That gap is closing. Fast.

What’s happening right now with tools like Lovable, Replit, and a dozen others isn’t just “coding got easier.” That framing misses the point entirely. It’s more like what happened when YouTube showed up. Before that, reaching a mass audience with video meant you needed a TV network, a production crew, a distribution deal. Then suddenly, one person with a camera and an internet connection could reach more viewers than a cable channel. The content didn’t change. Who got to publish it did. That’s the moment we’re in right now, except the thing being democratized isn’t video. It’s software.

The cost of building functional software is approaching zero. Not as a metaphor. As an economic reality. You can sit down today, describe what you want to build, and have a working application, frontend, backend, business logic, payment integration, in hours. Without a technical co-founder. Without a seed round. Without a team standup at 9am. The thing that used to be the bottleneck isn’t the bottleneck anymore.

And when that happens, the entire logic of the game changes.

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The traditional startup model was essentially a high-stakes poker hand. You go all in on one idea, you spend years building it, you raise money to buy yourself more time, and you hope, you really hope, that you find product-market fit before you run out of runway. Most don’t. And even the ones that do often spend a decade of their life getting there, burning through relationships, health, and whatever version of themselves existed before the company consumed them. It always felt like the only way to play the game. But it was never the only game.

What’s emerging now looks completely different. Instead of one enormous bet, you can make ten small ones. Hyper-niche apps. Specific problems for specific people. A tool for independent real estate agents to automate their follow-up sequences. A simple reporting dashboard for small franchise owners who are drowning in spreadsheets. A client portal for boutique law firms that still run on email threads and PDFs. None of these are billion-dollar ideas. That’s the point. You don’t need a billion-dollar idea. You need a real problem, a group of people who feel it acutely, and a solution that’s good enough to be worth paying for.

Five products at a thousand dollars a month each is five thousand dollars a month. Ten is ten thousand. That’s not a unicorn. But it’s a life. A genuinely good life, built on your own terms, without a board, without investors, without permission from anyone.

Here’s what’s worth being honest about, though: this isn’t passive income in the romantic sense. You’re not going to build something on a Tuesday afternoon and retire by Friday. These products need maintenance, iteration, user support, the occasional 11pm bug fix. But the shape of the work is entirely different from what building a startup used to look like. You’re not managing people. You’re not in all-hands meetings. You’re not writing quarterly OKRs for a team of twelve. You’re one person, moving fast, staying close to your users, improving things incrementally. It’s more like tending a garden than running a factory. And for a lot of people, that distinction matters enormously.

There’s another consequence that nobody seems to be talking about loudly enough yet: what this does to the existing SaaS market. Think about it from a buyer’s perspective. Right now, you’re paying fifty dollars a month for a generic tool that does 60% of what you need and 40% percent of what you don’t. It was always a compromise. You accepted it because there was no alternative. But what happens when someone builds the specific version of that tool for your exact use case, charges 1/10 the price, and genuinely understands the problem because they lived it? The generic, horizontal SaaS products aren’t going to disappear overnight. But the pressure on them is about to become relentless. The defensibility of “we do everything for everyone” erodes fast when someone else can do exactly your thing, better than you.

But none of that is the most interesting part.

The most interesting part is what actually separates the people who will win in this era from the people who won’t. And here’s the uncomfortable truth: it’s not who can build the fastest. The barrier to building is almost gone. Which means the new barrier is taste.

Anyone can vibe-code a tool that technically solves a problem. Very few people can build something that people actually love using. That gap, between a functional product and a remarkable one, is exactly where skills like product discovery, user research, and product intuition start to matter more than ever. Knowing how to talk to users before you build a single screen. Understanding not just what people say they want, but what they actually need. Designing an experience that makes someone think “finally, someone gets it” instead of “well, it works, I guess.” These are not soft skills. They are the competitive advantage of this era.

A great PM, someone who has spent years thinking about how products actually work inside a company, is not at a disadvantage here. They are at a massive advantage. Because they already know what makes a product go from zero to something people genuinely recommend. They know how to talk to users before writing a single line of code. They know how to prioritize what actually matters versus what just feels urgent. They know the difference between a feature and a solution. They just never had a way to execute on that knowledge alone. Now they do.

And here’s the thing: those skills are learnable. Product thinking isn’t a superpower reserved for people with a specific job title. But the people who are going to move fastest are the ones who already have some version of it sitting dormant. The person who spent years running operations at a mid-sized company, managing vendors, fixing broken workflows, dealing with the friction nobody else wanted to touch. They already understand the problem at a level most builders never will. They know the business, they know the pain, and now the only thing that was missing, the ability to actually build, is no longer missing. They don’t need to start from zero. They just need to wake up what they already know.

That shift in who gets to build will change what gets built. And what gets built will change what’s possible for the people who use it.

So here’s the question worth sitting with. Not “is this real?” It’s real. Not “will this affect my industry?” It will. The question is whether you’re going to be the person who builds the thing, or the person who, two years from now, pays for the thing someone else built because they moved first.

The tools exist. The moment is here. The only thing that’s actually missing is the decision to start.

The most common critique? Many of these apps are weak on security. They can be hacked, they are fragile, and they don’t scale. All true. But here’s the reality: this is the worst that Vibe Coding will ever be. From here, it only gets better.

My own MVPs were fragile too

At Global66, our first MVP was nothing more than a landing page, a Google Sheet, and Zapier connecting it all. It wasn’t secure. It didn’t follow any standards. But it answered the real question: is there water in the pool? Do people care about this problem?

Same story at Cabify. Many prototypes were far from robust. What mattered was validating a hypothesis, finding product–market fit, and proving we could solve a problem quickly. Security and scale only became relevant once we knew there was something worth building.

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This fear is nothing new

Every breakthrough technology goes through the same cycle. Experts in the old system focus on weaknesses of the new one and miss its exponential improvement curve.

• Fax users said the internet would never replace it. Too slow. Not reliable enough for business.

• BlackBerry fans dismissed the iPhone. Too insecure. No keyboard. Not for real work.

• Professional photographers laughed at early digital cameras. The quality was terrible. Now film is niche.

• IT managers resisted the cloud. Putting company data on someone else’s server? Impossible. Today it’s the standard.

• Academics mocked Wikipedia. “Anyone can edit it, so it can’t be trusted.” Today it’s the world’s go-to reference.

History repeats. What looks insecure and limited at the start becomes the default.

The market doesn’t wait for perfect standards

Look around. There are tens of thousands of SaaS products that are objectively bad. Weak code. No updates in years. Zero modern security. And yet, they make millions because they solve one specific problem.

Think about basic scheduling tools for dentists or niche vertical SaaS built by a solo dev. They don’t meet any “best practices.” But they work.

The irony is that with tools like Lovable, a vibe coder can now build something more solid than many of those legacy products that still make money today.

Tools are evolving fast

And this “security gap” is not permanent.

• Lovable already includes built-in security checks and connects frontend and backend with fewer vulnerabilities.

• Platforms like Replit make it easy to spin up full environments, deploy, and collaborate.

• Each new release raises the floor. Security and architecture improve by default.

What looks fragile today will soon be safer than much of what was built manually just a few years ago.

The real skill: validation

The most valuable skill right now is not becoming a cybersecurity expert from day one. It is learning how to validate ideas in the real market with real users.

Vibe Coding makes smoke tests easier than ever. You can launch in days, watch how users react, and decide if the idea deserves more investment. That muscle of validation is what builders should be training.

The real risk isn’t that your MVP is insecure. The real risk is never testing if your solution matters to anyone.

Vibe Coding is not anti-code

Another misconception is that vibe coders want to replace programming entirely. Not true.

What changes is the entry point. You no longer need to master a language from scratch to build something meaningful. If you can read code, make small changes, and understand basic logic, you can go far.

WordPress worked the same way. Millions started with templates and plugins, and many ended up learning HTML, CSS, and even full web development. Vibe Coding can play the same role for this generation.

The future is not no-code vs. code. It is builders who combine prompts, product thinking, and enough coding literacy to push further.

The real dilemma

The question is not “is Vibe Coding secure enough today?” The question is whether you are willing to build even when things aren’t perfect.

Because history shows us one thing: the fragile tools of today become the standards of tomorrow.

Stopping Vibe Coding out of fear of insecurity would be as absurd as banning Zapier + Google Sheets MVPs in 2020.

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What You’ll Gain:

• Understand Your Position: See exactly where your company stands in crucial areas like Customer Acquisition Cost (CAC), Lifetime Value (LTV), and retention rates.

• Identify Growth Opportunities: Spot trends and opportunities that you might be missing, helping you stay ahead of the competition.

• Optimize Your Strategy: Leverage detailed insights to refine your marketing, product development, and overall business strategy.

• Take Proactive Financial Actions: Make timely decisions that can boost your company’s financial performance and growth potential

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Transforming your data into actionable insights is crucial for business success. By following a structured process of defining hypotheses, planning and performing analyses, calculating the relative impact, and iterating as needed, you can uncover valuable insights that drive your business forward.

Hello Growth and Product Enthusiasts!

Understanding how to generate actionable insights from your data can be a game-changer for your business. It's a nuanced process, but with a systematic approach, you can transform ambiguous data into clear, strategic actions that drive success. Today, we'll walk through a structured method to help you turn data into valuable insights.

The Insight Generation Loop

Generating insights can be daunting, but by thinking of it as an insight generation loop, you can bring clarity to the process. Here’s a step-by-step approach:

1. Define Your Hypotheses

Your hypotheses are the backbone of your analysis. Here’s how to focus them:

• Prioritize Metrics: Choose metrics aligned with your key results and goals. Focus on areas where you lack information.

