Getting Stuff Done

• Basecamp - Project and Life Management

• Drafts | Where Text Starts - Digital Inbox

• Toggl - Time tracking

• Rocketbook | Erasable notebooks and 3x5 cards - Analog Inbox

• Insight Timer - Mindfulness meditation

AI

Frontier Models

• Anthropic Claude Pro

• Google Gemini Pro

Open Models

• Ollama Cloud

• Mistral

Harness

• Claude Code

Chat Interfaces

• Claude Desktop

• Duck.ai

Database

• Neo4j / AuraDB Graph

• Neon Serverless Postgres

Development

• VS Code - Code editor

• Typora - Text editor

• Docker - Virtualization

• Github - Versioning and code hosting

Changelog

• April 16th, added Toggl

• April 15th, 2026 Added Productivity tools

• April 15th, 2026 Created

First, you have the assessment apocalypse. Teachers who relied on take-home essays to determine if a student had learned the material were met with a rude awakening when ChatGPT gave pupils the power to meet the requirements with little more than a prompt.

There is also the problem of productive struggle. Neuroplasticity, the ability of our brain to build new neural pathways, requires effort and challenge. In a society that is chronically stressed and built to reduce friction, AI has been a welcome shortcut. But for many people it shortchanges the work necessary to learn new things.

Professors and pundits see this as a threat to education and are cautioning the public to not learn this way.

Their chicken-little-intoned message that the academic sky is falling sounds like too little, too late for this former teacher. The industrial model of instruction and assessment hasn’t been working for many learners for a long time. The numbers prove it. The biggest fallacy is the belief that all people learn the same.

I know because traditional education didn’t work for me.

And AI definitely does.

I am lucky to be metacognitive and able to think about how I think. And think about how I learn.

About six months of deep learning with AI, and hearing the doom of academics about why I shouldn’t be learning with AI, I started exploring why it works for me, and not others.

I do, we do, you do

This formula is how you teach a new practice.

Imagine learning for the first time how to change a car tire.

You unload the car jack from the trunk, along with the spare tire. Position the jack under the car, lift it, and then remove the bolts securing the wheel until you can remove the wheel and replace it with the spare.

I can do that on my own.

But I first learned by watching my dad do it. At some point, he did parts of it, and I did other parts. Then eventually he watched me do it. And today I do it on my own.

That same process happens when we teach students how to multiply 2 digit numbers by a single digit. The teacher stands at the white board, and walks step by step through the algorithm. Children dutifully copy what the teacher is doing.

Over time, teachers watch students while they do it, and work out the problem together. Until eventually, they are able to finish it on their own.

I do (teacher), we do (class), you do (individual completes it on their own.)

You do, we do, I do

When I reverse engineered how I learn with AI, I discovered I had inverted this process.

A year ago, I used Claude to analyze the data of my travel Substack Zigzag Along with Deana & Jeff. You can learn about the process in this article.

When I started, I didn’t know anything about SQL, ETL or data analytics. I just kept asking questions, and orchestrating my agent to do the tasks. Together we installed MCP servers for python, SQLite, pandas, and more. It wrote the code, built a database, queried it, and provided results.

You do. Claude did all the work. I watched. I was eating tokens and put in timeout like there was no tomorrow.

Then a friend asked:

But how do you know it’s answers are accurate?

And I went off to learn about the technologies that underlie the processes.

The next project was working on analytics for my Linkedin profile. This time, I was more careful about which MCP servers I installed. I upgraded to Postgresql, learned how to use Docker, and installed NocodeDB and Metabase for visualizations.

In the first you do phases, Claude was constantly rewriting the code. This time I installed VS Code, and was looking at what we were doing together.

We do. I researched and learned how to use the technologies that Claude used to build our ETL and analytics process. Token consumption was more modest. Fewer timeouts.

Today, I can install Docker images on my own. I modify files at the terminal without asking for help. I am learning Neo4j for the next database project. And I am rarely talking with Claude about it.

I do. My paid Claude account has actually expired, and I haven’t yet paid for the renewal.

