A few months ago, Lenny Rachitsky has a podcast interview with Howie Liu, a founder of AirTable. That point that stuck with me the most was that the organization is split up into Fast Team vs Slow Team. Neither team is inherently “better” or more skilled or more important than the other. They simply prioritize values differently so that they can operate at different paces.

The prioritizations and values are where I go off-script onto how I think Marketing Engineering/MarTech play into the work I’ve done.

I like MarTech to be fast and to cover the gaps for Product & Engineering while they pull together resources and designs. In a 2025 example, Calm started. offering BOGO deals whereby partners would send a CSV of thousands of codes and we wanted an system to keep an inventory of our available codes. I cobbled together a system of webhooks & API calls between Iterable & Google Sheets that we ended up rolling out to handle inventory for 5 partners: 50,000 unique coupon codes. Only 5 complaints!

At Calm, even among Lifecycle teams operate at different speeds in different silos. The Enterprise team is inherently slower and more methodical because there are big contracts at stake. The Consumer-facing team moves faster because we have a large pool of available users to test with. Experiments can reach stat sig within a day if we send enough emails.

At Speechify, the phenomenon was similar. As we were launching the Lifecycle programs from zero to one, speed was paramount. We needed to get emails in front of users so that they could buy a plan. The first few weeks were about as fast as we could work to launch a basic 4-flow Lifecycle marketing plan. We were in fast mode. Once we got launched, we had to slow down to get a better understanding of the optimizations we could make in partnership with other teams. Once those efforts got into motion, we would go fast again with the next project like testing channels such as SMS or WhatsApp. And migrating over to CustomerIO.

In order to perk up my familiarity with the practical business applications of AI, I stumbled upon an opportunity to work on a project involving Clay, which I can only describe as an automation platform that is a hybrid of Zapier and Google Sheets. Clay is a really interesting platform but the learning curve has been steep.

Having experimented with it about 20-ish hours, I think I have gotten the hang of it. Clay’s documentation, Clay University, has been a great resource and the community chat boards are pretty active. Lots of good tips an tricks to be found.

Here are some Clay design patterns that I’ve gotten accustomed to.

Job-Starter Table -> Multiple task tables -> Merge table

i’m tinkering around with trying to understand agentic AI and tool-use.

What I would like to be able to do is have the AI read a Google Sheet and use Tools to build multiple emails for me.

Hours 1-2

I read that LangChain is a framework for creating Agents. I presume it’s free? I followed these instructions to get a local LLM installed on my laptop. These instructions said to use Ollama so that’s the path I’m heading down. https://js.langchain.com/docs/tutorials/local_rag

My laptop was able to read the example document. That’s a win. I have a Macbook Air M3 with 16GB of RAM that I bought in September 2024 for about $1,400 USD. In other words — a firmly middle-of-the-road new Mac. I presume it’s going to run slow as molasses?

Hours 3

Back to tools. The Ollama Blog announced tool support in July 2024 which led me to this code example. Now I think we’re in business!

https://github.com/ollama/ollama-python/blob/main/examples/tools/main.py

Reading the docs for Llama 3.2B, it appears this new version is OK for tools and designed to run on less powerful machines. I don’t know what 1B or 3B means. I guess 3B words indexed?

Ollama blog notes were very helpful in explaining the steps necessary to index my documents so that AI will be able to do something with the text. It involves making embeddings by using an indexer and a vector store. I don’t know what that means. Hopefully I don’t need to know? https://ollama.com/blog/llms-in-obsidian

Hour 4

So much information my mind is spinning right now.