• Ask Broad Questions: Start with broad questions about why you aren't meeting your goals.

• Brainstorm Hypotheses: Think of conventional (plausible and realistic) and unconventional (imaginative and creative) hypotheses.

• Narrow Down: Validate that your hypotheses are meaningful, debatable, and testable.

Example: FoodFusion Case Study

John, the Search and Recommendations PM at FoodFusion, aims to improve the search-to-order conversion rate. FoodFusion is a leading food delivery app in the USA, similar to DoorDash and GrubHub. By asking why the search-to-order rate isn’t hitting the 30% target, John brainstorms and narrows down his hypotheses.

2. Plan Your Analysis

Once your hypotheses are defined, plan how to test them:

• Identify Data Sets: Choose the right data sets and ensure they’re reliable.

• Specify Variables: Define your input variables and control for confounding factors.

Types of Analysis:

• Funnel Analysis: Examine user behavior from the initial search to order confirmation. Identify drop-off points.

• Segmentation Analysis: Segment data based on different variables like ETA, price, or ratings to see how they impact user behavior.

• A/B Testing: Test different versions of features or interfaces to see which performs better.

• Correlation Analysis: Determine the relationship between variables (e.g., how ETA affects conversion rates).

Example: FoodFusion Case Study

John and his data analyst, Mike, prioritize data sets for ETA, rating, and surge pricing. They clean and prepare the data for analysis, focusing on sessions with at least one high and one low ETA restaurant to validate user behavior.

3. Perform the Analysis

Execute your planned analysis and interpret the results:

• Establish Baseline: Understand what is "normal" before analyzing.

• Run Analysis: Execute the analysis and capture the outputs.

• Interpret Results: Determine if your hypotheses are supported by the data.

Detailed Analysis Steps:

• Funnel Analysis: Mike sets up a funnel analysis to understand where users drop off in the process. He looks at the conversion rates from each step: search, view results, add to cart, review order, and confirm order. This helps identify where the biggest issues lie.

• Segmentation Analysis: Mike segments the data by ETA and surge pricing. For ETA, he creates bins (e.g., 0-10 minutes, 11-20 minutes, etc.) and analyzes how each impacts the likelihood of adding items to the cart and confirming an order. For surge pricing, he does a similar segmentation to see how price changes affect behavior.

• A/B Testing: John runs tests to see if showing ETA directly in the search results improves conversion rates compared to the current setup where ETA is only shown on the restaurant page.

• Correlation Analysis: Mike performs a correlation analysis to see how different factors (e.g., restaurant ratings, price, ETA) correlate with conversion rates. This helps identify which factors have the strongest impact.

Example: FoodFusion Case Study

Mike conducts a funnel analysis to establish a baseline and then performs segmentation analysis on ETA and surge pricing. The results show that higher ETA and surge pricing negatively impact conversion rates.

4. Calculate the Relative Impact

Quantify the significance of your findings:

• Measure Impact: Assess the practical significance and how it affects key metrics.

• Contextualize Results: Ensure your findings apply to relevant user segments.

Steps for Calculating Impact:

• Define Denominators: Determine the total user base or sessions your findings apply to.

• Quantify Changes: Calculate the potential increase in conversion rates or other key metrics based on your findings.

• Segment Analysis: Ensure the results are relevant across different user segments (e.g., new users vs. returning users).

Example: FoodFusion Case Study

John and Mike calculate the potential impact of improving ETA visibility on the search-to-order conversion rate. They project a significant increase in confirmed orders by prioritizing restaurants with ETAs ≤ 40 minutes.

5. Iterate as Needed

Revisit and refine your hypotheses and analysis:

• Adjust Hypotheses: Based on your findings, refine your hypotheses.

• Correct Flaws: Identify and correct any issues in your analysis.

• Explore Further: If the analysis is ambiguous, pivot to new hypotheses or different metrics.

Recap

• Define Hypotheses: Focus on key metrics and brainstorm both conventional and unconventional explanations.

• Plan Analysis: Choose reliable data sets and define clear variables.

• Perform Analysis: Establish a baseline, execute the analysis, and interpret the results.

• Calculate Impact: Quantify the significance and ensure applicability to relevant segments.

• Iterate: Refine and repeat the process to deepen your insights.

With this structured approach, you can unlock the hidden potential in your data, turning it into actionable insights that drive your business forward.

Stay Curious,

Sebastián.

Navigating the world of marketing measurement can feel like trying to solve a puzzle with pieces that don’t quite fit. Many marketers face distrust in marketing data and constant second-guessing from executives. To make things easier, I’ve broken down the key methodologies that can help measure marketing efforts accurately and effectively.

First, let's get familiar with some essential terminology: attribution, incrementality, and modeling. Attribution is about identifying what caused a specific action. Incrementality measures the additional impact of marketing efforts. Modeling involves creating a representation based on data. Understanding these concepts is crucial for choosing the right measurement approach.

Now, let’s dive into the different types of marketing measurement methodologies, each with its own pros and cons.

In-Platform Attribution

In-platform attribution uses tools like Facebook Pixel or Google’s conversion tracking to understand how ads are performing. These tools rely on pixels and tags that track user actions on websites. Here’s how it works:

1. Pixels/Tags: Small pieces of code embedded on your site that track user interactions. For example, the Facebook Pixel tracks actions taken by users after clicking on an ad, such as adding items to a cart or completing a purchase.

   Pros:

   • Quick and easy to implement.

   • Provides immediate data on ad performance.
     

   Cons:

   • Ad blockers can prevent tracking.

   • Privacy settings may interfere.

   • Often operates like a "black box," making it difficult to understand the attribution process.
     

2. Conversion APIs: These allow for server-side tracking, bypassing some limitations of pixels. They provide a more reliable data flow by directly sending conversion data from the server to the ad platform.
   

   Pros:

   • More reliable than client-side pixels.

   • Works even with ad blockers or restricted cookie settings.
     

   Cons:

   • Requires more technical setup.

   • Privacy policies might limit data sharing.

Multi-Touch Attribution (MTA)

Multi-Touch Attribution (MTA) gives credit to multiple touchpoints in the customer journey. Unlike single-touch models, which attribute a conversion to the first or last interaction, MTA considers all interactions that led to a conversion. Here’s a closer look at MTA models:

1. First-Touch Attribution: Attributes 100% of the credit to the first interaction a user had with your brand.
   

   Pros:

   • Simple to understand and implement.

   • Useful for identifying top-of-funnel activities.
     

   Cons:

   • Ignores the rest of the customer journey.

   • May undervalue important mid-funnel and bottom-funnel activities.
     

2. Last-Touch Attribution: Attributes 100% of the credit to the last interaction before conversion.
   

   Pros:

   • Highlights the final step before conversion.

   • Easy to track and implement.
     

   Cons:

   • Ignores the influence of earlier interactions.

   • Can misrepresent the effectiveness of initial touchpoints.
     

3. Linear Attribution: Distributes credit equally across all touchpoints in the customer journey.
   

   Pros:

   • Provides a more balanced view of the customer journey.

   • Recognizes the contribution of all touchpoints.
     

   Cons:

   • May over-credit minor touchpoints.

   • Doesn't account for the varying impact of different interactions.
     

4. Time-Decay Attribution: Assigns more credit to touchpoints that occurred closer to the conversion.
   

   Pros:

   • Recognizes the increasing importance of interactions closer to the conversion.

   • Balances the contribution of early and late touchpoints.
     

   Cons:

   • Can be complex to implement.

   • May still undervalue initial touchpoints.
     

5. U-Shaped Attribution: Gives more credit to the first and last touchpoints, with the remaining credit distributed among middle touchpoints.
   

   Pros:

   • Emphasizes the importance of first and last interactions.

   • Provides a nuanced view of the customer journey.
     

   Cons:

   • May undervalue the middle touchpoints.

   • Complex to set up and interpret.

Self-Reported Attribution

This method involves asking customers directly how they heard about you, often through surveys. While it bypasses some tracking issues, it introduces biases:

1. Recency Bias: Customers may remember recent interactions more clearly than older ones.

2. Honesty Issues: Customers might not remember or report accurately.

3. Limited Scope: Surveys often don’t capture the complete customer journey.

Despite these limitations, self-reported attribution can provide valuable qualitative insights, especially when combined with other methods.

Marketing Mix Modeling (MMM)

MMM uses statistical analysis to measure the impact of various marketing activities over time. It considers both online and offline channels, providing a comprehensive view. Here’s what MMM involves:

1. Data Collection: Gather historical data on sales and marketing activities.

2. Model Building: Use statistical techniques to correlate marketing activities with sales.

3. Analysis: Break down sales into baseline (sales without marketing) and incremental sales from marketing.

4. Forecasting: Predict future sales based on different marketing spend scenarios.

Pros:

• Provides a holistic view of marketing impact.

• Includes offline channels.

Cons:

• Requires extensive historical data.

• Not suited for real-time decision-making.

Incrementality Testing

Incrementality testing involves running controlled experiments to isolate the effect of marketing efforts. By comparing a test group exposed to marketing with a control group that isn’t, the true impact can be determined.

1. Hypothesis Development: Define what you want to test (e.g., the impact of a new ad campaign).

2. Audience Segmentation: Split your audience into test and control groups.

3. Execution: Run the marketing activity only for the test group.

4. Analysis: Compare results between the test and control groups to measure the incremental lift.

Pros:

• Provides clear insights into marketing impact.

