“200x faster.” “400x cheaper.”

You’ve probably seen it all over your feed lately.

3 posts in, and you still don’t know what the thing actually does.

So let me explain it the way I’d explain it over a coffee.

🪨 Why it’s called Jev

Jev is named after William Stanley Jevons.

In the 1860s, he noticed that when steam engines got more efficient, people burned more coal instead of less.

TypeSafe bets AI works the same way.

Make a decision almost free, and you’ll make thousands more.

In GTM, that means scoring every company in your market instead of a shortlist.

They call it a “System One” model, after Kahneman’s Thinking, Fast and Slow: the quick gut call, not the slow careful thinking.

If someone who knows your market could decide it in seconds, it’s Jev’s job.

🤖 So, what does Jev do?

Jev is a new AI model from a company called TypeSafe AI.

It came out on 15 September. The founder, Diogo Almeida, helped build the research behind ChatGPT, which is partly why everyone’s paying attention.

Here’s the big difference:

You hand Jev some info and a question with fixed answers.

It picks one, and tells you how sure it is. For example:

“Does this company fit my ideal customer? Yes. 94% sure.”

That’s it. No emails or summaries, not even a sentence.

What you get in return:

• It’s fast: answers come back in well under a second

• It’s cheap: $42 per billion tokens going in, and the answers cost nothing

• It tells you how sure it is, every single time

Writers and judges

Look at everything you ask AI to do in your GTM, and it splits into two jobs:

• Writers: the output is words. Emails, call recaps, proposals. Claude is great here, and nothing changes

• Judges: the output is a decision. Is this lead a fit? Is this reply interested? Which segment does this company belong to?

We’ve been giving both jobs to the same model.

So, when you ask Claude to score a lead, you pay for a whole paragraph of reasoning just to get one number out of it.

Jev only does the judging.

The three questions Jev can answer

1. Pick one: choose from a lead list you set, up to 255 options. “Which segment is this?”

2. Score it: place it on a scale you describe, 2 to 10 levels. “How well does this match my ideal customer?”

3. Yes or no: with a probability. “Is this an agency?”

And it can only answer with the options you gave it. It can’t invent a segment you never asked for.

So does it replace Claude?

No. Claude can do everything Jev does.

Jev just does the sorting differently:

• Faster: under a second, where Claude takes a few

• Cheaper: you pay for what goes in, never for an explanation coming out

• Consistent: same question, same answer, every run

• Honest about doubt: a confidence score on every answer

But Jev can’t research, write or reason. So you’d use both: Claude to think and write, Jev to sort.

At 500 to 5,000 contacts a month, Claude handles your list fine.

Jev pays off when volume grows, answers need to be instant, or the same lead must get the same score next week.

💸 OK, but is it worth it?

1. Cost

Scoring 5,000 contacts costs about 21 cents.

Claude Code won’t add to your bill either, it just uses some of your plan’s limits.

Either way, your review time costs more than any model.

2. Results

Early tests are mixed:

• Fedor Kovalev (Founder of Cheetah) tested 83 contacts. Jev matched the reference answers (set by other AI models) 77% of the time, for half a cent. But it missed 40 to 56% of the good contacts

• Thomas George (Founder-CEO of Housecat) sorted 100 emails. Jev slightly beat OpenAI’s Codex Mini

• Vercel got results five to 18 times faster, and more accurate

Good at sorting, but it can drop good leads.

3. Setup

Under an hour. Jev is on OpenRouter, so one API key gets you in today, no waitlist.

My take: worth testing now. Let it sort, and keep the final call yourself.

The confidence score is the prize: it shows which calls to trust, so you only check the shaky ones.

Below, I’ll show you how to run it on your own list.

What’s inside:

• The Jev skill for Claude Code, ready to download

• How to write your ideal customer so Jev scores it properly, including the one mistake that trips it up

• The confidence rules that tell you what to trust, what to check, and what to ignore

🛠️ Score your list with Jev, in under 1 hour

Read more

You post three times a week. 5 likes, mostly from peers.

Or you send connection requests every day.

More than half get ignored, because nobody knows who you are.

Two different problems, with the same cause.

You’re flying on one engine and wondering why you can’t get off the ground.

✈️ Two engines

Think of LinkedIn like a plane.

It needs traction to take off, and it has two engines to get it:

• The inbound engine: people see your posts, check your profile, and come to you

• The outbound engine: you find the right people and start the conversation yourself

Run one engine and you’ll crawl down the runway.

Run both and each one feeds the other, until the whole thing lifts off.

📥 How the inbound engine powers outbound

When your request lands, the other person looks at your name, your face and your headline.

They decide in about 2 seconds.

If they’ve seen your posts, your name rings a bell. That familiar feeling gets requests accepted and DMs answered.

Your posts also sit on your profile. When a prospect checks you out before replying, they find proof instead of a sales pitch.

📤 How the outbound engine powers inbound

Every buyer you connect with starts seeing your posts. So outbound builds the audience you want: buyers, not only peers.

Then the DMs push it further: LinkedIn’s feed favours people you interact with.

Start a conversation with a buyer, and your next posts show up in their feed more often.

So every DM warms up your content, and your content warms up your next DM.

And every conversation tells you what your buyers care about: Their questions, their objections, and their words.

That’s your next post, written by your market.

🎟️ Want the inbound engine running properly?

The Reply io team is running a free livestream this Thursday.

They’ll cover:

• How to grow your presence on LinkedIn

• How to post in a way that pulls your audience in

• How to turn that audience into warm leads.

Save your spot

You’ll also walk away with:

• The hooks that got Reply io 3M+ impressions on LinkedIn, with why each one worked

• A cheat sheet for never running out of post ideas

• The checklist the Reply team runs before every post

And the outbound engine?

That’s what I’ve been building on Founder’s GTM. The full system, in the order you’d set it up:

1. Find buyers showing intent.

2. Get your requests accepted.

3. Turn connections into replies.

4. Turn replies into meetings.

Each one comes with the Claude prompts to run it.

BONUS: Turn your DMs into posts

Here’s where the two engines connect.

First, get your conversations. There are two ways:

• Quick: open your LinkedIn inbox with Claude in Chrome and let it read your recent threads

• Complete: export your data from LinkedIn (Settings → Data privacy → Get a copy of your data → Messages), then upload the file to Claude

Then run this prompt:

Your buyers wrote those posts already. You’re just publishing them.

Takeaway

Posting alone builds an audience slowly.

Outbound alone knocks on the doors of people who’ve never heard of you.

Two engines, and each one feeds the other.

• Posts make your name familiar before the request lands.

• Conversations put your posts in front of buyers, and hand you the posts worth writing.

That’s the traction that gets the plane off the ground.

Jasper

PS: The outbound editions above are for Premium subscribers. Upgrade to get full access + every Claude GTM skill and advanced GTM guide on top.

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Last Wednesday, I joined Eugene Kadzin on the Instantly Daily Show.

The format: 10 minutes of news, a chat, then 10 minutes where the guest shares their screen and teaches something GTM-related.

I demoed exactly how the YYY email writing skill works. From scratch. Live, with nothing to buy.

Check out this article to understand how it works and get the skill.

What we did

Three steps, end to end:

1. Build a Claude skill that holds my YYY email framework, so the output repeats instead of changing with every prompt.

2. Open a LinkedIn profile and run the skill on it through Claude in Chrome.

3. Get a first-touch email built on what’s on that profile.

When the emails came out, Eugene laughed nervously:

“Hopefully I’m not gonna get such a cold email, because I will just be buying.”

That’s the bar. A cold email the person running a cold email show would reply to.

🧠 Why a SKILL, and not a prompt

A prompt gives you one answer. Next time you type it slightly differently, and you get a different email.

A skill stores the rules. The framework, the word limits, the banned phrases, what to do when research finds nothing.

It runs the same way on prospect one and prospect fifty.

🎬 Check out the full episode here

How to run this skill at SCALE, in minutes (200 people list example)

Then Eugene asked the obvious question:

“One prospect at a time works. How do you do it for 200?”

The skill writes great emails one by one.

It even pushes back when you ask for 200, and it’s right to.