The Future

This inversion of the typical pattern will work under these conditions:

• We have to legitimize productive struggle. Teachers and schools need to design environments that reward resiliency, rather than attendance and task completion.

• We must teach critical thinking. Everyone says this. It’s not happening at scale.

• We must accept that not all people learn the same way. I do not presume that because I can use AI in this inverted manner everyone else can and should. But the education system assumes that people like me should learn how everyone else does.

What about you? How do you learn? I’d love to hear from you. Leave a comment or DM me.

Subscribe now

My name is Jeff Long. I am a digital nomad currently living in Washington State, Albania, Cyprus, Greece, Italy, Bulgaria, Georgia, Switzerland, Des Moines, Iowa. I build with technology, and teach people how to create meaningful lives in the AI age.

Futures Past

Since as long as I can remember, I’ve been thinking about the future. Maybe it was the Buck Rogers comic books, or the Radio Shack electronics I grew up with. And as long as I can remember, I have had a computer around me. When I was 10, it was a Bally Astrocade video game console with a cartridge that allowed me to program in the Basic language.

The Recent Past

This combination of technology plus future-tense mindset served me well in 2020.

During the spring of my 20th year as a teacher, I was paying close attention to what was happening with Italy. So close, that I learned how to rewind Google News day-by-day and collect numbers on the spread of Covid, and look for what tipping point caused Italy to make the dramatic decision to close schools.

I created a spreadsheet with my numbers, and lined up the dates, and came to a sober conclusion. The next day on the playground, I told my fellow teachers: “We aren’t coming back to school after this weekend.” No one could believe it. But the numbers didn’t lie.

The pandemic changed everyone. How did it change me? Two years later, I boarded a plane to Europe. I quit my job, sold everything, rented out my house, and bought a one-way ticket to Corfu, Greece. There I boarded a ferry and spent three months in Sarande, Albania with my wife .

What was I going to do? I didn’t know yet. But I knew that my future did not include public school teaching. We went on from Albania to Cyprus, then Greece, and ended at the largest digital nomad festival in the world: Bansko, Bulgaria.

Being Present

I learned a lot on that first 10 month trip, living in Europe. One of the most important was starting to meditate.

At first it was a 3-minute exercise of breathing in and out, following the rhythm set by the meditation app on my Apple watch.

Then I progressed through the 7-day guided lessons on the Insight Timer app. 1 By the time I got to Cyprus, I was practicing daily lessons led by Jack Kornfield and Tara Brach.

Today, my practice is about an hour long, give or take. It includes both Vipassana style mindfulness, Orthodox Nepsis, and religious reading.

The Future

The other big change that happened in Albania was my first experiments with ChatGPT. It was terrible then, but it was hinting at what was to come.

Like other humanities professionals, I found I had a latent capability to use AI, because it’s primary language is human language, unlike previous computer programming. Proper word choice, grammar and syntax can make AI sing.

I spent the next years growing in my AI abilities, first with ChatGPT, and then Google Gemini.

When I returned to Albania last year, I met a new friend who introduced me to Anthropic Claude, and suddenly I was building databases, running analytics, and creating apps.

Being Future Present

Like everyone else, I believe our economy is being transformed by this technology so growing in our skills will help us navigate this new world.

Aside from the opportunities, there are massive uncertainties that everyone is feeling, myself included.

Here I found the topic I wanted to pour myself into:

• The application of mindfulness with the uncertainty of our future.

We can no more wish away an unknowable AI future than we can wish away a storm.

You can prepare for a storm. But if you spend all your energy on the storm, you miss the magic of the present moment.

We must learn to be future present.

I have three themes to guide me:

1. Being. When you see photos of me in museums, beaches, with family and friends, you are seeing me being. When I talk about meditation, prayer, or reading, I am talking about being. And I will encourage you to do lots of it.

2. Thinking. We are living during a crisis of meaning. Not only do we wonder about the purpose of life in the AI age, but we wrestle to collectively define the meaning of common words. We need to not just Know Thyself, but also work together towards a shared common future. This requires thinking. Lots of it. I will guide you through some of the topics we need to think about in order to do a better job at being.