• Scientifically rigorous.

Cons:

• Requires careful planning and statistical expertise.

• Can be resource-intensive.

Choosing the Right Methodology

The best methodology depends on specific goals and resources. Here’s a quick guide:

• Use in-platform attribution for quick insights but be mindful of its limitations.

• Implement MTA for a broader view of customer journeys, though it may not capture all touchpoints accurately.

• Opt for self-reported attribution to gather direct feedback, while accounting for biases.

• Leverage MMM for strategic, long-term insights, especially if you have substantial historical data.

• Conduct incrementality testing for precise, scientific measurement of marketing impact.

Practical Application

Let’s say you’re tasked with proving the value of marketing campaigns to skeptical executives. Start with in-platform attribution to get immediate data on ad performance. Then, layer in MTA to understand the customer journey better. Supplement this with self-reported attribution for direct customer insights. Use MMM to analyze long-term trends and inform strategic decisions. Finally, run incrementality tests to validate findings with scientific precision.

Conclusion

Effective marketing measurement isn’t about choosing one method over another; it’s about combining them to get the most accurate picture. Start by assessing your current measurement confidence and explore these methodologies to enhance your strategy. By integrating these approaches, you’ll be better equipped to demonstrate the true impact of marketing efforts and make data-driven decisions.

Ready to take marketing measurement to the next level? Dive into these methodologies and transform your strategy today. Accurate, actionable data is within reach – let’s get started!

What is LTV?

Picture this: You're sitting in your office, looking at a list of all your customers. What if you could know exactly how much each one of them would contribute to your business over their entire relationship with you? That’s LTV. It’s the total value a customer is expected to create for your organization over their lifetime. Think of it as a guiding star for growth strategists, influencing decisions on marketing spend, product development, and customer retention strategies.

Understanding LTV: The Building Blocks

To truly grasp LTV, we need to break down the key assumptions that define it. Just like building a house, understanding LTV requires a solid foundation. The main pillars are the unit of value, type of value, and real vs. predicted LTV.

1. Unit of Value: Most LTV calculations focus on the customer as the unit of value. However, in many B2B contexts, the value is measured per contract rather than per individual customer, using metrics like Average Contract Value (ACV). Imagine if your business deals with high-value contracts instead of individual sales—this distinction is crucial.

2. Type of Value: There are three common approaches to defining value in LTV calculations:

   • Revenue LTV: Looks exclusively at the revenue expected to be generated over the lifetime of the customer. It's like counting the coins in your treasure chest but ignoring the cost to acquire and maintain it.

   • Profitability LTV: Takes lifetime revenue and subtracts post-acquisition costs like the cost of goods sold, shipping, and customer service. This gives a clearer picture of the actual bottom-line impact of different customers.

   • Fully Loaded LTV: Includes additional value drivers beyond direct revenue, such as customer contributions to network effects, virality, and efficiencies. It's like finding hidden gems in your treasure chest.

3. Real vs. Predicted LTV: Real LTV is based on actual historical data, giving you a backward-looking perspective. Predictive LTV, however, uses leading indicators and assumptions about future opportunities. This is especially useful for early-stage companies or those with strong network effects, where each new customer can dramatically change the landscape.

Calculating LTV by Channel

Here’s a crucial point: LTV must also be calculated by channel. Each acquisition channel—whether it's Facebook, Google, TikTok, or organic search—can have vastly different LTVs. Why? Because user behavior varies by channel. Some channels might bring in high-intent users who stick around and spend more, while others might attract users who churn quickly.

Want to learn more about this? Check out this other post I wrote about the centralization of paid ads networks:

Testing and Measuring LTV for Each Channel

Moreover, it's essential to test and measure LTV for each channel. Imagine waking up to find your main advertising platform has suspended your account. This is why you need to diversify. Platforms like Google and Facebook can change their algorithms or policies overnight, blocking your account or reducing your reach. Incorporating the risk associated with each channel into your LTV calculations ensures you're prepared for such disruptions. This way, you’re not just considering the revenue a customer will bring, but also the stability and longevity of the channel through which they were acquired.

Strategies to Increase LTV

How can you make each customer more valuable? It’s not just about keeping them longer; it’s about making them happier and more engaged. Here are some strategies to consider:

• Personalization: Tailor experiences to meet the unique needs and preferences of your customers. Imagine your customer feels like they have a personal shopper who knows exactly what they need.

• Customer Education: Help customers get the most out of your products or services. An informed customer is a loyal customer.

• Loyalty Programs: Reward repeat customers with exclusive benefits. Think of it as giving them a VIP pass to your business.

• Excellent Customer Service: Address issues promptly and effectively. A happy customer is your best advocate and will return to you time and again.

Decoding the Formula

Let’s break down the formula for calculating LTV in a simple way:

Where:

• APV (Average Purchase Value): How much does a customer typically spend per purchase?

• PF (Purchase Frequency): How often does the customer buy from you?

• CL (Customer Lifespan): How long, on average, does a customer continue buying from you?

For example, if the average customer spends $50 per purchase, buys 5 times a year, and remains a customer for 3 years:

LTV= 50×5×3= $750

Behavioral Indicators

Understanding customer behavior is key to predicting and increasing LTV. Behavioral indicators, like visiting a pricing page or reading testimonials, can signal a customer’s potential for higher long-term value. Customer profiles, including demographics and job roles, also play a significant role in determining LTV. Additionally, the source of acquisition matters—customers acquired through organic search or referrals often have higher LTVs than those from paid ads.

Adapting to Changes and Staying Agile

Imagine expanding your audience from early adopters to marginal audiences, diverse mass audiences, and finally to new geographies. Each stage presents unique challenges and opportunities for increasing LTV. But here’s the catch: the landscape is always changing. Platforms can shift their algorithms or even suspend accounts without notice. It’s like building a sandcastle at the beach—you need to constantly adapt to the tides.

This is why it's crucial to regularly measure and monitor your LTV. Analyze your metrics to identify trends and areas for improvement. Different customer segments may have different LTVs, so tailor your strategies to maximize value from each segment.

Looking Ahead: Predicting the Future

Using actual data to calculate LTV is reliable, but it only tells you about the past. Predicting LTV, however, can help you plan for the future. This involves using leading indicators and making assumptions about future behavior and monetization opportunities. For instance, a subscription service might predict higher LTV based on the assumption that a significant number of monthly subscribers will switch to annual plans, increasing their value over time.

Conclusion: Building a Resilient Future

Understanding and optimizing LTV can transform your business. It’s about more than just numbers; it’s about building lasting relationships with your customers. By focusing on LTV, you’re setting the stage for sustainable growth and profitability.

Imagine having the foresight to see not just where your business is today, but where it can be in the future. By incorporating LTV into your strategy, you're not just reacting to the present but proactively shaping your success.

Stay tuned for future posts where we'll dive into value metric thresholds, such as the LTV:CAC ratio, that guide paid optimization decisions. Happy growing!

Picture this:

Your business is humming along, largely driven by digital ads. One morning, you wake up to find your advertising account has been banned. No warning, no explanation. Just like that, your primary source of traffic is cut off. It's a nightmare scenario, but it’s more common than you might think. The question is, can your business survive this sudden shock? If you’re overly reliant on these platforms, the answer might be a resounding “no.”

I’ve been there. My account was once suspended for a staggering three months. The process of setting up a new account and dealing with Meta executives was nothing short of a bureaucratic labyrinth. It was a stark reminder of the precarious nature of putting all your advertising eggs in one digital basket.

So, what’s the solution?

Diversification. You need to spread your wings and develop your own acquisition channels. Here’s why: proprietary channels give you the power to understand your users better, adapt to their needs, and build a community that genuinely benefits from your offerings. Your product should be something that people find valuable and want to share organically. This kind of growth model doesn’t rely on third-party algorithms over which you have zero control.

Think of it like this: In the early days of your business, it makes perfect sense to leverage networks like Facebook, Google, and TikTok. These platforms are fantastic for experimenting and understanding your market without the hefty upfront costs of building your own channel from scratch. They’re particularly useful for the early stages of a company when you’re trying to establish a foothold.

But here’s the catch—these platforms start to own more and more data about your users as your business scales. Their performance improves, and you get hooked on the results. It’s like digital advertising crack. The more you use it, the harder it becomes to imagine your business without it.

Then, one day, you hit a wall. These platforms no longer bring users at a reasonable cost. The payback period stretches out, and suddenly, what once seemed like a well-oiled machine starts to sputter. Now you’re stuck. You haven’t built your own channels, and breaking free from this dependency feels nearly impossible.

This is why it's crucial to start building your own channels early. Don’t wait until you hit that wall. Begin exploring and developing your proprietary channels from the get-go. This might mean investing in content marketing, building an email list, engaging with your audience on owned platforms, or even developing a referral program that incentivizes word-of-mouth growth.

Let’s not forget the power of community. A strong, engaged community can be one of your biggest assets. It’s something that no algorithm change or policy update can take away from you. Foster relationships, provide value, and create a space where your audience feels connected to your brand.

In conclusion, while platforms like Facebook, Google, and TikTok are invaluable tools, especially in the early stages, it’s crucial not to become overly dependent on them. By developing your own acquisition channels, you’re not just diversifying your traffic sources—you’re also gaining deeper insights and greater control over your user base. This approach ensures a more sustainable and resilient growth trajectory in the long run.

Remember, in the ever-evolving digital landscape, adaptability is your greatest asset. Don’t put all your trust in platforms that can change the rules at any moment. Invest in your own channels, build your community, and create a growth strategy that can weather any storm. After all, in the world of business, the only constant is change.