Writing 200 emails and running them as a campaign are two different jobs.

The second part of this article is about the second job:

The full playbook to go from one profile to a 200-prospect email campaign, without losing what made the first email good.

Inside:

• How to connect your email tool to Claude Code via MCP server.

• The batch prompt that writes 200 emails and holds back the weak ones

• The system that builds the whole campaign in your email tool paused, ready for your final look

Read more

In March, I asked the $5 billion question in this newsletter.

Is Clay worth it when Claude Code can reach the same data?

Two weeks ago, Clay raised $115 million. At $7.1 billion.

So let me own this one.

I framed it as Claude Code versus Clay. That framing was wrong, and Clay is the reason I know.

⚰️ The funeral everyone planned

The argument went like this, and I made a version of it:

• Data gets cheaper every month. Anyone can call Apollo or an enrichment API directly.

• Orchestration is what AI agents are built for.

• So Clay loses both halves of what it sells.

But Clay saw the same thing coming. Then it made three moves:

1. It cuts the price of data

On 11 March, Clay rebuilds its pricing. Billing splits in two:

• Data Credits for enrichment

• Actions for workflow work.

Then data gets cheaper. Clay’s own pricing page says the most-used enrichments now cost 50% fewer credits on average.

The top self-serve plan drops from $800 a month to $495.

Data was heading that way anyway. Clay gets there first, and moves its price to running the workflow, which stays hard.

2. It lets the agents call it

Clay ships an MCP server. A rep can now use Clay’s tools from inside Claude, without opening Clay at all.

The thing that was supposed to replace Clay now sends it work.

3. It builds its own agents

The new money goes into agents that find customers, research companies and set up outreach.

Clay stops waiting for someone else’s agent to route around it.

The detail I like most

Clay’s own announcement lists its customers: Anthropic, Google, OpenAI, Stripe.

And SpaceXAI. The company behind Grok Bot, the agent everyone says is coming for orchestration.

How to see it now

Every GTM stack does three jobs:

1. Data is a commodity. Shop around for it.

2. Workflow is where tools fight, and Clay fights hard for it.

3. Judgment belongs to you: who you target, why now, what you say.

🧩 Where Claude Code fits

For one person, Claude Code covers the last two jobs.

1. It holds your judgment in a file you own, your CLAUDE.md.

2. Then it runs the workflow. It researches accounts, decides who fits, and drafts the email.

What it lacks is data of its own. So it calls data tools through MCP:

• MoltSets sells contact data on a flat plan from $27 a month, with no per-lookup credits. It comes from the founder of RB2B, and it’s still in closed beta.

• Deepline connects Claude Code to dozens of data providers. Bring your own keys and it charges a $0 platform fee and 0% markup.

• Apollo, Prospeo and other lead enrichment tools plug in the same way.

Here’s what the “versus” framing missed:

You never have to choose on day one.

Do you really need Clay?

Clay is built for big teams. You’re probably not one yet.

So the answer depends less on Clay, and more on how you run outbound today.

Two or three facts about your setup settle it:

Use Claude Code with a direct data source if:

• One or two people run your outbound

• You want your ICP and rules in a file you own

• Research quality matters more to you than volume

• You’re happy to connect your sequencer through MCP, like Smartlead or Instantly, so leads go straight in

Add Clay when:

• Several sales reps run enrichment, and some of them will never open Claude Code

• Your team needs one shared, visual workspace everyone can see and edit

• You’d rather manage dozens of data providers under one account and one bill than juggle your own keys

• You want ready-made templates your team reuses without writing a skill

Even Deepline, which competes with Clay, says as much:

Running a founder-led team at $1-10M? You probably don’t need Clay yet.

Start with Claude Code and one direct data source.

Add Clay the day a second person needs to run enrichment without touching Claude Code.

Since Clay speaks MCP, you keep everything you’ve built.

💶 What each setup costs

List prices, September 2026. Ranges run from annual to monthly billing.

Clay rows exclude Claude. Add $17–20 a month if you still use it for research.

Claude plus MoltSets costs about a tenth of Clay Growth.

Clay Launch alone costs roughly four times as much. Enterprise lands near sixty times.

This isn’t a like-for-like comparison, though. Here’s what the gap buys:

• Clay gives you a shared visual workspace and pushes leads into your sequencer

• MoltSets charges extra for verified mobile numbers, and it’s still in closed beta

• Apollo credits run out, and heavy users pay more

• Clay top-ups cost 30% more than your plan rate

For a founding team sending a few hundred emails a month, the extra doesn’t buy much.

For a team of five running enrichment every day, it can.

Takeaway

Data got cheap for everyone.

Clay saw it coming, moved its price to the workflow, opened its doors to agents, and doubled its valuation on the way.

For big teams, that earns its place.

For a founding team running outbound, Claude Code with one direct data source does the job for a tenth of the money.

Either way, the same thing decides whether it works: who you reach out to, and why now.

Keep that in a file you own, and plug Clay in the day you outgrow it.

Jasper

PS: Want the Claude Code setup built for you step-by-step?

The Lead Enricher gets you a verified list with emails in minutes, then turns your whole list into scored, ready-to-send leads with 1 command. Updated for Q4 2026. For Premium subscribers.

Upgrade here to get full access.

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“Cold email doesn’t work”. 0 replies to your email campaign.

You rewrite the subject line three times. You make it shorter, then longer.

You run it through Claude to sound less salesy. But nothing really moves.

So now you’re working out whether to kill the campaign or just pay someone to run it for you.

Reply rate is one of 3 numbers to track. But it’s also the one that lies the most.

📐 What YYY is, in 30 seconds

There are 3 questions every single prospect asks themselves, answered in order, in one email:

1. Why ME? A dated event at their company, and the problem it creates. This is called relevance.

2. Why YOU? How you solve it. This is called credibility.

3. Why ACT? One thing they’d want even if they never buy from you. This is called a compelling offer.

That’s it. I wrote the full breakdown here:

The Claude skill in this article runs that framework against real prospects.

📊 The stat that explains it all

LeadHaste’s 2026 outbound benchmarks pull data from 20+ primary sources: Instantly, Gong, Cognism, RAIN Group, Expandi.

One line in there matters more than all the others: Only 5% of cold email senders personalise every email.

Which is strange, because the same report shows what personalisation does:

• Highly personalised campaigns get 142% higher reply rates than generic

• Prospect-specific openers and company research hit 18% reply rate, against 9% for generic templates

• Top reps get 4.2x more replies than the average rep

So the biggest lever sits untouched by 95% of people sending cold email.

Not so much because it doesn’t work. The problem is time: it takes 10 minutes a prospect, and 300 prospects is 50 hours.

But reply rate isn’t what pays. Here’s the full funnel:

Email-to-meeting sits at 0.5% to 2% for typical teams. Top performers manage 2% to 2.34%.

At average rates, that’s 117 emails per booked meeting.

🔱 3 ways to send, and what each costs you

1. The SDR way

A sales rep pulls a list, writes a few templates, and sends. Research happens by hand, if at all.

When they do find a signal, they found it from vendors like LinkedIn or Zoominfo, from a one search or one company at a time.

AI shows up at the end, drafting the emails.

While that same AI could be reading 100 companies and working out which have a problem right now.

10 minutes per prospect, done properly.

300 prospects is 50 hours. Nobody has 50 hours, so the real research stops happening.

That’s the 95%. Most sales teams still operate the SDR way.

2. The AI SDR way

Fully automated.

Sold as replacing your entire SDR team. We now know that promise didn’t hold.

What works is a leaner team with one GTM engineer/operator using AI well, and that’s where the market has landed.

An AI SDR fires at volume.

It knows a form you filled in and a firmographic filter. But it has never seen your closed deals.

0 minutes per prospect, and AI-personalisation sit at 9% reply against 18% for researched ones.

Vendors often hide the price, which tells you something.

11x’s own page says Alice starts at $36,000 a year, and third parties put year one closer to $50,000 once implementation lands.

Call it $20,000 to $60,000 a year, on an annual contract, in a category with 50 to 70% churn.

For the half of the reply rate you’re leaving behind.

3. The YYY way

Semi-automated.

The YYY Claude skill interviews you once, 2 minutes.

