It’s been a while since I published anything for this newsletter. Things have been tough lately in the startup land, and I was not comfortable with the thought of spending time writing for this newsletter instead of working on my startup.

Now that I have more clarity in my head, I will write again, albeit with a lower intensity than before.

If you are a new subscriber, please expect an occasional email from me. I’m mostly writing about engineering at startups and commenting on pieces of content that sparked my curiosity.

How AI can use software

A while back, I wrote about AI agents as the next evolution of AI tools. Fast-forward a couple of months, and things have changed a lot. New and more robust models are released (GPT4, Llama3, Gemini), and viral AI agent startups are coming out (Devin). And lastly, visual-focused models such as GPT-4V and LLaVA have made inspecting images (including computer interfaces) much easier.

Since my startup revolves around the future of work, I am heavily investing my time learning more about how AI can use software. I believe this will be one of the biggest opportunities in the tech world.

There are at least three ways AI can use software:

1. Through API

This is the most common way for AI to interact with other software. Even most of the AIs in the ChatGPT plugin marketplace work this way. It’s also the most reliable since the communication contract between software is already established.

Many AI Agents on the internet seemingly use AI like humans would (by pointing and clicking the UIs), but behind the scenes, most actually call APIs.

2. Through computer vision

Humans rely on visuals to use the software. That’s why there’s the field of User Interface Design, which involves creating interfaces so that humans can effectively use software.

With multimodal models, especially those equipped with vision understanding, AI can mimic how humans see and use software. The models analyze the software's rendered visuals and identify where the texts, buttons, and forms are. The LLM component then decides what to do next.

3. Through LLM

If the software is a web app, LLM can be used to analyze the markup code. It can detect texts, buttons, input boxes, and other UI elements.

As opposed to traditional RPA (Robotics Process Automation) software, where the bot would only work if the user provides very specific UI elements to work on (which may change due to website updates, etc.), AI Agents can use LLM to select the correct UI elements intelligently.

Here are two projects I found recently that might be worth taking a look at if you are into this topic:

1. Adept AI’s Fuyu-8B Models

Adept AI is one of the most funded startups pursuing the future of AIs working directly with other software. They have published many whitepapers and prototypes, but there is no working product yet.

Fuyu-8B is one of their latest multimodal models. It has a simpler architecture and training procedure than other multimodal models, presumably because Adept wanted narrower capabilities that were more suited to their goals. The model is designed to power digital agents to work with graphs, diagrams, and software UI elements, basically anything on screen.

I agree with their approach to building multimodal models to pursue their AI agent goal. For a real AI agent to be as helpful as a human, it needs to analyze the screen that the average human user sees, understand the user's context, and then act on behalf of the users. All of those activities will rely on image recognition and understanding. Text-based LLMs can only work if the input data is represented in a textual format, but then a lot of information will be lost.

2. WebLINX

WebLINX is a pretty cool model tuned for real-world website navigation. It provides a digital agent that controls a web browser and follows user instructions to solve real-world tasks inside and across websites.

The WebLINX agent consists of two components: 1) a Dense Markup Ranker (DMR) and 2) an action model.

Dense Markup Ranker is a specialized model that converts HTML pages into a compact representation with the most relevant elements while discarding the rest.

An action model (can be an LLM or a multimodal model) will then process the inputs (HTML, instructions, history, and images) and generate the subsequent actions to take (clicking buttons, typing into forms, or loading a new page).

Tidy First, by Kent Beck

Software creates value in two ways:

• What it does today

• The possibility of new things we can make it do tomorrow.

A while back, I read Tidy First by Kent Beck. In software engineering, Kent is known for co-authoring the Refactoring Book and creating the Extreme Programming method. In a sense, Tidy First is Refactoring’s little brother. The book is only 100 pages long and contains dozens of small, actionable advice to improve your code.

The book also touches upon how to fit tidying into your own personal development workflow:

1. When do you start tidying?

2. When do you stop tidying?

3. How do you combine tidying, changing the structure of the code, with changing the behavior of the system?

My favorite part of the book is chapter 21: First, After, Later, Never. With regards to the actual task that you need to do with the code, when do you do tidying? Are you tidying first, tidying after the task, tidying later, or not tidying ever?