The Perception Dilemma

Today, if you mention using ChatGPT to enhance your job performance, you might be met with suspicion or disapproval. AI, though advanced, is still viewed by many as an unreliable tool. There is a fear that relying too much on AI could devalue human work, questioning the authenticity and validity of the results obtained. This perception can lead to an environment where admitting to using AI is seen as a weakness or a shortcut, rather than an intelligent strategy.

Embracing the Potential

To change this perception, a significant cultural shift is essential. It's crucial to recognize and accept that AI, particularly tools like ChatGPT, are not substitutes for human intellect but complements that enhance our capabilities. This shift in mindset involves focusing on the potential and advantages that AI brings:

1. Enhanced Productivity: AI can automate repetitive tasks, freeing up time for employees to focus on more strategic and creative activities. This not only boosts productivity but also leads to higher job satisfaction.

2. Improved Decision-Making: With the ability to process vast amounts of data quickly, AI can provide insights and analysis that support better decision-making. Professionals can make more informed choices, backed by data-driven insights.

3. Skill Enhancement: By leveraging AI, professionals can develop new skills and improve existing ones. For instance, using AI for coding, data analysis, or writing can enhance one's proficiency in these areas, making them more versatile and valuable in the job market.

4. Competitive Edge: In a world where technology is advancing rapidly, those who embrace AI tools will have a competitive advantage. Not using AI could eventually be seen as a disadvantage, as it becomes a standard in professional settings.

A Future of Collaboration

In the not-too-distant future, using AI in professional environments will be as common and accepted as using the internet or basic software applications. The transformation is already underway, and early adopters of these technologies will be better positioned to reap the benefits.

It's essential to remember that artificial intelligence is here to augment our abilities, not replace us. The collaboration between humans and machines promises a future where our capabilities are amplified, enabling us to reach levels of efficiency and creativity previously unimaginable.

Conclusion

The use of tools like ChatGPT should be seen as a natural evolution in how we work, not as a threat. By embracing AI and its possibilities, we are building a future where technology and human talent coexist in harmony, driving society toward new horizons of innovation and progress. The key lies in recognizing the potential of these tools to transform our professional lives for the better, making us more productive, skilled, and competitive in an ever-evolving world.

The Business Model of Centralized Networks

Centralized networks operate on a business model that revolves around their ability to segment and target audiences efficiently. By collecting data from millions of users, these platforms can accurately predict who is most inclined to buy certain products or services. However, this process has its complexities:

1. Conversion Fees: As the purchasing process becomes more complex, conversion fees increase. If a platform needs to exert more effort to convince a user to make a purchase, it will raise the cost per acquisition (CAC) for the business.

2. Data Value: The true strength of these networks lies in their ability to retain and utilize user data without sharing specific details with advertisers. While they offer precise conversions and targeting, they never reveal the exact identity of users. This anonymity preserves their value as intermediaries and maintains their competitive edge in the market.

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Limitations and Opportunities

Despite their strengths, centralized networks have their limitations. Businesses depend on these platforms for conversions without direct knowledge of who their potential customers are. This creates significant dependency and potentially high costs for customer acquisition.

The Potential of Decentralized Networks

In contrast, decentralized networks present a promising alternative. These networks are not controlled by a single entity and therefore can offer greater transparency and control over data. Some potential advantages include:

1. Transparency and Data Ownership: In a decentralized network, businesses can have greater access to their potential customers' data, allowing them to better understand behaviors and needs without intermediaries.

2. Cost Reduction: By eliminating intermediaries, businesses could reduce the costs associated with customer acquisition and improve the efficiency of their marketing campaigns.

3. Privacy and Trust: Consumers could have more control over their data, choosing to share information with specific companies in exchange for benefits or compensation, potentially increasing trust and loyalty towards brands.

Conclusion

The future of digital marketing might lean towards a balance between centralized and decentralized networks. While the former will continue to dominate due to their ability to offer precise targeting and effective conversions, the latter could gain traction by providing greater transparency, cost reduction, and data control. Businesses need to stay attuned to these trends and prepare to adapt to an increasingly dynamic environment that prioritizes data privacy and transparency.

Developing an Engagement Strategy

1. Select a High-Level Engagement Strategy: Before delving into metrics and analysis, it's crucial to outline a high-level strategy for enhancing user engagement. This strategy will steer all subsequent actions and analyses. Here are the primary strategies:

   • Adding Use Cases: Expand the scenarios in which the product can be used, thereby increasing its relevance and utility to the user.

   • Increasing Frequency: Encourage users to interact with the product more frequently.

   • Increasing Feature Usage: Motivate users to explore and use more features of the product.

   • Increasing Intensity: Drive users to invest more time, money, or other resources during each interaction with the product.
     

   Each strategy involves trade-offs and costs, such as the potential for increased complexity with new use cases or the risk of burnout with too much intensity.

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   Examples:

   Cabify / Uber: Diverse use cases like airport rides, social outings, or daily commutes can lead to varying engagement levels based on the frequency and necessity of travel.
   
   Pinterest: Different users may engage weekly but vary widely in how often they log in or interact with the platform.
   
   MailChimp: Users might engage with different features like campaigns or automation, affecting the depth of their interaction.

2. Define Engagement Metrics: After choosing your strategy, define metrics that effectively measure user engagement. These metrics should reflect both the total engagement across all users and the average engagement per active user.

   • Total Engagement Metrics: Could include total active users, total actions taken, or total time spent.

   • Engagement Per Active User Metrics: These metrics provide insight into how deeply users are interacting with your product on average.
     

   Example: Netflix might measure total hours watched per user to gauge engagement intensity.

3. Define Engagement States for User Segmentation: Segmenting users into different engagement states allows for more tailored strategies and clearer measurement of progress. Define states such as "casual," "core," and "power" users based on their interaction levels.

   • Casual Users: Might interact with the product infrequently and have minimal dependence on its features.

   • Core Users: Regularly use the product and rely on it for daily or weekly tasks.

   • Power Users: Highly dependent on the product, using multiple features intensely.
     

   Example: In the case of Pinterest, you might categorize users who log in once a week as casual, those who use it three times a week as core, and daily users as power users.

Analysis and Adaptation

Once engagement states are defined, analyze the data to identify trends and correlations with long-term retention. This analysis helps in refining the engagement strategies over time. For instance, if data shows that users who engage with a product more intensely are more likely to remain loyal, a company might focus on strategies to increase user intensity.

Mistakes to Avoid

• Misaligning Engagement with User Goals: Ensure that increased engagement aligns with providing value to the user, not just driving up usage stats.

• Overcomplexity: Adding too many features or use cases can overwhelm users, potentially reducing overall engagement.

• Ignoring User Feedback: Regularly gather and incorporate user feedback to refine engagement strategies and ensure they meet real user needs.

Conclusion

Effective user engagement strategies are essential for fostering a deeper connection between users and products. By understanding the different levels of user interaction and strategically enhancing engagement through added use cases, increased frequency, diversified feature usage, and intensified user investment, companies can significantly improve user retention and satisfaction.

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The "aha moment" is the point at which a user experiences the core value of a product and realizes its potential to serve their needs effectively. This realization is crucial for driving engagement, retention, and long-term customer loyalty. This guide will provide a thorough understanding of how to identify and define this moment, integrating both qualitative insights and quantitative analysis.

Step-by-Step Process to Define the Aha Moment

1. Qualitative Understanding:

• User Perspective and Empathy: Begin by immersing yourself in the user's experience. Conduct interviews, user testing, and surveys to gather rich qualitative data. For example, a fitness app might find that users feel an "aha moment" when they see a visual representation of their month-long activity and notice patterns that weren't apparent before.

• Narrative Development: Create detailed user journey maps that include pain points and moments of relief or delight. These maps help in pinpointing where the "aha moments" are likely to occur. For instance, a user of a financial planning tool might experience an "aha moment" when they automate their savings and first see that they're on track to meet their financial goals without manual intervention.

2. Identifying the Actionable Metric:

• Core Action Identification: Identify actions that directly correlate with the realization of the product’s core value. For example, in a photo editing app, the core action might be the first time a user successfully enhances a photo using an advanced feature.

• Metric Development: Define specific, measurable indicators that signal the occurrence of the "aha moment". Continuing with the photo editing app example, the metric might be the use of at least one advanced editing feature within the first 72 hours of app installation.

3. Frequency and Timing:

• Determine Natural Frequency: Analyze user interaction logs to determine the frequency with which engaged users typically perform the core action. This data informs the expected frequency for new users to reach their "aha moment". For instance, if most retained users edit photos thrice in the first week, this sets a benchmark for new users.

• Optimal Time Frame: Establish a timeframe during which the core action should ideally occur to ensure maximum engagement. For example, data might show that users who edit a photo within the first two days of downloading the app are 50% more likely to become long-term active users.

4. Building the Aha Moment Matrix:

• Framework Setup: Construct a matrix where each row represents a hypothesis about the "aha moment" and each column represents different metrics such as retention rate, user satisfaction, and frequency of use. For instance, one row might hypothesize that the "aha moment" is editing three photos within three days of signup.

• Data Analysis: Use statistical analysis tools to find correlations within the matrix. Techniques like regression analysis, correlation coefficients, and logistic regression can help determine which actions are most strongly associated with long-term user retention. For example, analyze if users who perform the hypothesized "aha moment" action have higher retention rates.