Then it saves your offer, your buyer, your proof points and your disqualifiers.

Then it researches every single prospect personally. Profile per profile or an entire list if you like.

Not a personalised first line bolted onto a template, but a fresh dated trigger for each one, and 3 complete emails written around it.

4 to 6 searches, 3 emails drafted, 1 honest confidence grade.

Roughly 2 minutes per prospect instead of 10.

300 prospects goes from 50 hours to 10. And you read every one before it sends.

It’s not from another vendor’s template or an article about cold email.

The framework behind it comes from a decade of outbound work and reviewing thousands of campaigns.

📦 What you get today

The full YYY email writer skill, ready to plug into Claude and launch in Claude for Chrome.

Type one line and it runs everything: it interviews you, researches the prospect, does the writing, + an honest verdict on whether the prospect is really worth emailing.

⚙️ Installing the skill

Get the skill file from the link below and add it to your Claude account like any other skill.

Read more

0.2%. That’s the meeting rate inside a normal, well-run outbound team.

In one case, 15 salespeople with good process, covering the whole country.

2 meetings for every thousand people they contacted.

Now here’s what a founder gets, working alone, no team, no tools, squeezing outbound in between everything else: 0.5% to 1%.

That’s 2.5 to 5 times better than the professionals.

Subscribe now

We see this from opposite ends.

• works with founders starting outbound, one to ten million in revenue.

• works with growth teams further along, ten and twenty million and up.

Doug: “I get them after Jasper’s done with them, a few years later.”

Same companies, different years. The number keeps falling as they grow.

The question very few can answer

Ask a founder how many meetings outbound booked last month and you’ll get an open rate back.

Jasper:“Usually there is no answer to the question, so then we know where we are.”

Some weeks it’s LinkedIn, some weeks nothing.

The email tool shows opens and replies, so opens and replies become the scoreboard.

Ask a growth team and you get the same problem wearing better clothes.

Dashboards everywhere: CAC, pipeline, reply rate, sequence completion.

Doug’s sat in reviews where the reply rate looked great and nobody could say how many meetings it produced.

Why that one:

• Opens tell you your subject line or headline worked

• Replies tell you someone was curious, and curiosity is free

• A meeting costs them something, which is why it’s the first honest sign they care

Doug’s written the longer version: why everyone tracks activity instead of need.

Cannonball GTM

The 5% problem

No one wants to get on a call with you, just like no one wants to go to the dentist or negotiate with a smarmy car salesman. It’s just too painful. Yet, people still buy cars. People still go to the dentist…

Read more

5 months ago · 6 likes · Cannonball GTM and Doug Bell

Two doors, one question:

Founder? Can you say how many meetings outbound made last month?

Running a team? What’s your rate, and what should it be?

Which brings up the obvious thing. What should it be?

🪜 The ladder

Here are 4 numbers from our combined client work.

• 0.2% - the baseline. Good team, real effort, generic list. Where outbound usually lives, and where teams rarely check.

• 0.5% - shows up in two places that have nothing in common: a well-managed sales team, or a founder working his own network by hand. Hold that thought.

• 1% - same team, same message, pointed at a better slice of the market.

• 4% - when the list comes from real pain signals and the message is something the reader would want anyway. That’s Doug’s specialty, and the basis for Jasper’s email framework.

  Cannonball GTM

  Start Here: The Beginner's Guide to Cannonball GTM

  You Found This for a Reason…

  Read more

  a year ago · 21 likes · 2 comments · Cannonball GTM

📉 Why it drops as you grow

Go back to that second rung. A founder with nothing and a fully staffed team book at the same rate.

His edge was never talent, but closeness.

His list is people he already knows: Old colleagues, investor intros, people who reply to his texts.

10 customers, all of them by name, so every email starts from a problem he’s watched someone actually have.

Small volume forces sharp aim.

Then the company grows and the aim slips. Nothing breaks all at once:

• The network runs out, so someone buys a list

• The handwritten email becomes a template

• New hires arrive who’ve never met a customer

Volume goes up. Precision leaves quietly.

Neither of us saw the whole curve from where we stood.

Jasper watched the fast start and assumed scale would improve it.

Doug watched teams sat at 0.2% and assumed it had always been that way.

It took both ends of the same companies to see one number falling for the same reason, at every stage.

6 times the meetings, same message

Back to those 15 salespeople from the introduction.

National coverage, calls and emails, one state at a time.

Rate: 0.2%.

Nothing was wrong with them. Good process, good managers, activity right on plan.

Instead of working the market alphabetically, the team got pointed at companies showing real signs of growth, where the problem was live this quarter rather than possible someday.

—> New rate: 1.2%. 6x the meetings, same payroll.

If you’re a founder: your early rate isn’t luck, but what closeness buys you, and the job as you grow is finding ways to stay close.

If you’re running a sales team: good people stuck at 0.2% don’t have an effort problem. They’re aimed at the wrong companies.

✅ What to do on Monday

If you’re a founder:

• Answer one question this week: how many meetings did outbound book last month, any channel?

• Can’t answer? That’s your answer.

• Start counting before you buy anything. A number you track beats a stack you don’t understand.

If you’re running a team:

• Track meetings booked ÷ people contacted, by segment, last 90 days

• Compare it to the ladder

• Sat at 0.2% with a good team? Leave the message alone and fix the list. It’s free, and it’s the biggest lever you’ve got.

Either way: before you automate anything, know who you’re reaching out to and why now.

Every $ you spend in this category is downstream of that answer.

This article came out of multiple conversations between Jasper Vanu and Doug Bell.

PS: The article tells you to fix the list. Premium shows you how to build one, with people who need you, right now. Join Premium here and get 35% OFF your first year.

Get 35% off for 1 year

Here are my 5 pieces from the past month, including:

• An agent that works while you sleep.

• A skill that runs in your browser.

• A model that writes your DMs.

But, none of these three changed the outcome.

Because that’s defined by what you fed them.

Here’s each one, and who it’s actually for.

1. Grok Bot vs Claude Code for GTM

SpaceXAI shipped an agent that signs into your tools and works while your laptop is shut.

One of the best outbound operators around says it replaced his daily Claude Code use.

THE FINDING: it worked for him because his team already knew what good outbound looks like.

The bot inherited a system that already worked.

Use this if you’re wondering whether to move your GTM work onto it.

The first job to give it, and the guardrail to install before it touches a prospect.

2. How To Find Buyers Nobody Else Is Looking For

A founder fixed his cold emails until they sounded exactly like him. Reply rate: 0.4%.

THE FINDING: the emails were fine, but they were landing with people who didn’t have the problem.

Same model, same voice, and the reply rate went to 7.8% once the list came from his own closed deals instead of a filter anyone can run.

Use this if you keep rewriting copy and nothing changes.

3. Booking Meetings on LinkedIn with Claude in Chrome

29 meetings from 2,174 connection requests.

About 12 a day, which every LinkedIn guru will tell you is too few.

THE FINDING: refining strategy took years. The Claude in Chrome side panel that now runs it is about 3 weeks old.

You can compress years into minutes. Use this if your LinkedIn outreach is stuck.

Five steps, plus the skill that runs them in your DMs.

4. You Are Using Too Many GTM Tools

Some twelve-step stacks run $3,000 a month and need a full-time engineer to keep working.

THE FINDING: complexity is profitable, for the people selling it to you.

Three categories cover the whole job, and the leanest tool that does each one is usually the right one.

Use this before your next renewal. There’s a prompt that finds what you’re paying for and not using.

5. The LinkedIn DM System That Gets 40%+ Replies

Real numbers from three campaigns, 20.8% to 45% replies.

The three-step sequence and the exact messages.

THE FINDING: silence after the first message isn't a no.

People give up after one, and the ones who don't get replies from prospects who ignored the opener entirely.

Use this if people accept your connection request and then the conversation dies.

BONUS: which one to use first

If you’re not sure where to start, run this.

It names one article and tells you what it fixes.

Help me work out which gap in my GTM setup matters most
right now, and which article I should read first.