In short, Kent argues to:

• Tidy never when:

  • You’re never changing this code again.

  • There’s nothing to learn by improving the design.

• Tidy later when:

  • You have a big batch of tidying to do without immediate payoff.

  • There’s an eventual payoff for completing the tidying.

  • You can tidy in little batches.

• Tidy after when:

  • Waiting until next time to tidy first will be more expensive.

  • You won’t feel a sense of completion if you don’t tidy after.

• Tidy first when:

  • It will pay off immediately, either in improved comprehension or in cheaper behavior changes.

  • You know what to tidy and how.

Overall, this book is great and should be on every engineer and engineering leader’s shelves.

Top Finds

1. Late Bloomer, by Henry Oliver

Have you ever dreamed that you might be far more successful than you are today? Our society tells us over and over that if we're going to achieve anything, we'd better do it while we're young. But whether you're at the start of your career, sensing you're on the wrong path, or feeling unsettled later in life, you're likely wondering just how to reinvent yourself? Have you left it too late?

Whenever we think that we are finished or realize that we are way older than we used to be, remember that late bloomers exist. They are the people who joined a field very late yet still thrive, even outcompeting the younger people.

Also, a related tweet:

2. Worst Day, by Farnam Street

“Your worst day is a chance to show your best qualities, to stand out, and to learn an enormous amount about yourself. Very few people plan or prepare for what they’ll do and how they’ll act during those times. Those who do might well end up turning their worst day into their best.”

It’s easy to sail a ship in a peaceful sea. For some people, it’s not their best days that determine their character, but what they do in their worst days.

So, keep going on.

Make progress, however small.

Remember that everything compounds.

Patience.

See you next time!

As an engineer, I realized that knowing about product management will help me be more effective in my job. In my next job as an engineering leader, it enabled me to also be the VP of Product. And later when I turned into a technical founder, the skills came in handy.

In this post, I will be sharing:

• Three reasons to learn product management for engineers,

• Three actionable tips to get started, and

• Three beginner resources to dive into the topic.

Why engineers need to learn PM skills

1. Knowing PM skills increases the success rate of your project

When you know about user research, backlog prioritization, and stakeholder management—the critical PM skills—you will help your team's Product Manager not be a single point of failure.

There will be more brainpower to think about important problems and what to work on, further reducing potential mistakes and loopholes in your product.

2. PMs and stakeholders will like working with you more

When you have PM skills, your stakeholders, especially your product and business counterpart, will have an easier time working with you.

Since you will know how your company operates and what is expected from the product team, you can help them fill in the gaps and cover the nitty-gritty details—leaving the product and business team to work on more high-level problems.

3. Knowing PM gives you more ownership and opportunity

In my first job as an engineer, we don't have a product manager. So I needed to step up in learning product management until I could work on projects end-to-end, covering both engineering and product.

The product development cycle became faster because there were no back-and-forth delays between product and engineering, as it all happened in my head.

And the benefits were carried over to my next leadership role as VP of Engineering at a different company. From those experiences, my then-boss trusted me to be VP of Product for about a year until a dedicated VP of Product could be onboarded. A whole new kind of experience. And this wouldn’t be possible if I didn’t know a thing about product management.

How to get started

1. Turn your Technical Design Doc into PRD

Engineers are accustomed to writing a Technical Design Doc (let's shorten it to TDD). In contrast, one of a PM's key deliverables is a Product Requirement Document (PRD)

You can understand the aspects essential to a product manager by writing PRDs.

The goal of TDD & PRD is similar: bringing clarity to problems and solutions. With TDD, we are exploring the hows of the project, while with PRD, we are focusing on the whats and whys of the project.

There is some content overlap between TDD and PRD. To turn a TDD into PRD, you can add these extra sections commonly found in PRD:

• Success Metrics: business-wise, what are the parameters & goals for the project to be considered successful?

• User Persona: who are the target users, and how will they interact with the product?

• User Stories: what are the features, and why are they important?

• Go To Market Plan: how will you market and sell your product to users?

2. Let go of the builder hat, and pick up the user hat

One big product manager's job is to continuously improve the product. As the builder, we engineers tend to have biases that hinder having an objective view of our product. Especially if we spend most of our days looking at the codebase and tweaking the product. This makes us more challenging to assess and improve our own creations.