• Hypothesis Testing: Experiment with different definitions of the "aha moment" by modifying the actions or the time frame in your matrix. A/B testing can be particularly effective here. For instance, you might test two groups where one group is prompted to edit a photo immediately after tutorial completion, and another group is left to discover this feature on their own.

5. Refinement and Iteration:

• Iterative Feedback: Continuously gather user feedback to refine the definition of the "aha moment". User behavior and expectations may evolve, particularly as new features are added or as the market changes.

• Adjust Metrics and Hypotheses: Regularly update your metrics and hypotheses based on new data and insights. This dynamic approach ensures that the "aha moment" remains relevant and accurately reflects the user's experience.

Conclusion

Defining the "aha moment" is a dynamic process that involves deep user understanding, rigorous data analysis, and continuous refinement. By effectively identifying and optimizing for this moment, you can significantly enhance user engagement and retention. This guide serves as a foundational approach to not only identify the "aha moment" but also to integrate it into a broader strategy aimed at creating a compelling and addictive user experience.

I recently came across an interesting module on Reforge (specifically the Product Marketing module) about how to build a budget. Over the years, I've seen various methods for constructing an annual budget, whether it's for paid media investments, offline marketing, events, freelancers, and so on. The approaches tend to be either overly detailed or way too high-level.

The downside to an overly detailed approach is that it leaves little room for error and consumes a lot of time trying to make a financial model fit each initiative. For instance, if you have 30 initiatives throughout the year, estimating the leads, conversion rates, CAC, payback time, etc., can become overwhelming.

On the other hand, if you take a high-level approach, you might end up spending too much on initiatives that don't require it or too little on those that need more funding to succeed.

In this post, I share a framework that covers the following:

• Total Investment Amount

• Rainy Day Fund and Fixed Expenses

• Initiative Categorization by Tiers

• 4 Strategic Emphasis Archetypes

• GTM Plan and Objectives

Let's dive into it.

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How Much to Invest in Total?

The first question to answer is: how much should I invest in total?

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This model is ROI-based (Return on Investment), so the total amount should always be based on a revenue forecast. We can have two scenarios, one aiming for a low ROI (3x - perhaps because the market is saturated or there are few organic channels to leverage) and one for a high ROI (5x - because the market is new or you have plenty of organic channels to leverage).

A simple revenue forecast might look like this: (missed images when migrating from Ghost )

And the budget scenarios based on ROI would be as follows: (missed images when migrating from Ghost )

Rainy Day Fund and Fixed Expenses

Once we've decided on the total budget (whether high-end or low-end), we need to allocate funds for two items: the Rainy Day Fund and fixed expenses.

For the Rainy Day Fund, I recommend setting aside 3% of the total budget. This amount is reserved for unforeseen events, initiatives that need more funding, or unexpected setbacks. It saves you from having to renegotiate with stakeholders for additional budget.

Fixed expenses depend on your business type, but in this example, I've included costs for always-on paid media, creative design, and freelancers.

Categorization by Tiers

With these items set aside, we can break down the macro-initiatives to be executed by quarter. There can be several, but for simplicity, I've included one initiative per quarter. We also need to categorize each initiative into its respective Tier, where P1 = Game Changer, P2 = High Potential, and P3 = Normal Impact. This way, we can better distribute the budget to those with the highest potential impact.

Here's the formula for distribution by Tiers:

• Total budget = 10P1 + 3P2 + P3

Using this formula, the budget for macro initiatives is distributed like this:

As you can see, Initiatives 1 and 4, which are in the P1 tier, get a much larger budget than the others.

4 Strategic Emphasis Archetypes

Once we have the total budget per initiative, we need to categorize them into one of the "4 Strategic Emphasis Archetypes." This will serve as a benchmark for dividing investment over time and by sub-items (research, creative, awareness, acquisition).

Here are the recommended percentages for each archetype, the investment splash over time, and the investment by sub-item:

• Pain-point based: Positions the product as the best solution to an existing pain in the market. Recommended investment: 10%-15% for research, 15%-20% for creative, 20%-25% for awareness, and 50% for acquisition. Investment splash over time: 40/30/30 (across 3 months).

• Audience based: Positions the product as the best solution for an underserved audience. Recommended investment: 20%R, 10%C, 10%AW, and 60%ACQ. Investment splash over time: 33/33/33 (over 3 months).

• Differentiator based: Positions the product as the best alternative among others in the market. Recommended investment: 20%R, 25%C, 40%AW, and 15%ACQ. Investment splash over time: 50/25/25.

• Change based: Positions the product with a leader guiding a market in transformation. Recommended investment: 5%R, 30%C, 50%AW, and 15%ACQ. Investment splash over time: 50/25/25.

While these are the recommended percentages and investment splashes, adjustments are normal based on your company's stage and total resources.

Here's an example of a "Change Based" initiative with its investment by sub-items and splash over time: (missed images when migrating from Ghost )

GTM Plan and Objectives

Lastly, let's define the Go-To-Market strategy with the objectives for each initiative, the respective audiences/strategies, and the marketing activities accompanying the execution. The idea here is to make a quantitative estimate of the top-of-the-funnel volume you need, with conversion rates by channel (based on historical data or benchmarks), and see how close this estimate is to the set objectives.

Your detailed plan should look something like this: (missed images when migrating from Ghost )

And that's it! Now you have an ROI-based budget constructed bottom-up, with macro initiatives categorized by Tiers and in their strategic emphasis archetype, a division by sub-item, a time-based investment splash, and a detailed GTM strategy for each initiative.

I believe building a budget is more art than science, and it depends a lot on your past experience investing in similar channels or initiatives. I hope this approach helps you better back up your budget with different stakeholders and have clear objectives and tasks when implementing each initiative.

If you'd like me to share the template I created as an example, message me on LinkedIn.

🦉 Quote of the Week "Whenever you find yourself on the side of the majority, it is time to pause and reflect." — MARK TWAIN (from Timothy Ferriss, The 4-Hour Workweek)

That's all for today! If you liked this, let me know in the feedback below. If not, tell me too. :)

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I've been reading "Building a Second Brain" by Thiago Forte, and although there's a more extensive course available, the book introduces a straightforward concept for organizing your digital life. This framework can help you sort things both personally and at work, allowing you to take better notes, remember details, boost your creativity, and become more productive.

The framework I'm referring to is "PARA," which stands for Projects, Areas, Resources, and Archive. The most crucial aspect of this system is that it should help you find relevant information when you need it. Everything you organize using this method should be actionable information. By actionable, I mean you shouldn't just save things for the sake of saving them; it should be a conscious process where you only capture bits of information (text, image, video, etc.) that are useful for something you're currently working on, studying, or interested in at that moment in your life.

This makes it much easier to decide whether or not to keep something by asking yourself, "What project would this piece of information help with?" "Which area of my life does this information support?" "Is this tool something I can use in the short term?"

Let me explain what each category entails. I use this framework to organize my notes in Apple Notes, after migrating everything from Evernote. However, this framework applies to any note or file management system, whether it's Notion, Google Drive, Bear Notes, Obsidian, etc.

Projects

These are short-term efforts in your personal or work life that you're currently working on. They should have a clear end date. Additionally, projects must have a clear objective. For example:

• Finishing a prototype for an app

• Completing the design for a client's landing page

• Remodeling the kitchen

• Moving to a new house

• Lifting 80 kg in the snatch

Areas

These are long-term responsibilities you want to manage over time, with no clear end date. They should be important enough for you to not want to neglect them while they're in your system. These could be areas where you want to learn something, improve them, or manage them. For instance:

• Growth Marketing

• Product Development

• CrossFit

• Mental Health

• Personal Finances

• Personal Relationships

Resources

These are topics you're interested in, learning about, or might be useful in the future. For example:

• Gardening

• Yoga

• Cooking

• Productivity

• Photography

Archive

Finally, there's the Archive section. This is where you'll store all categories or notes that are no longer active in any of the previous sections but that you want to keep for future reference or search.

Here are some examples:

• Completed or paused projects

• Areas that are no longer relevant to your life

• Resources you're no longer interested in

Your note system should have a categorization with a similar architecture:

Another helpful tip for taking and organizing notes is not to categorize while capturing. I recommend having a folder called "INBOX" or something similar, where you drop everything as you capture it. Later, set aside 15 minutes each day to categorize all notes from your INBOX into one of the four categories. This way, your INBOX will always be empty, and everything will be in its appropriate folder for future reference.

I got this idea from "Getting Things Done" by David Allen, a great resource for managing your time and digital information overload.

I hope this organizational method helps. Remember, you can always make adjustments to find a system that suits your lifestyle. The key is for the system to help you be more efficient with your time, create better things, and reduce mental load. There's no point in overcomplicating a system if it takes more time to maintain than it does to derive value from it.

Why Are MVPs Important in Growth?

Hussam Sufan interviewed me for his blog and newsletter. In the interview, we covered questions like: What are MVPs (Minimum Viable Products) for? Which organizations should have a dedicated MVP area? How does an MVP area coexist with other areas within the organization?

I invite you to read the full interview here: Hussam Sufan's Blog.

🦉 Quote of the Week "When things aren’t going well, we tend to think we are lacking in something. But if we want to change our current situation, we should first part with something before we look to acquire something else. This is a fundamental tenet of simple living. Discard your attachments. Let go of your assumptions. Reduce your possessions. Living simply is also about discarding your physical and mental burdens." — Shunmyo Masuno, The Art of Simple Living

And that's it for today! If you enjoyed this, let me know in the feedback below. If not, tell me as well. :)

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See you next week!