## STEP 1: ASK ME
Ask me these five questions in one message. Short answers
are fine.

1. Am I running any agent or automation in my GTM work, or
   is it all manual?
2. When I write cold outreach, do I know what was happening
   inside that company before they'd want to hear from me,
   or am I working from job titles and company size?
3. Do connection requests get accepted and then the
   conversation dies?
4. Can I name every tool I pay for and say in one sentence
   why each one earns its place?
5. Do I know my cost per meeting on LinkedIn, requests sent
   divided by meetings booked?

## STEP 2: NAME THE ONE GAP
Tell me which single area is weakest, in one sentence.
Be direct. Don't list three.

## STEP 3: SEND ME TO THE RIGHT PIECE
- Weak on 1 → "Grok Bot vs Claude Code for GTM"
- Weak on 2 → "How To Find Buyers Nobody Else Is Looking For"
- Weak on 3 → "The LinkedIn DM System That Gets 40%+ Replies"
- Weak on 4 → "You Are Using Too Many GTM Tools"
- Weak on 5 → "Booking Meetings on LinkedIn with Claude in
  Chrome"

Give me the title and one line on what it fixes.

Takeaway

Five different pieces, five different tools, and the same thing decided every outcome: what went in before the tool did anything.

• The bot that doubled a client’s leads worked because the team already knew what good outbound looks like.

• The cold emails that sounded perfect still failed, until the list changed.

• The LinkedIn system took years to work out, and Chrome just made it faster to run.

So before you buy, install, or switch to anything this month, work out what you’re going to feed it.

That part is still your job.

Jasper

PS: Still a free subscriber? Premium subscribers get:

Get 35% off for 1 year

Eric Nowoslawski runs Growth Engine X, one of the most successful lead generation agencies out there.

52 concurrent clients. Notion, Intercom, Instantly, Clay, Secureframe.

8 million cold emails a month.

Last week he shared that Grok Bot doubled the leads his team was getting in a week for a client.

Then he said this: “replacing my daily Claude Code use.”

You might think : “Wow wait, didn’t Claude Code just ‘replace’ Clay and now Grok Bot replaces Claude Code?”

What Grok Bot actually is

Not Grok. Grok is the chatbot: you ask, it answers.

Grok Bot launched 11 August from SpaceXAI, the group formed after SpaceX bought Cursor and merged with xAI. Early beta.

Each bot gets its own computer in the cloud.

You sign it into your tools once, then it works across your apps the way you would, and comes back only when something needs approving.

The line that matters for GTM is theirs: it works “including platforms with no clean API or MCP.”

The difference with Claude Code is that Claude reaches tools through MCP connections, which someone has to have built first.

Grok Bot logs into the website like a person. Vendor portals, ad managers, old ops tools, anything with a login.

It also learns differently. No config file. You ask it to watch while you do a job once, and it saves the steps as a routine.

⚡ Why GTM Engineers like it so much

The reason GTM Engineers like Eric love it is more about speed than autonomy.

• Speed of setup.

• Speed of response.

• Speed of scheduling a task.

His words: “the closest I’ve felt to having a real coworker from these agents.”

A few specifics worth knowing:

• Usage is generous. Heavy daily use barely dents the allowance, which corrects the “expensive beta” framing, including mine.

• The API key handling is seriously good. Most agent tools make you either fiddle with keys or hand one straight to the model. Grok Bot brings up a card you drop the key into, and the model never sees it. That answers the credential worry I flagged earlier.

• It reaches your own machine too, not just the cloud one, so anything built locally in Cursor or Codex gets picked up.

• Plugins handle OAuth for tools that are usually painful to connect, Gmail and Drive included.

• You can run it from your phone.

“Replacing my daily Claude Code use”

Worth sitting with, because Eric is not wrong about the speed, and he’s using both at a scale most of us aren’t.

What’s been replaced is the execution layer, the running of jobs.

That’s a fair fight and Grok Bot may well win it, especially at agency volume.

What doesn’t move is what’s written down.

The CLAUDE.md that encodes who you sell to, which signals mean someone’s ready, what you never say.

That’s judgment in a file you own, and it stays yours whatever runs it next.

But I’d be overstating my case if I claimed these tools don’t compete.

For the day-to-day work of getting jobs done, they clearly do now.

So: try it. The question is what you hand it first, and how you’d know it went wrong before the damage lands.

Today, I will break down exactly:

• The first job to give it. The one mechanical task that tells you within an hour whether this thing checks its work or just acts. Get this wrong and you find out on live prospects.

• The audit prompt. Copy, paste, run it today. Every domain and inbox, every break ranked by what it’s costing you right now, with a step that forces the bot to show its work so you can verify each flag yourself in under a minute.

• The standing routine. The permanent guardrail to install before it touches a single prospect. Draft only, evidence rule, skip-list check, and the report section that makes an agent auditable instead of confidently wrong.

• The three failure signs. What goes wrong before real damage lands, and the exact moment to pull scope back.

• The per-client bot pattern. The idea from Eric’s post nobody’s talking about, and the reason he can run this across 52 clients without the context bleeding between them.
  
  Let’s dive in.

Rule 1: The first job to give it

Read more

I’ve reviewed hundreds of AI-written cold emails this year.

Almost all of them fail for the same two reasons.

Take a client of mine. He spent three weeks fixing his cold emails.

They ended up sounding exactly like him. Specific. Human. The kind of email you’d actually reply to.

But his reply rate was stuck at 0.4%. He’d just fixed the first reason perfectly.

The 50% everyone fixes

Shiv Sakhuja (Gooseworks AI founder) noticed that AI cold emails suck because the AI has no context about you.

Give it your voice, your proof points, your product story, and the output gets much better.

He’s right. That’s real, and it works.

My client loaded his CLAUDE.md with product details, case studies, voice rules, proof points.

And it did exactly what it should: The generic template voice disappeared. Every email sounded like it came from him and nobody else.

But better-sounding emails to the wrong people is just higher-quality spam.

The 50% almost nobody fixes

The problem was that his emails were landing with people who didn’t have the problem he solves.

This part is getting harder, because every GTM team now calls itself “data-driven,” which mostly means everyone looks identical.

Same dashboards, with the same filters.

Same recycled intent data, hitting the same prospects, in the same week.

If your list comes from a filter anyone can run, you’re competing with everyone who ran it.

So we threw out the filters and asked a different question: what was actually happening inside a company right before he closed them?

Where his signals came from

Not “B2B SaaS, 50-500 employees, currently hiring.”

We went through his closed-won customers and looked for what they had in common in the 90 days before they bought.

Three things came up:

→ a tech stack combination that breaks at a certain scale
→ a leadership gap in the exact function his product serves
→ a job posting open 60+ days, signalling an understaffed team

Yes, every one of those is public. Anyone can see a job posting and check a tech stack.

What nobody else has is your closed-won list. That’s where you learn which combination actually means someone is about to buy.

Your lead data tool has no filter for it because nobody built one. It only matters for your buyer.

Claude finds them because you tell it what to look for.

Reply rate went from 0.4% to 7.8%. Same product, same voice rules, same model writing the emails.

The only thing that changed was who received them.

The short version

→ Context about YOU just fixes the tone.
→ Context about THEM fixes the targeting.
→ Signals pulled from your own closed deals fix the competition problem.

People stop after the first one, wonder why nothing lands, and rewrite the copy again.

Your next three signals are sitting in your last five customers.

Go look at what was happening to them right before they said yes.

Jasper

PS: If you’re still a free subscriber, consider upgrading. Premium subscribers get:

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You sent the connection request. They accept, and you send a message.

Then one of two things happens.

1. Nothing comes back at all. 😔

2. Or they do reply, and you freeze, because now what? 🤔

That second one is where almost all LinkedIn outreach dies.

Someone actually answered you. They’re paying attention for maybe 30 seconds.

And you either jump straight to pitching a call and sound like every other salesperson in their inbox.

Or you say something vague and friendly and watch the whole thing fade out over the next week.

I did that for years. And here’s what changed:

46 meetings from 1,938 connection requests. Six months, about 12 requests a day. 2.37% meeting rate.

These were all ‘cold’ prospects. Fresh connections, pulled from LinkedIn signals. No warm intros, referrals, or inbound.