So, to have a clear view of our product, use it as the user, not as the builder. Imagine that you are a user trying to achieve something with your product.

Try to signup for your product with a new account. Or help someone you know use your product.

You will notice the imperfection, the issues, and the potential for improvement that we wouldn't otherwise get in our day-to-day work coding.

3. Be a master of quality

A great product manager is a gatekeeper of quality. They are the champion and the shepherd of high-quality products.

To develop an eye for quality, look at other high-quality products and understand what makes them great. Try to use them extensively, observe the details, and figure out the decision-making process for each aspect of the product.

It's like reverse engineering all the way to the product conception stage.

Beginner resources to dive into product management

So, if you are an engineer trying to learn more about Product Management, here's the best way to get started:

1. Inspired by Marty Cagan

This book will give you a lay of the land and is a helpful way to get your bearings. There's a reason why this always came up as many PM's favorite books. The book is dense yet broad enough to introduce us to the basics of building amazing products.

2. Product Management - From Meh to Awesome

This talk by Shiva Rajaraman is a must-watch resource[ for anyone interested in learning the craft. Shiva shared many small but essential learnings from his time managing products at YouTube.

3. John Cutler on Lenny’s Podcast

And if you want to start going down the rabbit hole, listen to John Cutler's episode on Lenny Rachitsky's Podcast. John wrote a popular newsletter called The Beautiful Mess, which has been on my regular reading list for a long time.

Those three resources will greatly speed up your learning journey. Do let me know if you learned Product Management from other materials :)

My Viral Podcast Interview

Two weeks ago, I was on a podcast that went viral. Even though it was posted to a channel with zero followers, my video somehow appeared on many people's YouTube home page and managed to attract 380 thousand viewers.

My post on LinkedIn about it also received many responses, amassing 200 thousand views and over two thousand likes. It's interesting how sharing more personal stories and showing our vulnerable side can be a differentiating factor on LinkedIn, where people are usually more focused on showing off their achievements.

In the podcast, I talked about my story of growing up in poverty, how I managed to escape it, and my personal mission behind Lumina.

Here's the podcast video (in Indonesian):

Taking on Ship 30 for 30 challenge

I joined the July cohort of Ship 30 for 30. A program where I have to write something online every day for 30 days.

Aside from helping me to be more consistent, there are four reasons why I decided to do this challenge.

1. It will help me build good habits.

There's nothing more habit-forming than forcing myself to do it for thirty days straight. And since the outcome is visible, it is easy to validate the progress.

2. It will help me generate more ideas.

I get many more ideas during the writing process than before. Sitting for 30 minutes daily typing on my notepad will help me be more creative and unlock more ideas. As many people say, writing is also thinking.

3. It will improve my writing skill.

I have always been a fan of the craft but never formally learned it. This time, I will practice the principles I obtain while reading William Zinsser's book "On Writing Well."

4. My learnings will help more people.

I worry that I will forget everything I have learned before sharing it with others. By reflecting and sharing my learnings, I hope it can help you shortcut a few things and avoid similar mistakes I made.

Over the next 30 days, I plan on writing on Twitter and LinkedIn about the challenges facing early-stage technical founders. I may repurpose some of the posts to this newsletter if it fits a story that I want to write here.

That’s all for this week folks. See you next week!

No top finds section as there has been many links already :)

Hiring early team members at a startup is critical. Yet many founders and managers struggle to find enough candidates to fill in the recruitment pipeline.

Unlike bigger companies, startups don't have a recognizable brand name yet. And we don't have an army of recruiters to help us scout.

So how can we unlock a flood of qualified candidates?

One of the most likely answers is just to work harder. But working harder without a clear plan will likely waste our efforts.

We have limited time but unlimited work, so we must prioritize high-leverage activities.

I learned the hard way and had to figure out what works best by trial and error after spending hundreds of hours.

But lucky for you, here’s here are eight high-leverage actions to get more candidates for your startup:

1. Ask your high performers to refer their top friends

Referrals are how great teams get built. It can get the ball running on your initial pipeline. And the quality of candidates from referrals is higher than candidates applying online on your website or through job platforms. Your team members only want to refer good candidates, especially if they will work together later.