A few days ago, while chatting with Benja at the office, we had a couple of questions about productivity. Generally, it's hard to find good YouTube channels or newsletters in Spanish that tackle productivity like Ali Abdaal, Tim Ferriss, and others. So, I decided to dive deeper into this topic, which I'm quite passionate about.

What does it mean to be productive? What methodology should you use to be more productive? What tools can help? How do sports and nutrition play a role?

These were some questions I asked two great friends in an interview format. We've always talked about these topics, and I consider them two of the most productive people I know. Let me introduce them:

• Alejandro Chung: Co-founder of Yeppo, the largest Korean cosmetics e-commerce store in Chile, with physical shops in Recoleta and Providencia.

• Thomas Maremaa: Co-founder of Encuadrado, the all-in-one platform for independent professionals, offering services from appointment scheduling to payment processing. Currently active in Chile and Mexico.

Here's the interview with these geniuses!

*Some responses have been edited for readability.

Alejandro Chung

Do you consider yourself a productive person? Why?

Yes, I consider myself productive because when I work with a team or, more precisely, when planning my and the team's schedule, I make things happen. This isn't only due to my effort but also to coordinating others' efforts. I orchestrate the teamwork dynamics to achieve the goals we set, aiming for more strategic objectives.

What tools do you use daily to manage your time, tasks, projects, to-dos, etc.?

I use Calendly to schedule meetings and coordinate with teams, Google Calendar for time-blocking, and Notion to document tasks and notes. Sunsama helps me manage daily tasks from various channels like Slack and WhatsApp and plan my week. OpenAI's ChatGPT is handy for drafting emails, creating procedures, and summarizing technical documents, reducing administrative load. I use BlueMail for email communication, crafting clear and context-appropriate responses.

Do you use any methodology to stay organized, be more productive, etc.?

I've tried various methodologies but haven't committed to any particular one. The Eisenhower Matrix, for example, is something I consider when organizing my schedule, but I don't use it systematically every day. I also try to tackle the most challenging tasks in the morning (Eat That Frog), when I have more energy, to avoid procrastination. I've used similar methodologies and others focusing on reducing daily work hours, but ultimately, I follow my experience as a manager and entrepreneur, knowing I must dive into strategic aspects and delegate operational tasks.

Describe your typical workday from when you wake up until you go to bed.

My day starts at 8 a.m. after 7-8 hours of sleep. After my morning routine, I have a simple breakfast and start work by checking messages on WhatsApp and Slack.

I use BlueMail to manage emails and Sunsama to plan my day, connecting it with my email and Slack. This way, I keep my schedule organized and optimize my time and energy. I also check Notion, where I have a morning triage checklist.

After reviewing messages and creating tasks in Sunsama, I plan and prioritize activities for today, tomorrow, and the following week. I allocate an estimated time for each task and sync my schedule with Google Calendar.

While executing tasks, I take short breaks every 30 minutes to stay focused. For lunch, I use food delivery services like ecook.cl, simplifying meals, reducing emotional load from decision fatigue, and taking care of my health.

At the end of the day, I briefly reflect on my achievements and lessons learned, then disconnect to focus on my personal life, including sports, relationships, and family.

How important do you consider sports, mindfulness, nutrition, etc., and how do you think it affects your productivity?

Sports and a healthy diet are crucial for my productivity and well-being. I practice triathlon for self-care and ensure a balanced diet.

I use supplements like ZMA (zinc, magnesium, and vitamin B6), which helps with muscle recovery and restorative sleep, allowing me to face the next day with renewed energy. I also use L-theanine, a compound from green tea that provides relaxation and focus, in stressful situations like oral presentations or important meetings.

If you could only keep one app/software for a year, which would it be and why?

If I had to choose just one app, it would be Notion. It's versatile, incorporates AI, allows you to create tables, organize tasks, set priorities and deadlines, and assign responsibilities. Notion is similar to project management apps like Trello or Asana and can also create documents like Microsoft Word.

What advice or tip would you give for general productivity?

Productivity is more of a feeling than a measurable concept. Some people spend hours at their computer without feeling productive, which can harm their well-being. Productivity requires focus and defining high-impact priority tasks. It's important to remember that things don't always go as planned, and we shouldn't stress over it. In summary, mindset and perspective on productivity matter, and it's crucial to delegate tasks, especially at managerial levels. Delegating operational tasks saves time and resources, allowing focus on more significant responsibilities.

Thomas Maremaa

Do you consider yourself a productive person? Why?

While I wouldn't describe myself as completely productive, I do consider myself organized and disciplined. I always look for ways to improve my productivity, but I'm not an expert.

What tools do you use daily to manage your time, tasks, projects, to-dos, etc.?

I use tools like Cron, Notion, and iPhone Reminders to keep track of my tasks, projects, and daily responsibilities.

Do you use any methodology to stay organized, be more productive, etc.?

I've implemented timeboxing into my routine, blocking specific time slots in my calendar to focus on projects and pending tasks. This allows me to have better control over my time and not have my calendar open for everyone.

Describe your typical workday from when you wake up until you go to bed.

I start my day by waking up, drinking water, and taking supplements. Then I do some physical activity, like going to the gym, jogging, yoga, or walking. Afterward, I shower, have coffee (sometimes breakfast), and spend time journaling and educating myself, either by reading or listening to a podcast, along with planning my day. After a quick catch-up on what's happening, I start working. I have lunch, continue working, and then cook dinner. To end the day, I check my to-dos, read or entertain myself, and finally do some stretching or mindfulness practice before going to bed.

How important do you consider sports, mindfulness, nutrition, etc., and how do you think it affects your productivity?

It's crucial. I believe sports, nutrition, and mindfulness are essential for my well-being and productivity. When I neglect any of these aspects, I notice a negative impact on my performance. While it's hard to maintain perfect balance, I try my best to stay active and eat healthily. Regarding mindfulness, I've struggled to maintain a consistent practice in the last year, but I keep trying.

If you could only keep one app/software for a year, which would it be and why?

I'd choose Notion, as it offers a wide range of functionalities that can meet various needs, such as taking notes, writing, and organizing information.

What advice or tip would you give for general productivity?

To improve productivity, discipline and organization are crucial. It's important to develop the mental strength to avoid distractions, like not having multiple browser tabs open or constantly checking WhatsApp, email, Slack, etc.

🦉 Quote of the Week "The more extreme the outcome, the less likely you can apply its lessons to your own life, because the more likely the outcome was influenced by extreme ends of luck or risk." — Morgan Housel, The Psychology of Money

And that's it for today! If you liked this, let me know in the feedback below. If not, tell me anyway.

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That's why I decided to learn Data Analysis with Python, using only Google Colab and ChatGPT. If you're interested in learning data analysis in just a few minutes without much programming experience, this article is for you.

What Do You Need to Get Started?

1. A Google Colab account (I tried Jupyter Notebook, but I prefer Colab).

2. A ChatGPT account (ideally with Plus to use GPT-4, although GPT-3.5 should yield similar results, perhaps slightly less accurate).

3. A dataset to analyze, preferably with a clear understanding of each column's meaning. The dataset should be in CSV format (comma-separated values). If you use macOS, I recommend uploading your .xlsx file to Google Sheets and then downloading it as a CSV. I had issues converting it to CSV from Excel on macOS.

And that's it! You're ready to begin.

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For this example, I used a dataset from a dog subscription box business. The data has been randomized for privacy, but the column structure remains the same.

Starting with a Prompt

Begin by sending ChatGPT a prompt with instructions to upload/load the dataset and explain each column's meaning. It should look something like this:

PromptCopy code

"Can you help me upload a dataset to Google Colab and explain what each column represents? Here's the dataset information: 1. timestamp: Date and time of purchase 2. customer_id: Unique customer identifier 3. product: Product purchased 4. quantity: Number of products purchased 5. price: Price of the product 6. discount_code: Discount code used (if any)"

ChatGPT will provide you with a response like this:

ResponseCopy code

"To load the dataset into Google Colab, you can use the following code snippet: ```python import pandas as pd # Load the CSV dataset df = pd.read_csv('dataset.csv') # Display the first few rows to ensure it's loaded correctly df.head()

pythonCopy code

 Copy this code and paste it into a new cell in Google Colab. Click the "play" button to import the libraries and read the dataset. Now, you need to specify the correct path for your dataset in Google Colab. If you've uploaded it to a specific folder, you may need to update the code snippet to point to that folder. Change `'dataset.csv'` to your dataset's filename. After running the code, confirm that your dataset has been loaded correctly by checking the first few rows. If everything looks good, you're ready to proceed with your analysis. ## Data Analysis with ChatGPT and Google Colab Here are a few common data analysis tasks you can perform with Python and ChatGPT: ### 1. Data Cleaning and Formatting In my example, the timestamp (date of purchase) wasn't being read as a date. Additionally, I had made the mistake of uploading dates in different formats. Here's how you can ask ChatGPT to fix this: ```Prompt "ChatGPT, I need help fixing the date format in my dataset. The 'timestamp' column has dates in various formats. Can you provide code to standardize the date format?"

ChatGPT will likely respond with something like this:

ResponseCopy code

"To standardize the date format, you can use the following code snippet: ```python # Convert the 'timestamp' column to datetime, inferring the format df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce') # Display the first few rows to ensure the conversion worked df.head()

vbnetCopy code

 Running this code snippet should convert your dates to a standard format. Make sure to check for any errors or issues after the conversion. ### 2. Analyzing Customer Behavior Here are a few questions you can ask ChatGPT to analyze customer behavior: - What is the relationship between the use of discount codes and the amount paid? - Who are the top 20 customers by number of purchases and total spending? Is there any variable that could explain why they buy or spend more? - What is the average purchase frequency per year for customers who buy at least once? Which plan corresponds to different frequencies? ChatGPT will generate code snippets to answer these questions, allowing you to gain insights quickly. For example, let's analyze the top 20 customers by the number of purchases and total spending: ```Prompt "ChatGPT, can you help me identify the top 20 customers by the number of purchases and total spending? Can you provide code to analyze whether any variable might explain why they buy or spend more?"