This agent tutorial is exactly what I’d like to figure out. https://python.langchain.com/docs/tutorials/agents

LangChain has a lot of community-contributed tools. Most interesting tools I’m seeing right now are these tools:

• Twilio – Can I get the Agent to create a message for me? Limited to SMS and WhatsApp messages so not quite what I’m looking for.
• Playwright Browser Toolkit – maybe too advanced for a newbie. Requests may be better.
• Requests interesting for scraping sites. I wonder if it could help me categorize blog content.
• RSS Loader – could it help me curate stories?
• Obsidian – for on-my-computer notes

Hour 5

https://python.langchain.com/docs/how_to/tool_calling

lost in the ether of wrestling with getting this to call a feed and find relevant articles. Realizing now that reading content has certainly been done-to-death. How can I make this do something more interesting for me?

Hour 6

struggled with having Ollama read and summarize my Flipboard magazine. this has been a waste of an hour

Hour 7

Back to the original idea of why did I want agent AI in the first place?

Goal: I want to increase sales. General one-message-for-all approach is not working. I want to split people up into groups/clusters of interests.

I also want to find a pattern of behavior that occurs before people buy.

It has been suggested to me that a Graph Neural Network (GNN) is a good way to identify this behavior. GNN is a whole different rabbit hole to look into. My friend told me to look at JanusGraph

At this point, I’m not quite sure how AI and Graphs fits into the picture here. The insight will come eventually.

At Calm, we rely heavily on Figma to generate marketing copy proofs for copywriters and other marketing folks to see their text visually in an email mockup.

Typically the process might go as follows:

1. Using an existing template, our designer mocks up a series of emails containing rough draft.
2. 3-email series x4 population segments = [12 emails x 15 minutes per mockup = 3 hours]
3. He shares with the copywriters.
4. Copywriters eyeball the design and write new copy to fit the theme of the year. They don’t have Figma seats, so they write their revisions as Figma comments.
5. The Designer now transfers the text edits from the Figma comments into the Figma text nodes. [5 minutes per email = 1 hour]
6. Designer shares the Figma file back to copywriters. Every round of back-and-forth is 1 hour.
7. Now we have to transfer the text from the Figma into email templates! [15 minutes per email = 3 hours]

The total effort is about 7 hours for a 12-email campaign or 35 minutes per email.

Our work process is overly reliant on having access to a Figma license. The process served us well for several years but we came to a crossroads where there are too many back-and-forths between design and copywriting and it was taking up a lot of time.

In effort to streamline or workload, I created a Figma plugin to pull text from a Google sheet

In Figma,

The Figma mockups have a specific # of text layers. I give each text layer a unique name:

Section1_Header
Section1_Body
Section1_Link

In Google Sheet,

Map a Google sheet row-for-row to each text layer.

Hello Reforge team. A quick note: I’m playing around with an AI voice generator to make narrating this video faster!

This is how Calm uses Google Sheets to build and track emails.

I’ve created a simplified version of our sheet specifically for Reforge. In practice, Calm’s sheets have 70 or 80 rows instead of the 28 rows in this example.

At right you can see a basic newsletter. We break up each piece of the newsletter into chunks that we map to blocks of rows on the Google Sheet.

We replicate this google sheet column multiple times to represent different versions. In this example I’ve layed out six email newsletters. It could be 6 months or 6 weeks. whatever your newsletter cadence.

We also use the spreadsheets to manage newsletter translations for different languages.

Each section of the spreadsheet maps to a section of the email. Starting with the hero section on the email mockup, we have a header, a paragraph body,  and a call to action link. Each of those elements has a corresponding line on the spreadsheet.

We also have three sections that look the same. Therefore we have  three corresponding sections on the spreadsheet.

Finally, we finish with the email section on the bottom.

Now that the copy has a defined structure and pattern, we can interpret the email easily programmatically.

I connected the spreadsheet to a Google Apps Script that shows a basic preview of the newsletter to make the email easier to review and check for errors.

Iterable journeys have two webhook nodes to choose from so I was trying to figure out which node will make an outbound call to an API, receive data, and then write data back into the User Profile.

The “Journey webhook” node will not write data to the user profile.

• This is an outbound-only HTTP request.
• We can use the webhook to tell external certain things about the user that Iterable knows. But the webhook itself is fire-and-forget.