DM people who already know you, past clients, dormant contacts, your existing network, and the rates go further up.

LinkedIn Outreach Benchmarks 2026 tracked 180,155 connection requests sent between January 2025 and August 2026.

Across that whole dataset, the average is 5.4 meetings booked per 1,000 requests sent. Just over half a percent.

At that rate, booking 46 meetings takes about 8,518 requests. I did it with 1,938, roughly a quarter of the volume.

Learn everything about how to get your connection requests accepted here:

Now, today is about knowing exactly what to say once someone replies, until booking the meeting.

Not just a script you copy.

It’s a conversational strategy, where every message has one job, and you always know which one you’re on.

Years of getting this wrong, until I got it right

It took me a few years to get here. Now you can apply it in 30 minutes.

Every rule in the system below exists because an earlier version of it cost me a conversation.

And it’s all packaged into the skill you can save further down.

But I’m not handing you just a button to press blind.

Because it’s a bad idea to automate something you don’t actually understand.

So before the quick fix, here’s the entire strategy laid out in full.

Read it first. Then let the skill run it for you:

Read more

The SaaS companies crushing it right now have one thing in common. And it’s not AI.

Replit, Lovable, Databricks, Supabase,.. all growing fast, and none is dying.

Meanwhile Salesforce cut 4,000 jobs last year.

Workday’s CEO spent three quarters changing his story from “customers renewing at lower headcount” to “we’re not focused on seats anymore.”

Jason Lemkin listed these survivors, but didn’t explain what they share that makes them survive.

I’ll do it here. NONE of them sell seats:

• Harvey sells legal work done.

• Sierra and Decagon sell resolved tickets.

• Replit and Lovable sell working software you describe in plain English.

• Databricks, Supabase, Vercel sell infrastructure the AI-native economy runs on.

Every single one is priced around outcomes or usage, instead of humans with logins.

‘Old’ SaaS assumed humans would be the primary operators: Clicking, configuring, updating dashboards.

$50 per seat, more seats = more revenue.

That worked for 15 years. Then AI started compressing headcount at the biggest SaaS company on the planet.

Where This Shows Up in Your GTM Stack

You’re not switching out Harvey or Databricks this week.

But somewhere in your own GTM tools, this same thing is probably already costing you, and there’s usually one clear fix.

Clay works the old way. You buy credits.

Even the newer stuff, like using Clay’s tools from inside Claude instead of opening Clay’s own dashboard, still needs a person there triggering it, one small batch at a time.

Every call burns credits whether it needed to happen right now or not.

Claude Code works the new way. You own it.

You pay for what actually gets done, research finished, lists built, messages written, not for logins or API calls.

That covers a big chunk of what GTM tools charge you for: finding leads, researching them, writing outreach.

It’s not a swap for your CRM, phone system, or support tools.

The teams I work with are cutting their Clay bill by 60%.

They move research to Claude Code, and Claude calls Clay directly when enrichment is the right job for it, instead of running everything through Clay by hand.

If you’re paying per seat or per credit for anything, that clock started ticking about a year ago.

Worth checking before your next renewal.

Jasper

PS: Want the Claude Code setup founders use to replace $800/month Clay workflows (MCP stack + CLAUDE. md template)?

Get it right here.

Before sending any LinkedIn DM, you need to be able to answer 3 questions.

Good outreach starts before you write the first line.

If you don’t filter for fit and context, no message will land.

Here’s the typical DM on LinkedIn everyone gets, and everyone ignores:

Here’s exactly why it doesn’t work:

❌ Zero targeting.

“I saw your content” tells you nothing, it’s the connection-note equivalent of “hope this finds you well.”

❌ It reads like an email.

Four dense paragraphs is way too long for a first message on LinkedIn. You already give up reading when you see a wall of text. Chat is not email.

❌ It’s only about them.

Every line pitches their company. Nothing about you or why you’d care.

❌ It’s a pitch, dressed as a compliment.

The “we’d love to have you” line is the ask, wearing a friendly hat.

❌ There’s no good offer.

Nothing compelling, nothing useful, nothing to say yes to before being asked for something.

3 Crucial Questions

These 3 checks save you from wasting effort on people who will NEVER engage.

1. Are they active on LinkedIn?

Plenty of people have profiles but hardly log in.

If they haven’t posted or reacted in weeks, they likely won’t accept your connection request.

You already filtered for this when building your lists.

The Sales Navigator “posted recently” check separates people who might engage from profiles that never will.

2. Are they open to connecting?

Get 50%+ of Your Connection Requests Accepted answered this one.

Of course, you can only DM someone if you are connected (unless you send InMail).

Your profile and targeting do most of the work here.

When your headline tells people exactly what you do, for whom, and with what outcome, acceptance rates climb well past 50%.

Those new contacts are already pre-qualified.

3. Are they open to talking?

Getting connected isn’t the same as starting a conversation.

You need messages that bring up a relevant business problem and eventually lead toward a call.

Most people freeze here and send something generic that goes nowhere.

Nobody’s sitting there hoping a stranger messages them.

Your job isn’t to get everyone talking. You just need to find the small number of people who actually want to.

If your connection request had a personal note, pick that topic back up.

If it was blank, wait at least 24 hours first. Some people will message you first in that window, either to sell you something or to ask what you do.

Either way, you’ll know instantly if they’re worth talking to.

After those 24 hours pass with nothing, it’s your move. That’s where the system starts.

Results from real campaigns

Here are the results of campaigns I ran with a client, using prosp.ai.

Reply rates ranged from 20.8% to 45%, depending on how ‘warm’ the leads are.

Every good LinkedIn outreach campaign should have a rate of at least 20%.

If you don’t reach that, it’s time to get back to the drawing board. You can do that right here, by adopting the 3-step system.

The 3-Step DM System

If you’ve read the Claude Code Email System, this will feel familiar.

Here’s what an effective LinkedIn sequence looks like:

Read more

Five pieces from this year got read more than anything else I’ve published, by a wide margin.

If you’ve only got 10 minutes to go through my content, spend it here.

They’re the ones that changed the most GTM setups.

1. Start with Claude Code for GTM: Where to Start if you haven’t built anything yet.

Many think building a prospect list means paying for a scraping tool or hiring someone to copy-paste for three hours.

This is the piece that proves neither is necessary.

2. Once that’s running, Clay vs Claude Code settles a question worth asking before you pay for either:

Clay is valued at $5B, and Claude Code can reach the same data directly through MCP.

Read it before your next renewal, not after.

3. The Only GTM Tools You Need in 2026.

Some of those twelve-step tool stacks run $3,000 a month and need a full-time engineer just to keep working.

Complexity is profitable, for the people selling it to you.

If your subscriptions have crept past what you can name off the top of your head, start here.

4. Catch Intent on LinkedIn with Claude

The one to read if you’ve been prospecting strangers while ignoring the network you already have.

Your next 10 leads are probably closer than you think.

5. And Context Engineering last, on purpose.

I have yet to meet a founder who said their AI outbound is compounding.

This is the piece that explains why, and what fixes it.

Your bonus: a real diagnostic (not a survey).

If you’ve got a CLAUDE.md or companies.csv already built, this prompt reads them directly instead of asking you to self-report.

If you don’t have those yet, it works from your answers instead.

Either way, it ends by naming the one piece above to read first, instead of a vague “area” to think about.

Help me figure out which of these five gaps matters most in my
GTM setup right now, and which piece I should read first
because of it.

## STEP 1: CHECK WHAT'S REAL
If a CLAUDE.md or companies.csv exists in this project, read
them and use what's actually there. If neither exists, tell me
so, and ask me the five questions below in one message, short
answers are fine for all five at once.

1. Is Claude Code actually running against my market, or still
   unused?
2. Could I name and justify every tool I'm currently paying
   for, in one sentence each?
3. Am I finding prospects mainly through obvious channels
   (ads, cold lists), or through real signals?
4. Have I actually looked at who's one or two steps away from
   me in my own network, or am I only reaching strangers?
5. Does every outbound session start from zero, or is there a
   file that remembers what worked last time?

## STEP 2: NAME THE ONE GAP
Tell me which single area is weakest, in one sentence, based
on real evidence where you have it and my answers where you
don't.