ChatGPT's response might look like this:

ResponseCopy code

"To find the top 20 customers by the number of purchases, use the following code snippet: ```python # Group by customer_id and count the number of purchases top_20_customers = df.groupby('customer_id').size().nlargest(20) # Display the top 20 customers by number of purchases top_20_customers

To find the top 20 customers by total spending, use this code snippet:

pythonCopy code

# Group by customer_id and sum the total spending top_20_spenders = df.groupby('customer_id')['price'].sum().nlargest(20) # Display the top 20 spenders by total spending top_20_spenders

vbnetCopy code

After running these code snippets, you can identify the top customers by the number of purchases and total spending. ### 3. Exploring Data Insights Let's explore the relationship between customer behavior and other variables: ```Prompt "ChatGPT, I want to explore whether there's a relationship between customer behavior and other variables. Can you provide code to visualize the data and identify potential patterns?"

ChatGPT's response might suggest using visualization libraries like Matplotlib or Seaborn to create graphs and plots that reveal trends or patterns.

For example, to visualize the relationship between discount codes and the amount paid, you could use this code snippet:

pythonCopy code

import matplotlib.pyplot as plt import seaborn as sns # Create a scatter plot to visualize the relationship sns.scatterplot(x='discount_code', y='price', data=df) plt.title('Relationship Between Discount Codes and Price') plt.show()

This scatter plot helps visualize whether there's a correlation between discount code use and the amount paid.

Build a Predictive Model

To build a predictive model, you'd need a more complex approach. If you're interested in learning how to create a predictive model to forecast customer behavior, let me know, and I'll cover it in a future edition of this newsletter.

Here is a prompt for ChatGPT to build a simple predictive model:

PromptCopy code

"ChatGPT, I'd like to build a simple predictive model to estimate the probability of a customer purchasing in the future. Can you provide code to help me get started?"

ChatGPT might respond with a code snippet to create a basic logistic regression model to predict the probability of future purchases.

Wrap-Up

This approach allows you to explore and analyze data using Python and ChatGPT quickly. Whether you're interested in customer behavior, exploring data insights, or building predictive models, these prompts and code snippets can get you started.

What Would You Do?

Now that you know who your top customers are, what would you do with this information? Alternatively, what would you do with customers who have a higher probability of churn?

🦉 Quote of the Week "We go to work five days per week but spend more than one of those days on average just looking for the information we need to do our work. Half the time, we don’t even succeed in doing that." — Thiago Forte, Building a Second Brain

And that's it for today! If you enjoyed this, let me know in the feedback below. If not, tell me too.

Don't forget to subscribe if you haven't already.

See you next week!

What a week loaded with AI-related news! GPT-4, Anthropic Claude, Midjourney V5, and the latest from Google and Microsoft.

You probably saw Google announcing its Workspace AI and Microsoft introducing 365 Copilot. If not, here's a quick summary of what each offers:

Google Workspace AI:

• Docs Assistance: Brainstorming, editing, writing, and rewriting with AI.

• Slides: Auto-generation of images, audio, and video.

• Sheets: Data generation and analysis through auto-complete, formula creation, and categorization.

• Gmail: Email drafting, response, summarization, and prioritization.

• Meet: New background generation and note capture during meetings.

• Chat: Workflow enablement for task management.

Microsoft 365 Copilot:

• Unified Assistant: Copilot is a chatbot accessible in all Office applications via a dedicated button.

• Word: Draft creation for editing and improving.

• PowerPoint: Presentation creation with a few instructions.

• Excel: Trend analysis and data visualization.

• Outlook: AI-driven email management, summarization, and quick response drafting.

• Teams: Key discussion points summary, identification of agreements/disagreements, and action item suggestions.

Now, in the era of "knowledge workers," where you and I likely fit into that description, AI will play an increasingly significant role. While many might view this as a threat to their jobs, which often happens during "technological breakthroughs," I believe we should embrace this as an opportunity and ride the wave.

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The Importance of AI in Our Jobs

How significant is this? Well, EXTREMELY.

Many tasks that used to require significant time and focus no longer need to be done from scratch, thanks to AI. For example, I remember spending HOURS writing copy for Facebook ads, looking for the best CTA, crafting engaging ad copy, and so on. Now, with AI, this can be done much faster, better, and with a variety of options.

Meta Ads, for instance, benefits when you provide multiple text variations, allowing for optimized ad combinations. Previously, this required significant time investment. But with ChatGPT or other AI tools, you can focus on strategy rather than drafting copy.

Lawyers can also leverage AI tools to save money for their clients. Just ask about a specific case based on current laws, and AI can offer a response that a human then corroborates.

A few weeks ago, I wrote about "Prompt Engineers," people skilled at crafting "queries" to AI for better responses or outcomes. Some companies pay over $300K per year for this expertise, and it makes sense as a high-paying skill. I've seen significant differences in the quality of results depending on whether someone knows how to write a good prompt. These are skills you should be honing to ride the AI wave and increase your value in the job market.

Personally, I believe we're in a fantastic moment where many jobs will change for the better. Our potential as humans and knowledge workers will be greatly enhanced with AI. The examples mentioned are text-related, but I could expand further into art, design, software, etc. If you haven't seen it, check out the GPT-4 introduction video, where a simple drawing becomes functional code. This is a game-changer, opening a universe of possibilities for people who previously didn't know how to code or want to speed up the initial creation process.

Soon, a significant portion of work will be generated with AI assistance, as it no longer makes sense to do things from scratch in many cases.

Open Questions About the Future of Work

This raises several questions:

• Which jobs will change dramatically in the coming months?

• Which jobs will disappear?

• What new jobs will emerge?

Based on what we're already seeing, we can start forming an idea of the answers to these questions.

What about you? Are you honing your expertise with the help of AI?

🏄🏻‍♂️ Mark Zuckerberg Pivots to AI After His Metaverse Bet

It's not just Google and Microsoft; Meta also wants a piece of the AI pie. After announcing his "year of efficiency," with additional layoffs of over 10,000 people this year, Mark Zuckerberg announces that the new focus will be on AI. Will Meta ride the wave successfully?

🦉 Quote of the Week "Make stuff you love and talk about stuff you love, and you’ll attract people who love that kind of stuff. It’s that simple." — Austin Kleon, Show Your Work

And that's it for today! If you enjoyed this, let me know in the feedback below. If not, let me know as well.

Don't forget to subscribe if you haven't already.

See you next week!

With the rise of advanced chatbots based on language models like OpenAI's ChatGPT, it seems everyone is talking about artificial intelligence (AI). Tech companies like Microsoft, Google, Amazon, Snapchat, and LinkedIn are creating comparable text-generation technologies or integrating ChatGPT into their products. Meta, the parent company of Facebook, Instagram, and WhatsApp, is no exception.

Last month, Mark Zuckerberg's tech giant launched its own large-scale language model focused on research. A few days later, the company announced it would soon incorporate AI into its consumer-oriented products. Now, the CEO has declared that AI won't just be a part of Meta's work—it will be its main focus. Meta is shifting its core objectives from populating a sparsely inhabited virtual world to the increasingly crowded (but hot!) field of generative AI.

Under the company's reorganization known as Zuckerberg's "year of efficiency," the "largest investment Meta is making is in advancing AI and integrating it into all our products," he wrote in an official update published Tuesday. "We have the infrastructure to do this at an unprecedented scale, and I think the experiences it enables will be amazing," he added. The announcement was made in the same letter where the CEO confirmed Meta would lay off 10,000 employees.

In recent years, most of the company's R&D spending outside its direct family of apps has focused on its virtual reality (VR) sector, which has proven unsettling and uninspiring. Meta's costly line of VR headsets and its VR gaming platform (a Second Life imitation), Horizon Worlds, have been priorities and promoted by executives as the company's future.

In the recent past, Zuckerberg has made grand statements about his VR initiatives. Just a couple of months ago, the CEO said the metaverse would become "as important as smartphones." He also presented VR technology as the future of work, although employees forced to use it didn't seem too pleased. In 2021, Meta went on a hiring spree, employing thousands of people to develop its virtual reality offerings. The company's 2022 year-end financial report revealed it had been spending over $1 billion a month on its metaverse and other VR projects.

Looking ahead, Meta will face significant challenges as it attempts to lead in the AI and virtual reality space. Competition in the AI field is fierce, with tech giants like Google, Microsoft, and Amazon developing their own innovative technologies and applications. Moreover, these competitors have already established a strong foothold in the market, making it difficult for Meta to easily overtake them.

To combat these tech giants, Meta must focus on creating unique and valuable products and experiences for users. This could involve exploring new ways to combine AI and virtual reality, creating more natural and immersive interfaces and applications that enable users to communicate and interact in unprecedented ways. By fusing AI and VR, Meta could be in a unique position to offer a richer and more dynamic metaverse experience.

In the coming months, we can expect Meta to continue expanding and improving its current offerings in AI and virtual reality. It's likely we'll see more acquisitions and partnerships in the AI space, as Meta seeks to boost its technology and strengthen its market position. The company might also reveal new AI-based products and services designed to enhance our daily lives and change the way we interact with technology.