We are forced to use the “User Profile update” node

• Iterable automatically stuffs a ?email=<email address> parameter on every request (security prob?)
• This node allows for an Authorization header.
  • Screenshot shows Authorization: abcdefg
  • We can do Authorization: Bearer <token> to do basic auth instead of authorization token?

Example request header from User Profile update via webhook

GET /?email=exampleemail@foobar.com HTTP/1.1
Host: <HOST>
User-Agent: AHC/2.1
Accept: */*
Authorization: <STRING>
X-Forwarded-For: <IP ADDRESS>
X-Forwarded-Host: <HOST>
X-Forwarded-Proto: https
Accept-Encoding: gzip

Calm was the subject of a email UX teardown recently which was well-written and had several interesting insights. The obvious first reaction to this post was “oh no, we’re horrible” but the reality is she articulates several weaknesses that the team has been aware of.

We missed an opportunity for personalization

Complaint – She was a happy user churning out of the product. When she churned we hit her with a generic urgent messaging that others recognized not to be soothing. Why are we talking to her like a beginner?

What is the root of the complaint? We don’t distinguish “happy churn” from other types of churn. (Note to self: what are the other types of churn?) Our churned-user experience is certainly under-appreciated.

This user has multiple email addresses: a paid subscription email and a trial churned email. Presumably she used the trial churn email to conduct her research.

The paid subscription email address was unsubscribed from marketing emails. She received transactional emails only. This underlines that we should sneak in some more light marketing into our transactional emails.

What technology do we need to solve this? Lots of people have multiple email addresses. It’s not a technical issue. However we could benefit from having consistent time-based email cadence (use as a default) augmented by an event-based cadence when users are well engaged.

Is there a barrier to fixing it? No barriers. We have the capability to look at the user’s engagement within the last several months or weeks to separate out an engaged user from unengaged users — at all times!

Inconsistent email cadence

Complaint – she experienced an email dead zone where she didn’t see anything messaging from us for weeks (months?)

What is the root of the complaint? There was a dead zone for several weeks after churn from trial. She eventually got enrolled into a monthly email but monthly may not be enough frequency to maintain engagement.

What technology do we need to solve this? Increase production

Is there a barrier to fixing it? Emails are too difficult to specify right now.

Off-brand copywriting

Complaint – The author and several LinkedIn commenters perceived the emails to be aggressive and ironically not “calming”

What is the root of the complaint? She received several sale-oriented headlines during a large revenue-producing campaign.

What technology do we need to solve this? No new technology necessary to write copy. Slow down and work cross-functionally to capture critique from marketing & content teams to get on the same page for user-facing communication.

Is there a barrier to fixing it? We are not trained copywriters. However, we can learn to write in an on-brand voice. That is a marketable skill that can be augmented with AI.

Referring back to the personalization problem, we could come up with alternate subject lines for well-engaged users who do not need urgency-oriented messaging.

I had to add a disclaimer block to some messaging so that users don’t mistakenly look for coupons on Apple or Android.

I suspect this line of text caused some of the drop.

“This discount is valid only for subscriptions purchased through [domain name]. Offer not valid for subscriptions purchased in the Apple Store or Google Play Store.”

I attended Activate in San Francisco held at the Marriott Marquis. It was a fine time. I gravitated toward high-level strategic talks in-person. I take a look

Most insightful talk: Women Language + Power and Standard Communication by Susannah Baldwin

When I walked into the room vaguely labeled “professional development” session I was late so I hurriedly took a seat at the side of the room. After I got settled and composed I realized I was one of two men in a room of one hundred women. Ooops! I felt way out of place but I stayed in the room and happy I’m happy I did.

The speaker’s point was that women are taught at an early age to speak in a soft or indirect manner. To reach the upper echelons of But that’s not

Many Useful takeaways : Find Your Target: Reaching Different Personality Types Without Flooding Your Marketing Channels by Tim Hemingway, Havas Media

Broadening my horizons: Presenting with Impact by Susannah Baldwin