More than half of my hires came from employee referrals. The rate should be similar to other startups. And onboarding them is easier because they already know team members who have worked there.

2. Set a referral party session for your team members to focus on referring

When I was at Twitter, I went into a session where we would come and refer candidates in exchange for cool swag. It inspired me to start a session at my company where we invite employees and give them snacks. They then would sit down and put down the name and emails of their friends that they think would be a good fit for the company.

Whenever I do this, after a couple of hours, we would have more than 200 candidates ready to contact.

3. After each interview, ask the top three friends they know

At the end of every interview, regardless of how the interview goes, I would ask the candidate who are their top three friends working in the field. Just knowing their full names are enough. Even better if the candidate can help introduce you to their friends.

Do this for a couple more iterations, and you will have an exponential candidate database. And once in a while, a specific candidate pool will be saturated. It happens when the people recommended to you are already in your database. It is a good sign because you have exhausted that pool and won't leave anything on the table. Now time to move to another pool.

4. Whenever you meet people, ask the top three friends they know

Generalizing on the previous action, now not only after an interview but also anyone you meet.

If the person you meet is more senior, you can be more specific in your ask. And be more general in your intention. Not only for recruiting but also for learning and networking. For example, who are the best mobile engineers you have heard of? Or, who are the best product people that I can learn from?

5. Cold message strong candidates on LinkedIn or Twitter

When you don't have a team yet, cold messaging is the easiest method to get started. Before messaging people you don't know, start with people you already know. Tell them you are building something and want to hire the founding team members.

And what works best is if you do the cold messaging on your own. Founders or hiring managers have a higher reply rate than recruiters. If you have recruiters and would like to still get the benefits, ask them to operate your LinkedIn account on your behalf.

Here's a cold message template that I often use:

Hi {First Name},

I'm Irfan, co-founder & CTO of Lumina, a job & community tech startup backed by Y Combinator & Alpha JWC.

I found your profile impressive for a {Role Name} role at Lumina.

Let me know if you are open to chatting, reply with your WA number & we'll take it from here :)

If you want an even higher reply rate, you can show some proof that you have taken a look at their profile. And share how exactly their profile matches what you are looking for.

6. Arrange group dinners with candidates you admire and pay for it

Back when I was in college, a veteran CTO of the biggest national conglomerates invited me and some other engineering candidates to a dinner. It was an exciting experience to meet both the CTO and the other candidates.

Years later, I hosted multiple dinners with various candidates. Usually in a small group of 7-10 people. We can build more intimate connections. And we can assess more of someone's personality during an in-person session. As a bonus, the participants will remember the dinner long after the session ends, like I did.

Don't brand this as a recruiting dinner. Call this a regular dinner catchup or a mixer. Let them bring one or two of their own friends. Interesting connections might happen that otherwise would not.

7. Host or sponsor a community meetup

If you want to scale your dinner further, say to a group of 40-50 people, you can host or sponsor a community meetup. In Silicon Valley, it is common for companies, especially smaller startups, to host a meetup at their office. If you want to host one, you need to provide the venue, some light dinner, and two or three people to give talks. The hassle is that you need to find the participants on your own, which may not be easy.

When you sponsor a community meetup, you only need to provide the venue and some snacks, while the community organizer will handle the marketing and the meetup logistics. It is fairly easy to find meetups that you can sponsor. Websites such as Meetup.com, Eventbrite, and Luma have many communities with recurring meetups.

You can set up a small booth for people to stop by or go around and talk to everyone. It is easy to bump into people who are open for opportunities and you can assess them on the spot.

After the meetup, you can message the participants and ask if they would be interested in interviewing for your company.

8. Engage headhunting firms or freelance recruiters

When you have secured some initial funding, moving fast is very important, and you may want to capitalize on that momentum. Engaging headhunting firms or freelance recruiters make more sense than recruiting yourself. They will save you a lot of time.

Headhunting firms and freelance recruiters only charge you when you hire candidates coming from their pipeline. If you have the budget, this is an easy win. You will have vastly more candidates and interviews while only paying for the successful hires.

Most of them also have a sensible refund policy. If the candidate doesn't perform as expected or leaves the company within three months, they will refund your money or will help you look for a replacement for free.