However, Meta's success will largely depend on its ability to address and overcome privacy and security concerns that have plagued the company in the past. As AI and virtual reality become more integrated into our lives, protecting user privacy and data security will be increasingly important. To earn public trust and ensure sustainable growth, Meta must demonstrate its commitment to transparency and accountability in all its operations.

In summary, Meta's future is uncertain but promising, with significant opportunities and challenges ahead in the AI and virtual reality realms. The company's ability to compete with other tech giants will depend on its focus on innovation, creating meaningful user experiences, and its commitment to privacy and security. Only time will tell if Meta can meet its ambitious goals and emerge as a leader in the AI and metaverse space.

It's unfortunate to report the latest news that is unfolding as I write this edition of the newsletter. This week, Silicon Valley Bank (SVB), the most "startup-friendly" bank in the U.S., is facing its worst crisis due to a series of missteps and bad timing.

What's going to happen now? How does this impact the startup ecosystem worldwide? How does this affect LATAM startups?

I don't have all the answers, but we'll likely see reactions and ripple effects in the coming days. In this edition, I'll explain what happened (in simple terms) and discuss the initial global consequences.

*Disclaimer: I'm not a financial analyst, so the information here is based on my own research from various internet sources.

The Collapse of Silicon Valley Bank (SVB) Explained

Here's some context if you're not familiar with SVB:

• It had $190 billion in customer deposits (end of 2021).

• Its primary clients are venture capital (VC) funds and VC-backed startups.

• It's the 20th largest bank in the U.S.

Sequence of Events Leading to the Collapse:

1. SVB had to do something with its customer deposits. It either lends the money or invests in assets. Given that it had over $190 billion, it was challenging to lend that amount in a short period.

2. For this reason, and others, in 2022, SVB bought $80 billion in "Mortgage Backed Securities" (MBS).

3. This worked until the U.S. government's policies threw a wrench in the works.

4. With interest rate hikes, U.S. Treasury bonds became more attractive than the securities held by SVB. Treasury bonds have "zero" risk and offer 2.5x the return compared to MBS.

5. This resulted in a loss for the bank. SVB's stock began to drop.

6. Greg Becker, SVB's CEO, needed to act quickly to prevent further decline.

7. His strategy involved building a "safety net" of cash by doing two things: 1) Selling assets from the portfolio, and 2) Issuing new SVB shares.

8. Typically, this wouldn't be a problem, but something else was happening simultaneously: another U.S. bank was on the brink of collapse.

9. That bank was Silvergate Bank, one of the most important banks in the crypto world.

10. Its main client, FTX, collapsed, leading Silvergate Bank's clients to withdraw $8 billion, causing the bank to announce on March 8 that it would cease operations...

11. This happened on the same day SVB announced its safety net strategy.

12. Greg and his Head of PR felt comfortable with the announcement until they realized the reaction from their clients.

13. Their clients panicked upon hearing that their bank needed a safety net after 1) the collapse of FTX, 2) tech layoffs, and 3) fear of a recession.

14. This triggered a mass withdrawal of funds from SVB, causing the bank's stock value to drop by over $80 billion overnight.

15. The California regulator intervened and declared SVB insolvent.

-- UPDATE 03/12/2023 --

16. The U.S. government declared that it would guarantee all deposits from SVB clients, allowing them to access their funds from Monday. HSBC bought SVB UK.

Key Takeaways from This Event:

• A communication error can be extremely costly.

• Markets are extremely complex and interconnected in multiple ways.

• The perceived value by your clients is worth more than the book value.

Additional Ripple Effects and Their Links for Further Insight:

• Silicon Valley Bank UK also collapsed.

• The stablecoin USDC lost its stability and dropped to 0.9 USD due to a percentage of its reserves being held in SVB.

• Roku, Roblox, and other startups disclosed how much cash they had in SVB.

• SVB has branches and clients in China, Denmark, Germany, India, Israel, and Sweden, which might also be impacted.

🦉 Quote of the Week "Don't waste time worrying about things you cannot control." — Shunmyo Masuno, The Art of Simple Living

Quote brought back to life thanks to Readwise. Try it for free here.

That's all for today! If you liked this, let me know in the feedback below. If not, tell me as well.

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See you next week!

Hey!

The title might sound a bit click-baity, but the idea of this post is to demonstrate the potential of knowing how to give good instructions to AI. It's not an exhaustive guide (I might do a more detailed one in the future), but you'll see with an example how this skill is becoming increasingly valuable in the market.

There are already job positions offering over USD 330,000 for the role of "Prompt Engineer," which involves giving instructions to AI to ensure its response aligns with the user's actual request (among other things).

Why is this happening? Because AI, or in this case, ChatGPT, has a lot of knowledge but isn't necessarily very intelligent—those are two different things. This is why we must study how to give better instructions to get the results we want in the way we want. Here's a simple framework to illustrate the example and show how you can learn anything with the help of ChatGPT.

Let's say you want to learn to program in Python for data analysis. The initial prompt could be something like, "Design a study plan to learn Python for data analysis," to give you some topics to study:

(Not copying the entire response to avoid lengthening the post.)

While it gives us a list that we can insert into Google topic by topic (or ask ChatGPT), there's still a lot of work left, and it's not very detailed. Let's try asking for a timeline and links to resources so we don't have to search for everything ourselves and can organize our time:

Much better... but can we do even better? Yes!

We can train ChatGPT with a more detailed prompt. The last response is too ambiguous, and we don't know if it's just videos or 1,000-page documents, how long it'll take to complete, etc. That's why we need to give instructions in terms of "Who, What, How, When, and Why."

Let's see how it works with the following prompt:

Act as a programming professor who creates study plans for people to learn programming. You will be given the student's goal, the time they have available, and their preferred type of resources. You will create a study plan with a timeline and links to the resources. Include only relevant links because time is limited. My first requirement is as follows: "I want to become a data analyst, but I don't know how to program. I can study 10 hours a week and only want video resources. I want to learn to program in Python. Create a study plan for me."

Much better, right?

This prompt follows the framework I mentioned earlier. The "Who" (programming professor), the "What" (study plan with a timeline), the "How" (video resources), the "When" (limited to 10 hours per week), and the "Why" (to learn Python).

An excellent way to ensure that this knowledge sticks is to put it into practice in a project, ideally a project on a topic that interests you to make it more engaging. For this, we can also use ChatGPT.

Link to external database.

What did you think? I hope this framework for creating prompts for ChatGPT helps you get better answers and results. The example might also help if you were here to learn programming 😅, or if you want to learn anything with the help of ChatGPT.

🦉 Quote of the Week "Your brain is no longer the bottleneck on your potential, which means you have all the bandwidth you need to pursue any endeavor and make it successful. This sense of confidence in the quality of your thinking gives you the freedom to ask deeper questions and the courage to pursue bigger challenges. You can’t fail, because failure is just more information, to be captured and used as fuel for your journey." — Thiago Forte, Building a Second Brain

That's all for today! If you enjoyed this, let me know in the feedback below. If not, tell me too.

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See you next week!

So, how does this hack work? I go for a walk, open my voice notes, and start recording myself while talking. It may feel a bit strange at first, but trust me, it's worth it. As you speak, you can start with a brief structure by answering simple questions like these:

• What motivates me today?

• What's the most important thing I need to finish today?

• What am I currently worried about? Why am I worried about this?

• Do I have any ideas I'd like to explore by talking out loud?

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With these simple questions, the idea is to develop your ability to delve deeper into your responses and be as detailed as possible. Try to provide context, names of people, feelings, the level of importance you attribute to things, etc.

Once you've recorded the audio, it's time to transcribe it into text. I recommend downloading Descript, which has worked best for me in Spanish.

Transfer the audio file to your computer, upload it to Descript for transcription, and voilà, you'll have your stream of consciousness in text.

Now, you should clean up the text a bit in Docs, Apple Notes, or any other text editor so that ChatGPT doesn't get overwhelmed with your filler words, and let's move on to the next step.

Go to ChatGPT, and you can ask for different things (I've experimented with some, but I'll give you a few examples, and the rest I'll leave to your imagination).

You could write a prompt like this:

PromptCopy code

"TRANSCRIBED TEXT" This is a transcription of a voice note I recorded for myself. Create a summary with the main points in the order of importance that you think is most relevant. Additionally, create an action plan for this day with tasks by the hour using productivity methodologies. Use markdown and create a table with specific times for each task, including how much time each task should take.

This is a basic example of how ChatGPT could help you organize the ideas in your head and put them into action first thing in the morning. You can evolve the process to do other things. Here are a couple of ideas where recording, transcribing, and asking ChatGPT for help might be useful:

• Writing an important email

• Preparing a presentation

• Preparing a pitch

• Preparing a speech (thanks to Cami for this idea)

• Preparing for a difficult conversation

You can ask for recommendations, make adjustments, expand the text... the sky's the limit! Try it and let me know how it works for you.

📖 What I'm Reading

"Building a Second Brain" by Thiago Forte. A great book and method if you're someone who takes notes in an app like Evernote, Apple Notes, or Notion. Or if you'd like a framework for capturing, organizing, and using the information you consume daily.

🦉 Quote of the Week "We extend beyond our limits, not by revving our brains like a machine or bulking them up like a muscle—but by strewing our world with rich materials and weaving them into our thoughts." — Annie Murphy Paul, author of The Extended Mind

That's all for today! If you enjoyed this, let me know in the feedback below. If not, tell me anyway.

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See you next week!