'm sending with is a platform, um, like HeyReach.
Another one that we like is called BotDog. Um, both of these have APIs. Um, but what these enable you to do is basically, uh, do LinkedIn DM campaigns, um, from these accounts.
I also know people that are just, like, using LinkedIn DM sorry. LinkedIn InMail for this and seeing incredible success right now, uh, using this strategy.
So, yeah, just throwing out all the strategies that are available. Um, so this is how you can build this pipeline.
Right? Now how do you actually, like, have an agent that is managing that inbox? So looking at Instantly as an example, they have an API, and that API allows for you to monitor and manage the entire account.
So you can have an agent that's literally writing copy for each individual email or person that you're contacting or reaching out to, um, and writing those variables, and then that can be basically pushed into instantly. So this happens outside the platform, gets pushed in.
But the bigger thing here is they also have webhooks. So when a positive reply happens, you can send that webhook confirmation back to your agent that's hosted on some type of cloud server.
And that agent, you give it basically, um, like, base prompt, right, of, like, your the goal like, here's all the context that you need, and your goal is to try to get people to schedule demos on, you know, this this link. Right? It can manage that inbox, answer questions, push people deeper.
But the thing that gets really fascinating and really powerful with this g is, like, it can do these follow ups, like, months later.
Right? So it's like, okay. Like, also, like, every six months, right, I wanna program it program that in to, like, re re reach out to these people that went cold.
I can also plug it into my scheduling application, like Calendly or, like, cal.com. I can give the agent access to see, okay. Did this person that we reached out to, can we did they actually schedule a discovery call?
Did they actually, you know, produce the action that we're or, you know, make the action that we're trying to optimize for? And so from this, you can basically build this, like, SDR in a box.
Right? That is, again, finding new people for you based off of the engagements that they're interacting with on social, finding the emails, actually writing the emails, deciding if this is a good ICP fit, and then sending that to the sending platforms and then managing the inboxes of those sending platforms.
And, again, when I say agent, right, like, when I'm saying, oh, it's managing this inbox, it's literally just code under the hood. Right? It's code under the hood with an LLM attached.
That is an agent, like, in this context here. You don't have to overcomplicate this. You don't have to have God in a box managing an email inbox.
It'd be a very simple setup to actually prove produce this. I also get asked this question a lot. Like, do you need use, like, some agent framework under the hood?
It's like, a lot of the times you don't need it. It's just bloat. You can just have a very simple, like, a very simple solution for these finite problems.
Right? It doesn't have to be this overcomplicated or overengineered thing. So, anyways, happy to answer any questions about that or dive deeper on any of this.
Again, it's hard to show code, and so I I didn't really do that today of, like, this is how you do it. But what you need here, basically, the final piece is you need to set up a server. So use something like a railway or this is what we do at, like, graft.
Right? It's like we have the data pipeline warehouse and then the server to deploy these agents to that's, like, off of the live data streams. But, yeah, happy to answer questions, Deep.
I mean, to be clear, you're,
you know, you're using a harness like ClaudeCode or Codex to actually build out all of the thing. But this the hard part is the strategy around, you know, who you're going after, why you're going after them, what's your tool stack that like, what's amazing is you just, like, outlined, here's all the tools that you need to get, like, set up.
Then it becomes, okay, I have to go into you know, that's what people are talking about, software factories.
Like, we're all in the software factory business now. Right? Because we're just going and we're spitting up stuff like this, the software, to actually go and complete these tasks.
Absolutely. I think the thing that we are, like, focusing on like, it's so to say like, a a good way to think about this is, like, if you can build it in Cloud Code and, like, have some type of local system that you're running, you can probably deploy that to a server somewhere, right, and have that run on an hourly cadence or a daily cadence or whatever that ends up looking like.
The challenge ends up being, how do I set up the infrastructure that's necessary for the agent to be able to do this? Right? And the the the solution is, like the open source solution as an example.
Like, we talked about this on the last call. Use something like Arabyte, uh, with Clickhouse to get, like, create your data pipeline and your data warehouse so you have that data stream for the agent to make those decisions.
And then you have to have some server. And, like, when I say server, what do you what is that? Right?
For the uninitiated, it's just a computer that is on all the time somewhere else that you're putting code onto.
Right? I think the software factory thing is super fascinating as well. Like like, really, it's funny.
