Founding GTM Engineer at @clay and author of The GTM Engineering Newsletter. Husband & Father of 2 in Brooklyn.

Brooklyn, NY
This 👇 plus must be able to use/learn AI
My best career advice: At every job you should either learn or earn. Either is fine. Both is best. But if it's neither, quit.
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why not HUUUUGE intelligence
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Who are the most AI-pilled GTM operators on X? I need some good accounts to follow. Pls help
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🤣 Dino Jurassic SaaS
how do we get @BrianLaManna_ out of Dino Jurassic SaaS and into AI-native GTM? need more creators in the AI space + love his content would be cool to have him report in the trenches at one of them
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The other one is "this is a once in a lifetime opportunity" No its not
the most bogus cliché: “When offered a seat on the rocketship, don’t ask what seat, just get on it.” I hate this quote and have no idea why it got popularized people don’t join rocketships because they turn down great roles; they don’t join because they don’t get those offers in the first place
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Everyone uses Monty at @clay
One of our biggest bottlenecks was the queue between a good question and someone with the time and context to give a good answer. So our data team built Monty, a custom AI analyst who lives in Slack.
Article

We gave everyone at Clay an AI data scientist (how we built it)

How we packed our data team's brain into an analytics agent Josh Hanson · Pranav Mital (@pranavmitl) · Sep 30, 2026 Over the past decade, most data teams have converged on a common “modern data stack”

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Alex Lindahl retweeted
New episode of the Crew Podcast, and it's our first roundtable. Liam Mulcahy (Parallel), Jack Gashi (Avoca), Mark Ebert (Profound), Todd Busler (Clay). Four sales leaders running revenue at AI-native companies, one table, some bourbon. They get into why annual planning is dead, which parts of the plan get written in pen, the deal cap Jack uses to stop reps from turtling, and why Jack thinks most AEs should forget about equity entirely. For founders making their first GTM leadership hire, VPs scaling teams, and reps trying to pick the right rocketship.
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what us Creators in Residence do at @clay and why we have them
I don’t want to sell you a fishing rod. I want to fish for you, show you how I fish, and inspire you to learn. I want to share the joy and impact of the work until @clay becomes the obvious choice for growth. My last video raised a few questions. So I made a follow-up 🐟
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Alex Lindahl retweeted
I don’t want to sell you a fishing rod. I want to fish for you, show you how I fish, and inspire you to learn. I want to share the joy and impact of the work until @clay becomes the obvious choice for growth. My last video raised a few questions. So I made a follow-up 🐟
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the last mile of vibe coding/designing is so frustrating that its almost not worth it
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Everyone will say you can't do it
Every time a @clay customer asked “can we outbound Germany?” I'd freeze. GDPR. Strict AF. I had no playbook to offer. Until Nicolas Schell gave me another perspective. We're hosting a @Reddit AMA on the topic. Thursday 2:30PM EST on r/gtmengineering. Drop your questions and we'll be sure to cover.
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Every time a @clay customer asked “can we outbound Germany?” I'd freeze. GDPR. Strict AF. I had no playbook to offer. Until Nicolas Schell gave me another perspective. We're hosting a @Reddit AMA on the topic. Thursday 2:30PM EST on r/gtmengineering. Drop your questions and we'll be sure to cover.
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You’re wasting credits in @clay . Do this instead. Claygent for web research LLMs for generated content Jev for decisions (routing, labels, y/n) 7 use cases you should use @typesafeai Jev for in Clay.
New rule of thumb in @Clay: Formula when it’s arithmetic. Jev when it’s a label / score / yes-no LLM when you need words. Claygent when you need the web. Jev in Clay = - Saved credits - Faster workflows - Better decisions - Reliable confidence
Article

Mini guide to decision AI in Clay

Let the LLM write, let Jev decide. We have a new GTM primitive: Decision AI. It's called Jev. It saves money. Accelerates workflows. Produces reliable confidence scores. Think of Jev as the bouncer

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Alex Lindahl retweeted
New rule of thumb in @Clay: Formula when it’s arithmetic. Jev when it’s a label / score / yes-no LLM when you need words. Claygent when you need the web. Jev in Clay = - Saved credits - Faster workflows - Better decisions - Reliable confidence
Article

Mini guide to decision AI in Clay

Let the LLM write, let Jev decide. We have a new GTM primitive: Decision AI. It's called Jev. It saves money. Accelerates workflows. Produces reliable confidence scores. Think of Jev as the bouncer

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Excited to try out @CueAgents from @ManusAI. But how does it differ from @Muse & @bot?
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This is how you bring em together + The recipe to scale @clay w/ RevOps
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