## STEP 3: SEND ME TO THE RIGHT PIECE
Match the weakest area to exactly one of these and give me the
title to go read next:
- Area 1 → "Claude Code for GTM: Where to Start"
- Area 2 or 3 → "The Only GTM Tools You Need in 2026" or "Catch Intent on LinkedIn with Claude," whichever fits my
  answer better, and say why
- Area 4 → "Catch Intent on LinkedIn with Claude"
- Area 5 → "Context Engineering"

Jasper

PS: If you want full access to all 5 pieces above + every guide, every Skill, every system —> get 35% off your first year below. Offer valid until Sunday.

Get 35% off for 1 year

The hottest trend in GTM right now is outsourcing your brain to an AI.

A few months ago, an AI startup (Gooseworks) wanted to run my GTM for me.

YC-backed, an AI coworker with its own computer, email, skills and memory. It would run outbound, manage the CRM, and I’d approve its work from Slack.

A client asked me last month whether he should just pay for one of these instead of learning Claude Code.

“Why would I build it when I can hire it?”

Fair question. Here’s what I told him:

The blind spot in every managed AI

These tools execute fast. What they can’t do is fix what you feed them:

• A vague Ideal Customer Profile gets automated into vague outbound

• Generic signals become generic campaigns, run faster

• Untested messaging gets scaled instead of tested

The strategic thinking still has to come from you. No tool changes that, whichever logo is on the invoice.

Which means the real question was never “AI or no AI.”

It’s which camp you join:

💳 The Renter

• Pays monthly for a managed AI coworker

• Fast start, polished interface, someone else’s roadmap

• Owns none of it: the prompts, the data, the logic and the off switch all belong to the vendor

The day the pricing changes or the startup pivots, his entire GTM walks out the door with it.

That’s not a hypothetical anymore.

Gooseworks still says “runs your GTM” on its homepage.

But three months on, what it’s actually shipping and pushing hard isn’t an autonomous coworker running your outbound.

Now it’s a tool that remixes ad templates for Meta.

🛠️ The Builder

• Creates the same system in Claude Code

• Slower first week, then compounding every week after

• Owns everything: the CLAUDE.md that encodes his customers, every prompt, every learning, at a fraction of the platform fees

Nobody can raise the rent on his own brain.

Even the AI startups know it

Here’s the detail that gives it away:

Gooseworks open-sourced every skill its AI coworker uses, both before the pivot and after.

That includes the exact skill that now makes the ad creative they charge you a subscription for.

Anyone can install it straight into Claude Code, for free.

Think about what that means. They’re charging monthly for something you could build yourself with the same free parts already handed out.

The subscription is the business, not the product.

The value was always the system underneath it, the one he open-sourced.

And when the big ambitious version of the company got hard to keep running, that free, owned system is the part that survived.

Lay the first brick today

The renter’s advantage is speed to start. So remove it.

The builder’s first brick is a CLAUDE.md, the file that teaches Claude Code who you sell to, and it takes one conversation to draft. Here’s the master pronpt:

You are my GTM system architect. I'm building the foundation
of my outbound system in Claude Code instead of renting a
managed AI tool: two files, CLAUDE.md and BATTLECARD.md, that
every future session will read before doing anything else.

Never smooth over a vague answer into something that sounds
specific. If my answer is soft, tell me it's soft and ask me
to sharpen it before moving on. A file built on vague answers
produces vague outreach, which is the exact failure I'm
building this system to avoid.

## STEP 1: INTERVIEW ME, ONE QUESTION AT A TIME
Wait for each answer before asking the next.

For CLAUDE.md:
- What I sell, in one sentence, and roughly what it costs
- My best customer of the last year: who, what was happening
  in their business right before they bought, what it was
  costing them
- The customer I regret taking: who, and the early warning
  sign I ignored
- Two or three job titles I actually sell to, not job titles
  I wish I sold to
- One phrase or tone I never want to sound like

For BATTLECARD.md:
- My single best proof point: a named customer (or
  anonymised, my choice) and a specific number. If I don't
  have one yet, say so rather than inventing one, and note
  it as a gap to fill before this file is trustworthy
- The one thing that makes a prospect say yes, described the
  way a customer would say it, not the way my website does

## STEP 2: DRAFT BOTH FILES
Write CLAUDE.md with these sections: ROLE, COMMUNICATION STYLE
(3 voice rules from my answers, not generic advice), IDEAL
CUSTOMER PROFILE (written as the condition my best customer
was in, not just firmographics), NO-GO LIST (built from my
regret customer).

Write BATTLECARD.md with: THE PROBLEM WE SOLVE, THE PROOF
POINT (exactly what I gave you, flagged clearly if it's
generic rather than a named result), OBJECTION HANDLING (ask
me for my single most common objection and draft one response
to it).

## STEP 3: STRESS-TEST BOTH FILES
Before finalising, tell me which answer I gave you was the
weakest, the one most likely to produce generic output if I
don't fix it now. Push back on that one specifically. Then
tell me what's still missing that a full system would need,
the proof point, the ICP, or the voice rules, so I know this
is a starting foundation, not the finished system.

Keep both files under a page each. Every future session
inherits them, so nothing generic gets in.

One conversation, and you own the file every managed tool keeps behind its login.

Takeaway

The renter and the builder end up with the same capability on day one.

They end up in different businesses by year one: one holds a subscription, the other holds a system.

Which camp are you in: paying for a managed AI coworker, or building the system yourself?

Reply “builder” and I’ll tell you what I’d add to your CLAUDE.md .

Jasper

PS: The prompt gets your first file drafted. The Claude Code GTM Starter is the full system: CLAUDE.md, five saved Skills, and the Rules that catch mistakes before they ship.

Get it here.

The other day someone congratulated me on my ‘fundraising’.

Warm little note attached to their connection request:

“Congrats on the fundraising! Super excited to see the Clay team making some serious waves these days. Would love to connect!”

I don’t work at Clay. I never have.

Some automation matched my profile to Clay and confidently personalised away.

That’s what personalisation at scale looks like from the receiving end.

And it’s why the entire approach is backwards.

The decision before the note

Here’s what happens when your request lands:

The person sees your name, your face, and your headline.

In about 2 seconds, they make one subconscious calculation:

“Do I recognise this person, and if not, could this connection be worth something?”

Two nos, and even the sharpest note disappears into the void.

A yes, and they click ‘Accept’ without reading a word you wrote.

The numbers

Across my client’s last campaigns, requests to well-targeted lists ran between 50% and 74% acceptance.

No clever notes or fake familiarity. Targeted lists, a headline that says something concrete, and mostly blank requests.

Hold onto that reply rate column on the right. We’ll come back to it at the end.

Those acceptance rates start with the list.

The targeting came from the plays in the last three editions:

• Live intent signals

• LinkedIn’s native hiring signals.

• Your competitor’s newest connections

If your list is full of people already showing interest in a solution like yours, the rest of this system turns them into connections.

Why connection notes backfire

Every note that opens with “Saw you liked...” or “We’re both in...” screams the same subtext: I’m about to sell you something.

Everyone has seen a hundred of these. The template is the tell, and more effort makes it worse, because effort spent proving familiarity signals exactly the opposite.

The senders rarely work out why their acceptance rate sits under 30%, because they keep fixing the copy instead of the system behind it.

The system, 3 steps:

Read more

The compound effect of AI has a dark side nobody talks about.

Build good habits with it and they compound into something real.

Build lazy habits and those compound too, into a dependence you don’t notice, until it’s too late.

I’ve had a version of this conversation with multiple founders this year, and it always splits the same way.

Same Claude Code, same MCP servers, same starting capability.

Six months apart, they’re describing two completely different relationships with the stack.

✅ The founder who compounds

• Reviews every AI-drafted email before it goes out

• Tests messaging manually before automating anything

• When something fails, updates the no-go list so the system never repeats it

• When something works, asks why and writes the pattern into CLAUDE.md

Each campaign lands a little sharper than the last, because the system remembers what he learned instead of him having to.