This is how I'm thinking about marketing now. Like, marketing is just code. Like like, when I generate an image, like, that's just a JSON prompt under the hood.
Like, when I make, like, you know, Seedance AI avatar videos, that's just like an LLM that, like, scraped Reddit, like, read some things, wrote a script, and then we it's just an API call that's happening to Kai AI to generate that image with, like, okay.
Here's how you chain this together to make it into thirty seconds. Every everything now, like, in in my cofounder, this is his firm belief. Like, Max always says this.
He's basically like, the only agent is a coding agent, actually. Everything else is just software that's being made by the coding agent.
I think this is, like, this paradigm shift, only and something that we are obsessed with. Like, why are you paying tokens for things that can be just code that is running on super cheap compute?
You don't you don't have to have, like, inference every time that you're doing this action. Only use inference when you need it. And this is kind of this, like, differing viewpoint that I think, you know, everybody's just like, oh, token abundance.
I'm gonna token max. I'm like, I'm actually totally, like, probably the opposite of that. Like, why?
It just is wasteful. Like, do the thing that is the simpler thing that has less likelihood of breaking. Like, if you have Hermes trying to run your Facebook ads, high likelihood it might just, like, absolutely nuke the account.
But if you have it run based off you you build a a piece of custom software for yourself that's running based off of a system that a normal hue like, a real human would run, Totally different, you know, outcomes that you're gonna get from that that are probably higher quality. So
Okay. Do we have time for a second marketing agent demo flow?
Yeah. I can talk through I just did this I just did this for, uh, my team.
Um, I I don't know if that'll be super interesting, actually. I mean, you tell me. We basically were like, k.
How do we, uh, at scale, make social content on LinkedIn for, like, the entire team? And, like so we have them basically, we're interviewing them. We take the transcripts.
We pull out the insights. The insights get written into posts. The posts automatically get scheduled to their LinkedIn accounts using a tool called Ordinal
MCP. Yes. Stop.
Like, yes. This is interesting because a lot of people I mean, lot of people might have heard you know, listen to this cold cold email approach or cold reach out approach and are like,
I wanna go the organic route. So, like, what's what's an example of setting up a marketing agent in an organic route, and and can you break that down for us? Absolutely.
Yeah. I'll do the LinkedIn one because it's super topical, and, like, we've had a lot of interest in this lately by companies, which has been pretty fascinating. They're using this with, like, their sales teams.
Like, they want, you know, their seven person sales team to be posting daily. How do they actually do that and make unique ideas? So this also pairs with the cold email.
I'll talk about that as well. But, yeah, just to run through the process, super simple. It's like, literally record a conversation like this.
Like, I have a a a weekly call, like, one on one with, like, the people that we're doing this for in the org. And I'm just like, tell me everything that, like, you've learned in the last week.
I just basically interview them, have a conversation. Right? It doesn't have to be anything.
Like, you don't have to have any focus. It's just like, what are the things that that jumped out at you after being in these sales calls or whatever your job is? You can do this for, like, technical people as well at the organization.
You can do this for everybody. And I imagine this is how the, like, the real companies are doing this. There's no way that, like, everybody at, like, a lovable is running the content that's going out across all of the accounts.
Maybe that's happening. But I think what's more likely is that there's somebody behind the scenes that's orchestrating this.
That also doesn't have to be an interview. It can just be sales calls or internal comms. Like, Alex Lieberman, as an example, has been talking about about this a lot where they're they're basically sourcing like, so much context is happening within their Notion, within their code base, within their their Slack.
We see this as well. Right? You can use one of these agents to query those data sources.
Right? Like, query the sales channel or query the Gong transcripts, and that's where you can pull these insights from.
And, honestly, a lot of the times, you find that it's really ins like, it's really good content that's trapped in there. Like, these ideas, like, for example, a customer had, uh, you know, a customer said that or a potential customer said this, and it was, like, why they didn't buy the product. And that can turn into an unbelievable piece of content, um, that you can extract from.
So you get source material. Why do you have to get source material? The reason is because if you go and you try to just have the agent, like, think about this, you're like, write good LinkedIn content.
It's gonna be the most mid thing you I mean, it's you're gonna waste the person's time on the other side. Right? Um, or you're gonna get flagged for AI slop by LinkedIn's new feature that just released this morning.