Hiring the right people may make or break your startup. So, ensuring you have a healthy funnel to get your next hire is essential.

Threads

Threads, Meta’s answer to Twitter, finally arrives yesterday. And I think it’s pretty decent. The UI is clean. The critical features are there. And you can import your Instagram followings into the new app.

In one day, it attracted a whopping 30 million users. Since I am also an active Instagram user, albeit only to stay in touch with my real-life friends, I find this a refreshing scenery. I communicate through visual means on Instagram. And it has been a long time since I communicate with my real-life friends in open spaces through texts.

I don’t think Twitter will get killed yet. It will still have decent usage with its current user base, as it has been in the past ten years.

I joined Twitter almost fifteen years ago, in 2008, one or two years after it launched. I witnessed many evolution of the platform. I even worked there for two summers and saw the company's inside workings. In every town hall meeting, we would iterate the company's mission, to make every voice heard. We would listen to stories where underserved users’ voices are being amplified for the better. We would listen to how the platform impact democracies around the world.

I believe Threads also has the same mission. And with the current progress, I’m rooting for it to succeed.

Let’s connect on Threads here: https://www.threads.net/@irfan3.me

Top Finds

1. 📺 Greg Brockman (Stripe) on Recruiting

Greg Brockman is a legend in the tech industry. Before being known as the Co-founder, CTO, and subsequently President of OpenAI, he was the first engineer and CTO of Stripe. In this short interview during his early years at Stripe, he talks about how he does recruiting. Many of my hiring philosophies are inspired by him.

2. 📰 Hiring Religion, by Paul English

Paul English is also a legend in the tech industry. In this article he wrote in 2002, he shared his criteria for hiring (bandwidth, attitude, experience, and lack of dysfunctional behavior). He then shared his principles to hire for his startup.

3. 📰 Write like a human, by Om Malik

In a world where every piece of writing becomes AI-generated or corporate-speak, writing like a human is a special art. Be real. And write like a person. Your words will be unique because only you can be you.

Have a good week folks!

These painful ailments made me reflect that my hard work on the weekdays may be useless if I can't enjoy what I have earned on the weekend.

In this post, I am sharing a short reflection on this and several other findings that may be interesting to you.

i. Protect our downsides to enjoy our upsides

People chase success more than they take care of themselves.

Everywhere we go on social media or the internet, we see that people generally want more success instead of protecting themselves from suffering.

We want to learn to make money or build a business through books, conferences, and podcasts. Even more so than to develop good budgeting skills or to practice limiting our desire to spend money.

Let alone learning to build resilience or practicing good mental health.

The examples of generating upsides that are easy to get viral:

• How to get promoted

• How to earn more money

• How to get rich

• How to enjoy life and get maximum happiness

Also, the equally important but often overlooked:

• Understanding the rules not to get fired

• Understand budgeting and living within our means

• How to avoid going into poverty

• How to avoid depression

Many resources are already there to help us maximize upsides, so I want to balance them with downside protection by sharing about Stoic philosophies in this newsletter.

Aside from how to be successful through tech and business, how can we balance it with good health and well-being?

Silicon Valley entrepreneurs taught us how to innovate and build tech companies.

The Stoics taught us how to manage our emotions, control our desire, and lead a virtuous life.

I believe achieving lasting success without prioritizing our physical and mental health is impossible. However, many people neglect these foundational aspects of life to pursue more material achievements.

We can learn upside maximization a lot easier. The drive can stem from external motivation and mimesis. Our surrounding environment is naturally enabling us this. We want what other, more successful people have.

To think about downsides, we must look inward and assess ourselves.

So let's step back from the world and what everyone else is chasing.

Then invest more in protecting our downsides.

For when the bad times come, we are safe.

And can get back up fast.

ii. More active outside

Even though my weekends are mostly immobile, my weekdays are pretty active. I often joke to my friends that my headache only comes when I am not busy working.

I went to five different offline events last week. Everywhere in the world, more tech and startup events are coming offline. This is a great chance to break free from pandemic isolation and desk-bound bad habits. And meeting people in person also has a higher experience density than any online event would have.