❌ The founder who gets dependent

• Automates everything from day one

• Hardly reviews the output, checks the dashboard instead of the emails

• Runs the same generic campaign month after month, because nothing feeds learning back into the system

Ask this founder six months in to describe his own ICP without opening a prompt, and watch him struggle.

AI didn’t make him better. It replaced a muscle he stopped using, slowly enough that he never noticed the trade.

Same stack. Opposite outcomes.

The gap isn’t which AI you use, since both founders above are usually running the exact same stack.

It’s where you’re treating it as a training partner or a shortcut.

• The founders who review, refine, and feed their learning back into the system are building something that compounds every week.

• The founders who automate and forget are building a dependency that gets harder to unwind the longer they wait.

Run this before automating anything else

The habit that separates the two doesn’t need to be complicated.

It’s writing down why something worked, every time, so the system inherits the lesson instead of losing it.

Here’s a Claude Code prompt that forces that habit instead of hoping you remember to do it:

You are my GTM system architect. I'm building the foundation
of my outbound system in Claude Code instead of renting a
managed AI tool: two files, CLAUDE.md and BATTLECARD.md, that
every future session will read before doing anything else.

Never smooth over a vague answer into something that sounds
specific. If my answer is soft, tell me it's soft and ask me
to sharpen it before moving on. A file built on vague answers
produces vague outreach, which is the exact failure I'm
building this system to avoid.

## STEP 1: INTERVIEW ME, ONE QUESTION AT A TIME
Wait for each answer before asking the next.

For CLAUDE.md:
- What I sell, in one sentence, and roughly what it costs
- My best customer of the last year: who, what was happening
  in their business right before they bought, what it was
  costing them
- The customer I regret taking: who, and the early warning
  sign I ignored
- Two or three job titles I actually sell to, not job titles
  I wish I sold to
- One phrase or tone I never want to sound like

For BATTLECARD.md:
- My single best proof point: a named customer (or
  anonymised, my choice) and a specific number. If I don't
  have one yet, say so rather than inventing one, and note
  it as a gap to fill before this file is trustworthy
- The one thing that makes a prospect say yes, described the
  way a customer would say it, not the way my website does

## STEP 2: DRAFT BOTH FILES
Write CLAUDE.md with these sections: ROLE, COMMUNICATION STYLE
(3 voice rules from my answers, not generic advice), IDEAL
CUSTOMER PROFILE (written as the condition my best customer
was in, not just firmographics), NO-GO LIST (built from my
regret customer).

Write BATTLECARD.md with: THE PROBLEM WE SOLVE, THE PROOF
POINT (exactly what I gave you, flagged clearly if it's
generic rather than a named result), OBJECTION HANDLING (ask
me for my single most common objection and draft one response
to it).

## STEP 3: STRESS-TEST BOTH FILES
Before finalising, tell me which answer I gave you was the
weakest, the one most likely to produce generic output if I
don't fix it now. Push back on that one specifically. Then
tell me what's still missing that a full system would need,
the proof point, the ICP, or the voice rules, so I know this
is a starting foundation, not the finished system.

Keep both files under a page each. Every future session
inherits them, so nothing generic gets in.

This is the kind of habit that separates the two founders above.

One does this every week without thinking about it.

The other has never run anything like it, which is why nothing he learns ever makes it into the next campaign.

Takeaway

The stack was never the variable. It’s identical in both cases above.

What decides the outcome is whether someone keeps doing the thinking, or hands it over the day the automation starts working well enough to ignore.

Run the prompt above after your next batch, and you’ll already know which side of this you’re on.

Jasper

PS: The prompt encodes one batch of lessons. The Context Engineering framework is the full system: the CLAUDE.md structure, the four components of an AI-ready GTM setup, and the method for making every session compound instead of repeat.

Get it here.

I was on a call with a CMO this week who runs a private group of growth leaders.

He sits with them every week and hears what they do.

He told me the market has not just slowed, but stopped moving.

The businesses that need solutions have quit buying. They’ve stopped bringing in outside help, and they still haven’t shrunk the teams they know are too big.

“They’ll run the old playbook,” until the board fires them.”

Why founders freeze

A new tool launches every day, and someone explains why it changes everything.

Another post insists that skipping AI outbound already means falling behind.

The feed fills with options before a single decision gets made.

What causes the freeze? Noise.

So you wait. Waiting feels responsible: research, “getting the setup right,” a quieter quarter to come.

None of it looks like a mistake, because standing still leaves no evidence behind.

A failed campaign leaves a mark, and so does wasted spend.

Doing nothing leaves nothing to explain on Monday, which is exactly why it feels safe.

A close rate that hid the problem

A founder I worked with last year had a 70% close rate. Every deal came through referrals and warm intros.

She hadn’t sent a cold email in five years, because why would she? The number said everything was working.

The number was also the trap. A 70% close rate on a shrinking pool of warm leads isn’t a strength but a ceiling with good lighting.

A 70% close rate sounds like skill. Hers was something else: she'd never tested herself against a stranger who didn't already trust her.

The unfreeze system

Four simple steps, each with one job.

1. Name one customer: Not your Ideal Customer Profile. The single real account you’d want ten more of.

2. Name the pattern: What was happening at that company right before they bought?

3. Find one match: One company showing that same trigger right now, with dated public evidence.

4. Send one message. One sentence, naming the evidence, sent today.

Here’s the system as a prompt, so it runs itself instead of living in your head:

You are my GTM strategist. I run a bootstrapped B2B company and I've stalled. Too many tools, too many tactics, too much noise. I keep researching and never start.

Remove every choice but one. Do not give me a system, a
stack, or a list of options. Give me exactly one company to
contact and one message to send. Nothing else.

## STEP 1: LEARN FROM ONE CUSTOMER
Ask me to name the single best customer I've ever had, and
tell you:
- what they do and roughly how big they are
- what was happening in their business when they bought
- the problem I solved, and what it was costing them
Wait for my answer before continuing.

## STEP 2: NAME THE PATTERN
From that, name the single situation that makes a company
likely to need me right now. Write it as one line:
"A company that has just [event], which usually means
[the problem I solve]."
Ask me to confirm or correct it. Do not search until I do.

## STEP 3: FIND THE ONE COMPANY
Search for companies showing that exact situation in the last
90 days, with dated public evidence: a hire, a launch, a
raise, a post. Weigh candidates internally, but show me only
the single strongest match.

Before showing it, check yourself:
- Is the evidence dated and specific, not inferred?
- Would you stake your own reputation on this match being real?
If nothing clears that bar, say so plainly and tell me what
would need to be true for a company to qualify, rather than
handing me a weak guess dressed up as a strong one.

## STEP 4: WRITE THE ONE MESSAGE
For the company you found, write the opening line I'd send:
one sentence, naming the evidence and what it likely means
for them. No pitch, no meeting request, no link.

Show me, in order:
- the company and the dated evidence, so I can verify it
  myself before sending anything
- the one message

Stop there. Tell me to send it before offering anything else.

Ten minutes gets you one company and one line, verified, ready to send.

What this fixes, and what it doesn’t

This fixes today’s problem: getting the first message out before the noise talks you out of it.

It doesn’t fix next week.

It won’t tell you which signal to build the next list around, or stop that list from going stale, or turn one reply into a habit that runs every week without you remembering to trigger it.

I built that part as a weekly routine, for when this week’s start needs to become a system.

That wall shows up for almost everyone within a week of unfreezing.

The first message works, and then you stall again because nothing is running underneath it.

Takeaway

The noise got loud enough that standing still started to feel like the safe choice.

But standing still is the most expensive move on the board, paid every month spent waiting to feel ready.

Three things you can do today:

• Name the one customer you’d want ten more of.

• Run the prompt above and get one company, one line, verified.

• Send the message

Jasper

PS: This article gets you unstuck once. Premium keeps you unstuck:

• Full access to the Claude GTM Systems Library
• 30+ Claude Code Skills for list building, lead enrichment, and outreach that books meetings with your ideal clients.
• Every prompt, skill, and framework ready to apply and run. New skills added regularly. You keep everything permanently.

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Every day, your future customers tell LinkedIn they’re trying to solve a problem. They just don’t tell you.

• Someone in your market clicks “attend” on an event about the exact problem you solve.

• Someone else comments on a post about it.