The the better way to do this is source this from real human conversation because that's where these original ideas are coming from. Another example of this is, like, literally this podcast.
You could extract all the insights from the transcript, and that can be used as social content. And this is like a strategy I use for myself, but it doesn't have to be just your own. It can be somebody else's as well.
It can be, you know, a podcast with Naval. It can be whatever. It can the source material can be anything.
But the system that you create is some type of source material that's happening on, you know, some type of cadence. And then from that, I'm I'm building basically this writing and scheduling process. So what I I'll walk through now how to actually, like, do this.
Um, so take that source material. You're gonna do an API call, um, into, uh, you know, some LLM.
As an example, uh, for this, like, you could I mean, we've even used just, like, uh, Claude Sonnet as an example, and it's probably good enough on the writing side. Um, and then once you have that those written posts, you're then going to go and use scheduling tool. We like Ordinal for this.
Um, they're a partner of ours as well. Um, but it allows for you to have multiple LinkedIn accounts connected to it, and then they can also interact with each other, which is amazing. But you can, through their API or their MCP, schedule these posts to each of the individual accounts.
And then Ordinal also has and I could just go into this to actually show you. Ordinal also has the analytics data that pulls in from your LinkedIn post there as well.
So we can see the breakdown of, like, which content is actually performing well. So it has the analytics of the multiple accounts. You can actually see the breakdown of the individual posts, and that data stream can go back to the agent so that it understands, okay.
This is what's getting impressions. This is what's doing well. Let's go do more content, like when it does its cycles of writing.
That can influence the next round of creative. So topics like this perform better based off of the source material we pulled. How can we snowball or remix?
Use those specific words, snowball or remix, to have it go further.
Right? And this is where the LLM is thinking on top of that data stream. And when you look at, like, what is happening here, like, what does the social media manager do?
I actually think the social media manager job, like, full stop, is it's I think it's already dead, but let's I won't get into that if you're listening to this. Please learn how to make and manage content at scale across multiple accounts.
It's with agents because that's gonna be I think that's the real meta now is, like, how can a single person manage, you know, ten, twenty, a 100 accounts across all of these different channels.
But when you look at what a social media manager did previously, like a good one, that was actually excellent excellent at their job, is they would prospect for ideas. They would make content about those ideas. They would publish it.
They would look at the data to see which got the most impressions, and then they would turn that into a recurring content calendar where they're like, okay. I'm just remixing this these same ideas over and over again.
If you look at my Twitter, like, post as an example or even my LinkedIn, it is the exact same thing remixed every 90.
Like, full stop. That is all that's happening. And that when when you get enough information, like a bigger enough corpus, you have you basically understand what's already gonna go viral.
Like, I I have these posts that I've literally used for the last two years. Every time I post it, I know it's gonna go viral. I can't post it every day.
You post it every ninety days. Right? And that's how you can go back into this cadence.
And so, again, have this mentality of I'm prospecting for ideas. I'm prospecting for winners.
Once I find those, I'm trying to use those as as often as I can because I know that that's what's going to work. That is what the audience is resonating with.
And this is this applies to product as well. Right? Like, when I think that a lot of first time founders, they they spend time thinking about, like, I'm trying to get the market to buy this.
And in reality, it's like, I'm the the the pros at this is like, what does the market wanna buy? Can I build it, and can I sell it to them? Right?
Like, that is actually how you start a business. And if for some reason, it's this this flip thing where they're like, oh, I'm trying to invent a new idea. I don't wanna invent a new idea at all.
I wanna be like, what do people want to buy that currently, like, they can't buy? And can I go and figure out this the way to build that thing?
And then I know I can sell that back to my notes. The market is going to be receptive to it. And you need to think about content in the same way where, like, what is the content that the market is currently receptive to?
And by mining that content from other sources that has already had a viral moment, this is a way to leapfrog that to identify that. Then you're going and you're putting your own spin. You're putting your own, you know, angle on this.
So, anyway,
a lot of thoughts there. Agreed on the social media manager is, like, that role is dead, or it's evolved.
It's gonna evolve. Like, it's gonna evolve into the social media agent man manager. So you're going to need to be able to spin up agents so that you can create a bunch of accounts on the fly that systematically creates content like you have.