3. Doing. The ever-presence of digital devices has created an unprecedented degree of distraction leading to an epidemic of attention dysfunction. As a digital nomad and independent contractor, I have had to learn to make decisions about what to do next while fighting the urge to pick up my phone. Claude and Gemini have become superpowers for me, but they are not without their own peril. I am not interested in productivity for the sake of improving my efficiency and increasing the corporate bottom-line. I will talk about how I build with AI and use other tools to help me accomplish meaningful work which frees me up to think and be.

I hope you will join with me Being Future Present

Subscribe now

Lost with the Wrong Map

My wife and I have been digital nomads since 2022, living for months at a time in countries across Europe.

Early on, we learned a hard lesson about navigation. In unfamiliar cities, we relied on Apple Maps and often ended up lost down narrow alleys or facing unexpected dead ends.

Frustrated by wrong turns, we switched to Google Maps and immediately felt the difference – having the right map makes all the difference.

But Even the Right Map Can be Misread

Once, while wandering through Nicosia, Cyprus, we followed Google’s walking directions toward what looked like a normal street. As we got closer, we realized we were approaching a dotted line on the map – the kind you usually ignore.

Suddenly, we stumbled upon a border crossing! (Turkey occupies the northern part of Cyprus, including part of the capital city.)

We hadn’t intended to leave the country that day, but there we were at a checkpoint.

The Newsletter Map

In the world of newsletters, reading analytics can feel like navigating a strange city. Many Substack writers obsessively check the basics on their dashboard: open rates, subscriber counts, maybe the number of likes or shares. These are your maps.

But if it’s the wrong kind of map (or an incomplete one), you’ll miss the turn-offs and borderlines that matter.

Relying only on Substack’s default analytics is like navigating a complex city with a simple tourist map. You see a few big landmarks, but miss the critical details.

And risk taking some wrong turns with your strategy.

The Limits of Substacks’ Basic Metrics

Substack provides a solid starting set of numbers. On your dashboard, you can find your total subscribers, recent growth, post views, email opens, likes, and even the number of new subscribers each post brought in.

Those are important “landmarks” – much like the famous monuments on a city map.

However, there are significant blind spots in these basic metrics that can lead you in the wrong direction:

• No early warning for churn: Substack doesn’t tell you which subscribers are fading away. You usually find out someone churned after they unsubscribe. There’s no alert for “hey, these 50 people haven’t opened an email in two months.”

• No identification of conversion-ready readers: Who among your free subscribers is most likely to upgrade to paid? Substack’s default tools won’t highlight your superfans. You can see overall open rates, but not a list of “these 10 people who always open and click.”

• No link between content and outcomes: While you can see per-post views and some subscriber gains, it’s not clear which content truly drives your business goals. You can’t tell whether your in-depth tutorial brought in more revenue than your personal story, or vice versa.

In short, the default map of data has big holes.

So, how do we fill them?

By drawing a more detailed map.

Digital Cartography – Mapping Your Newsletter Data Landscape

Think of advanced analytics as digital cartography for your newsletter. It’s the process of mapping the landscape of your audience data in detail.

Instead of just seeing “open rate: 45%” as a single number, you map out which readers open most and which rarely do. Going beyond the surface to chart things like:

• Engagement territories: sections of your audience and content where engagement is especially high or low.

• Conversion pathways: the typical routes by which a casual reader becomes a paying subscriber (maybe they read 5 free posts, then a particular article convinces them to upgrade).

• Risk regions: warning signs in your audience (e.g., longtime subscribers who suddenly stop opening emails).

• Opportunity zones: untapped potential, like an audience segment that loves one content type that you could offer a paid product for.

It might sound abstract, but in practice it’s about using the data you already have (emails, opens, clicks, subscriber history) and overlaying it with insight, much like how a topographical map shows you not just the roads but the elevation, the rivers, the forests. You get a richer view of the terrain. And a richer view means better decisions.