• A third adds a “hiring” badge that says they’ve got a gap and a budget.

Each one is a person looking for a solution to their problem, in public, where you could see it. Most people walk straight past.

In case you’ve walked past them you’ve sent the messages. Built the list, right titles, the people you’d actually want as customers.

But almost nothing comes back. So you assume the copy’s wrong and rewrite it.

The problem was never your message, but reaching the right people at a random moment.

So even a perfect message goes nowhere, because the person reading it isn’t thinking about the problem you solve.

Why timing beats volume

Many chase the loud signals. A company raises money, a company posts a job, and everyone piles in at once.

The quiet ones are better, and barely anyone watches them. They’re the small things people do on LinkedIn every day without thinking twice.

Less obvious, less crowded, and just as much a sign that someone’s in the market.

Too many names, too little time

Collecting these signals is easy. The list just comes back huge and mostly useless.

Pull 500 event attendees and maybe 40 are real buyers. Grab everyone who commented on a post and most are fans and job seekers.

Then you’d have to read all 500 by hand. That’s hours nobody sustains, which is why these signals stay theoretical for almost everyone.

That’s the part Claude takes off your hands.

Two steps: grab the list, then qualify

Two jobs, two helpers.

1. Grab the names. A LinkedIn automation tool collects everyone who showed a signal, in bulk. You can use HeyReach for this. It’s quick at gathering, which is all you need it for. Check my previous walkthrough of how to do this exactly.

You don’t have to pay for one, though. Claude in Chrome reads the same lists straight off the page, pulling the names into a clean list for you.

2. Sort and rank. Claude reads that messy list, drops everyone who isn’t really your buyer, and ranks the rest by how interested they look, telling you why each made the cut.

Where to start

Four signals, and they’re not equal. Work them in this order:

Read more

A few years ago I almost ran my business into the ground, on a good week.

I was drowning in client work and got complacent. Booked solid, delivering, busy in the best way. And because the pipeline was full, I did the laziest thing a founder can do.

I stopped feeding it.

No outbound, no prospecting. Why would I, with more work than I could handle?

For months, nothing happened. Then the market shifted.

A few projects wrapped, a couple didn’t renew, and I looked up to find the top of my funnel gone empty.

I’d been so sure of the work that I’d let the one thing that brings in more work go quiet.

So I rebuilt outbound from zero. Building pipeline when you’re calm is work. Building it when you can feel your cash running out is a different kind of pressure.

I got out of it. But I never forgot how avoidable it was.

You might be making the same mistake

Right now, I see a whole market of teams making this exact mistake. Trusting a channel because it feels safe, while it slowly stops working.

Inbound is changing underneath everyone, and AI is the reason.

People used to search, scroll a page of links, and click yours. Now they ask an AI, get one answer, and never see the list your SEO fought for years to climb.

Your ranking holds. The clicks it used to earn quietly go elsewhere.

And the founders best at the old game are the most exposed. They trust the channel that built them, and keep waiting for it to recover.

The dip you can always explain away

I watched this land on another founder that sells to retailers, names you’d recognise. A business built entirely on his network and inbound, deals that turn into five-year clients.

He’d message me every few months and agree it was time to start outbound. But every time, nothing moved. The channel still felt warm enough to wait on, and starting felt big.

So he waited. Another quarter, another dip he could explain away.

The founders who get out of this have one thing in common

I take on a handful of founding teams at a time, by choice, and the ones I say yes to share one thing: They’ve stopped waiting.

I can’t make that call for anyone, and I’ve stopped trying. Nobody changes someone who isn’t ready.

The most I can do is say it plainly: every month you wait, someone who started is reaching the clients you assumed were yours.

The way out is the thing I stopped doing

The way out? Reach the right companies directly, before they go looking for what you sell. The ones showing in public, right now, that they have the exact problem you fix.

Most teams never start, because the internet handed them ten tools and a hundred tactics and they froze.

So here’s the first step, small enough that you can’t talk yourself out of it. Point this at your best customer, and it builds a starting list of companies in the same situation right now.

You are my outbound starter. I've leaned on inbound and my
network for too long, and it's drying up. I need to start
reaching the right companies directly. Don't hand me a system
to master. Hand me one sharp list I can act on this week.

## STEP 1: LEARN FROM MY BEST CUSTOMER
Ask me to name the single customer I most wish I had ten more
of, and tell you in plain words:
- What they do and roughly how big they are
- What was happening in their business when they bought
- The specific problem I solved, and what it was costing them
- How they found me, or how I found them
Wait for my answers before doing anything else.

## STEP 2: FIND THE REAL TRIGGER
Work out the underlying situation that made them a buyer. Not
the industry. The condition: a recent change, a pressure, a
moment. Write it back to me in this exact shape:
"A company that has just [specific event], which usually means
[the problem it creates that I solve]."
Ask me to confirm or fix it. Do not search until I confirm.

## STEP 3: BUILD THE LIST, SIGNAL-FIRST
Search public sources for ten companies showing that trigger
right now. Rules:
- The trigger must be visible from something real and dated in
  the last 90 days: a hire, a launch, a funding round, an
  expansion, a public post, a job ad.
- The company must plausibly fit the size and type of my best
  customer.
- No guesses. If you can't point to dated evidence, leave it out.
- If you can only verify seven, give me seven and say why.

## STEP 4: NAME THE BUYER, NOT THE OBVIOUS NAME
For each company, the person to reach owns the problem the
trigger points to, not always the most senior name. Small
company: the founder. Bigger one: the function head one level
above the pain, not the CEO who'll never reply. Give me the
exact role at each.

## STEP 5: HAND ME THE STARTING BATCH
Return a table, strongest first:
- company
- domain
- the dated evidence they share my customer's trigger
- the person and role to reach
- one line on the angle I'd open with, naming the evidence
Then tell me the single company to email first, and why it's
the best use of my first hour. Keep it to ten.

Ten companies, one afternoon. That’s all the first step is.

I had my good week years ago. It’s the most expensive thing I never noticed.

Don’t wait for yours to end before you feed the funnel again.

Best,

Jasper

PS: That prompt gets you one list. The full system, every Claude Code Skill, the Systems Library, and every guide, is what turns one list into pipeline you build every week, without waiting for inbound to save you.

Become Premium to Build

I’ve been sitting on this one for a few years.

Every time I thought about publishing it, I hesitated. It’s the kind of play that stops working the moment too many people know about it.

But it’s the most direct way to source leads I’ve come across, and it belongs with the readers who’ll actually use it more than it belongs in a drawer. So here it is.

One caution before we start. The less your competitors know this is possible, the longer it keeps working for you. So keep it to yourself.

The first time I ran this, I’d set the filters, pulled a list, and started reaching out. It worked faster than anything I’d run before.

Then a competitor messaged me on LinkedIn:

“hey man! are you trying to poach my clients?”

Whoops. I hadn’t touched his CRM or broken into anything. The filters were sharp enough that I’d pulled his leads, and some of his freshest actual clients with them.

The highest-intent signal in B2B

Think about what has to be true for someone to connect with your competitor, this week.

Often, they’re not just browsing. People don’t usually connect with a vendor in your category for nothing.

Many are researching the space, sizing up a provider, or already mid-conversation about buying the kind of thing you sell.

The connection is the visible residue of an active buying process.

A fresh connection to your direct competitor isn’t an inference. It’s often a person who has taken a deliberate step toward buying what you sell.

There’s almost no warmer public signal, because there’s no closer proxy for intent than someone walking toward your competitor’s front door.

No list was ‘stolen’ really. The buyer showed their hand, and you read it before anyone else did.

What you need

• LinkedIn Sales Navigator. Required. The filters that make this work don’t exist on a free or Premium account.

• A way to export the leads. Either Claude in Chrome, which reads the list and pulls it for you, or a lead-enrichment tool with a Chrome extension like Apollo, which is faster and can attach emails as it goes. Pick one.

• Claude (Chat or Code). For the cleanup and enrichment pass after export. Code only if you want the weekly routine on top. For a one-off, chat is fine.

The system

Step 1: Build the search and save it.

In Sales Navigator, run a lead search with these filters:

Read more