Like, you get millions of impressions a month, free impressions. Actually We get platforms paid are paying you, which is insane
to do it. It's insane. And I get paid to build lead pipeline.
Like, think about it. It's crazy. And, like, I I it's so funny, man.
I'll talk to, like, founders or, like, you know, large like, people that that run bigger companies, and they'll they'll be like, why are you why would you would you invest in social?
And I'm like, look at the earned media. Like, if you were paying for those impressions on platform, for example, on LinkedIn, it's like $22 per thousand impressions is the average.
Right? It's like every post that you get, even with an account that's like 500 followers, you can get a thousand impressions. That's like $20 that you just, like, put into your pocket for free.
Yeah. Right? But it's it's there's the earned media side, and then there's also, like, the platforms pay you.
Like, YouTube literally pays you to do marketing
for, like, checkout. Like, what the what the hell? It's crazy.
It's crazy. And then, you know, for the people who are like, well, I don't wanna do a personal brand, makes sense. What Cody is suggesting is, like, have people on your team have these personal brands.
And if you don't want and by the way, I'll give you a piece of sauce. If you don't wanna do that, another really smart thing to do with agents creating content for you is creating theme based pages or topic based pages.
So for example, my good friend, Julian Shapiro,
you know, he had a company, a growth agency called Demand Curve. Absolutely goat, by the way. His blog is incredible, and He's the guy.
That's what I came up on. So I'm just like, one of the actually grew up with Julian. No.
Did you really? That's amazing. Yeah.
He was like my name. He owns like a farm now or something. Right?
Yeah. That's awesome. So
I need to get him on the pod, but Julian, being the smart guy he is, it's not like he created a x account that was slash demand curve.
I mean, maybe he has that, but he actually created an x account called at GrowthTactics. So he's creating content on this GrowthTactics page.
People interested in growth tactics follow it, and then they learn about his agency
and his products through that. Right? That's Exactly.
Media company. And, like, again, it doesn't could be I mean, there's the ones that are my favorite are, like, Chase Passive Income. I don't know if you've seen this.
Yeah. Um, they're doing it more as a meme page, but, like, you can use, like, this attention that you can garner for free as a way to drive inbound for whatever whatever it is that you're building. It doesn't have to just be you.
It can be this, like, anonymous thing that is still providing value that you're aggregating and, you know, organizing for the Internet. Right? So I'll leave it there.
I don't know if there's any other questions. Really,
you know, impactful marketing agents that you just broke down. I wish we had forty hours together and we did like a crazy
Comment below. That's the only way I come back. That's the only way he'll have me.
Alright? So you have to do this. You have to comment what you wanna learn.
I'll tell you I'll I'll teach you whatever you want. It can be how to build social media agents, like, for TikTok clouds. It can be, like, how do I actually run a paid ads account?
It can be anything that you can imagine. It can be direct mail.
I'll literally walk you through how can you send direct mail at scale by scraping Google Maps. You name it. How do you advertise on TV, and what's the meta there?
Uh, like, how do you get cheaper clicks on LinkedIn?
I I can break down any of that. So I appreciate you, Cody. We'll see you in the comments section.
Like always, I'll include links for where to follow Cody on the Internet, in the show notes, in the description. Can I shout it out to you? Give me give me the opportunity.
Go for it. Hell, yeah.
Go find me on Twitter, LinkedIn. That's where I'm the most active. And if you want to deploy these exact agents that I talked about today, go to graph.com.
Uh, we have both the platform solution for this and also we forward deploy software engineers to do these actual implementations on our platform. We would love to help you.
If you're a fast growing company, that is who we're seeing the most success with. So thanks for having me, g. God bless you, Cody.
I'll see you next time.
The Hook
The bait, then the rug-pull.
Greg Isenberg opens with a claim built to reframe how you think about growth software: marketing agents are the new coding agents. What follows is Cody Schneider building two of them live on screen — a cold-outbound machine sourced from LinkedIn hand-raises, and an organic content engine mined from real conversations — naming every tool in the stack along the way.
Cody Schneider maps the exact infrastructure — pipeline, warehouse, agent — behind a Facebook ads system that researches, creates, publishes, and kills its own losing ads.
Greg Isenberg gets a live, screen-shared tour of Jack Dorsey's new agent-native chat app from an early user — and presses him on whether it actually beats Slack.
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