To illustrate, let’s navigate three key “landmarks” on this data map that every newsletter author in the 200–1000 subscriber range should be watching.

1. Dormant Subscriber Territory

Spotting At-Risk Readers

The first analytics move every Substack writer should make is to identify dormant subscribers, especially dormant paid subscribers. These are people who were once active but have quietly stopped engaging. In this territory lie your highest-risk cancellations. After all, most subscribers don’t wake up one morning and randomly hit “unsubscribe.” Churn is usually the end of a gradual disengagement journey.

Common signs you’ve entered the Dormant Subscriber Territory include:

• Decreasing open frequency: A reader used to open every email, but now they open one in three.

• Delayed opens: They used to read your emails immediately after publishing; now, they sit in their inbox for days.

• Partial reads: They aren’t reading to the end anymore.

🧭 Pro Tip #1: How to Spot Churn Risk in Google Sheets

Substack won’t flag disengaged readers, but you can build your own early warning system.

Step One: In your subscriber dashboard, click the three dots in the top-right corner. Choose "Export," then select "Export all columns" and download the .csv file.

Step Two: Import the file into Google Sheets.

Step Three: Sort or filter by the “Email Last Opened At” column.

Step Four: Add a column to flag subscribers who haven’t opened in 30–60 days.

Bonus: Use conditional formatting to color-code churn risk at a glance.

In minutes, you'll have a simple visual dashboard showing who’s quietly slipping away—before they unsubscribe.

2. Superfan Highlands

Finding Your Conversion Gold Mines

On the flip side of the disengagement zone is a very exciting place: what I call the Superfan Highlands. These are the peaks in your audience – the readers who can’t get enough of your content. Every newsletter has a subset of subscribers who are deeply engaged. Identifying them is like finding veins of gold in your mountain – they are your best candidates to become paid supporters (or to buy whatever you offer next).

Signs you’re in the Superfan Highlands:

• They read everything: Long posts, short posts, off-schedule posts – a superfan doesn’t miss a beat. If you experiment with a new format or send a surprise email, they’re the first to open it.

• Multiple touches: Perhaps they open the email, and click through to read on the web, and even read it again later. Maybe they read on their phone, then again on their laptop. That indicates the content resonated enough that one read wasn’t sufficient (or they wanted to digest it fully).

• They engage beyond reading: These are the folks who leave thoughtful comments, hit the “like” button, share your post on Twitter, or even email you replies. They may also follow you closely on Substack’s Notes or other platforms.

• They dive into archives: A superfan who discovered you recently might go back and read a bunch of your older posts. If you suddenly see older posts getting views, that’s often a superfan bingeing on your content library.

🧭 Pro Tip #2: Identify Your Superfans

Want to know who’s most likely to convert to paid—or who deserves a personal thank-you?

Step One: In your subscriber dashboard, click the three dots in the top-right corner. Choose "Export," then select "Export all columns" and download the .csv file.

Step Two: Import the file into Google Sheets.

Step Three: Sort by the “Emails Opened” column to find your most active readers.

Bonus: Look for long-time free subscribers who stay highly engaged—that’s upgrade potential.

You’ll uncover the core audience who’s already leaning in. These are your newsletter’s true believers—and your best bet for paid conversions, feedback loops, or early access offers.

3. Content Impact Valleys

Unearthing Hidden Gems in Your Posts

Not all content is created equal. Some of your posts will soar (high open rates, lots of shares), and some will flop – that’s normal.

But there’s a tricky phenomenon I’ve noticed in my data: sometimes a post that looks like a flop initially can turn into a quiet success over time. If you’re only watching the immediate metrics, you’ll miss it.

I call these the Content Impact Valleys – low spots that eventually reveal an upward slope.

Let me share a quick personal story. In my travel newsletter, Zigzag Along, my wife once wrote a heartfelt piece about a 70-year-old woman we met on the island of Corfu. This woman happened to be the granddaughter of a local folk hero, and we even took a selfie with her.

Zigzag Along

☕️Coffee in Corfu 🇬🇷 with the granddaughter of the REAL Spiros Americanos from 'The Durrells'

Some travel moments simply can't be planned…

Read more

a year ago · 4 likes · Deana

We loved the story, but when we published that issue, the email open rate was disappointingly low. Honestly, I thought that post didn’t resonate, and I mentally filed it under “oh well, they can’t all be hits.”

Now fast-forward a month. I’m testing some of our new Stack&Scale analytics tools. I look at the past 3 months of post-performance, and I nearly spit out my coffee. That Corfu story? It had become our third-highest viewed post ever!

How on earth…?!

It turns out, over the weeks, many subscribers who hadn’t initially opened it went back and read it later. Perhaps via the archive or because I mentioned it in a follow-up. Plus, some folks shared it around because of the unique story, and new readers checked it out.

The lesson for me was huge: you don’t always know right away what content is working best. Some posts need time to “bake.” If I had only looked at the first 48 hours, I’d have dismissed that article as a miss. The deeper analysis showed it was a slow-burning hit.

Turning Insights into Action

At this point, you might be thinking, “Okay, great – identify who might leave, find my superfans, analyze my posts over time… but what do I actually do with all this?”

The real power of analytics comes when you act on the insights. A detailed map isn’t useful if you don’t plan a better route, right?

Here are a few strategic moves you can make once you have this richer data map of your newsletter:

Launch a retention campaign for at-risk subscribers: Remember those at-risk readers in the Dormant Territory? Don’t wait for them to churn! Set up a simple win-back email sequence or personal outreach.

For example, send a gentle note along the lines of “We miss you – here’s what we’ve been up to in case you’d like to catch up.” Highlight a few recent posts they might like.

Nudge your superfans toward conversion: Take that list of highly engaged free subscribers. These folks deserve VIP treatment. Consider a special offer or just a direct ask: “You’ve been reading us for a while – if you enjoy this, we invite you to support us and get a discount.”

You might give them a promo code or discount for the first year of paid, as a thank you for being loyal. Or invite them to a live Zoom call or AMA for subscribers – something that makes the value of upgrading clear and enticing.

The big picture: Newsletter growth isn’t just about getting more people in the door. It’s about understanding the people you already have – seeing the full map of your audience’s journey – and making informed moves to keep them happy and bring them closer to your goal (whether that’s paid subscriptions, course sales, Patreon support, etc.).

When you have that complete picture, you’re no longer navigating blindfolded.

You know where the winding paths and hidden bridges are in your audience, and you can lead your readers (and yourself) to where you want to go.

Your Data Map, Done for You

By now, the advantages of deeper analytics are clear, but you might also feel a bit overwhelmed. Mapping data, exporting CSVs, spotting patterns… it can sound complex or time-consuming, especially if you’re already writing every week. The good news? You don’t have to do this alone or build tools from scratch.

This is exactly why I founded Stack&Scale.

I’m a data nerd at heart. I love uncovering the hidden stories in subscriber data. I want to make it simple for other writers to do the same. We’ve built an AI-powered analytics service for Substack writers in the 200–1000 subscriber range (yep, right where you are).

What do we do? We take your Substack data (with permission) and run it through advanced analytics, the kind big companies use, tailored for indie creators. Then we hand you a report full of actionable insights: who’s fading, who’s engaged, what’s working, and where your best growth paths lie.

But we don’t just dump data—we give you strategy. Our goal is for you to walk away feeling clear and confident about what to do next.

Right now, you can get our Comprehensive Substack Accelerator Analysis for just $149 (beta pricing). You'll receive your complete analysis within three business days, including:

• Comprehensive 5-7 page analysis of your newsletter metrics

• AI-powered growth recommendations

• Subscriber engagement patterns and opportunities

• Content optimization strategy

• Custom re-engagement plan for at-risk subscribers

• 30-minute consultation to review findings

If you’re ready to stop wandering and start navigating your newsletter with precision, let’s do it.

Get Your Substack Analysis