Also, meeting new people allows me to expand my luck surface area. The more people I have good relations with, the higher the odds are we will collaborate later.

iii. Reading the Foundation trilogy

I like science fiction, especially those that are published decades ago. It’s fun to see what inventions turned out to be a reality and what are still dreams. For us technologists, they serve as a repository of incredible inventions waiting to be created. It’s also refreshing to fill our imagination with new wild ideas.

Last week, I finally finished reading the esteemed Foundation trilogy by Isaac Asimov. I have heard about the novels for a long time but only managed to pick them up this year.

The books are surprisingly readable for works of literature that were written more than 70 years ago. They are short enough to finish in two or three sittings, and the suspense is a real page-turner.

The Foundation series contained seven books—three of the original trilogy, two sequels, and two prequels. I plan to read other sci-fi books before returning to the rest of this series.

The novels spawn hundreds of years in history and include an ensemble of characters. I thought it would be hard to make it into a movie or TV adaptation, but Apple TV made it nonetheless.

I also recently watched the first season, and it is pretty good! The acting of Lee Pace, whom you might know as Thranduil, the Elven king from The Hobbit movies, is marvelous in playing Emperor Cleon I.

The second season of Foundation will air in two weeks. I am very much looking toward to enjoying the show.

iv. AI and the burden of knowledge

Mario Gabriele of The Generalist wrote a fascinating fable on the difference between humans and AI in handling new knowledge.

For humans, we are not born at the frontier of knowledge. We need to learn from the knowledge passed on by the previous generations.

So as the frontier is pushed further out, reaching it takes future generations longer. This is the "burden of knowledge."

The more we know, the more the next generations must learn to expand the frontier further.

However, AI does not suffer from the burden of knowledge. The technology does not degrade or die, but it simply improves.

AI today is the worst it will ever be. It will only improve. Bigger datasets. Better architecture. More aligned models.

Thousands of researchers are working on it and billions of dollars are being poured.

Mario then proposed several explanations for resolving the impending gap between humans and artificial intelligence. Should we build a tool to live forever and elevate our minds with brain-computer interfaces? What should we do in modern life that humans increasingly do not understand?

The Generalist

AI and The Burden of Knowledge

Brought to you by Public…

Read more

3 years ago · 170 likes · Mario Gabriele

v. Top finds

1. 🖼️ Growing engineering career, a mind map

My friend built a vast map of what areas an engineer should try to grow. For engineers, these can be a great starting point to evaluate your current state. For managers, it can spark ideas on how you would help your team members improve.

2. 📰 Developreneur newsletter

wrote an excellent newsletter for developers wanting to be entrepreneurs. Starting as an entrepreneur has never been easier, yet there are mistakes that people can easily overlook too. Reading the stories of those who have made the move is a great way to learn and avoid mistakes.

His post below on managing money for entrepreneurs is a gem.

Developreneur

The 3 Dead-Simple Rules of Managing Money for Entrepreneurs

Somehow, schools don't teach kids about money. Facts. This makes managing finances hard. Particularly if you have a fluctuating income. Who's got variable income? Yes, you guessed right. Entrepreneurs. That's why we need to educate ourselves. Here are my top 3 tips to avoid starvation when things go south…

Read more

3 years ago · 2 likes · 1 comment · David Levai

3. 📺 The Three-Body Problem trailer launch

The novel of the same name by Cixin Liu is one of the best sci-fi novels I have read. Usually, science-fiction books revolve around America. This novel, however, views it from a China-centric perspective.

What's more interesting, the TV adaptation of the novel is produced by the same set of people who produced Game of Thrones, David Benioff and D.B. Weiss. Many fans hate this, especially after watching how they handled the Game of Thrones final season.

However, that problem may not be happening here because the materials in The Three Body Problem are finished, unlike in Game of Thrones, where the materials for the final seasons did not exist.

From the experience of coding and managing hundreds of projects in the past ten years, I have seen many mistakes in choosing tech stacks. The mistakes can lead to reduced engineering velocity, slow yet unstable product launches, and difficulty in growing.

For example, at a project I was advising, there was a legacy product where every feature was built from scratch, using a complex technology that was very burdensome to maintain. When it was launched, it was very unstable, and the business operations virtually stopped for a week. And what followed were terrible times for everybody. A seemingly simple feature could take weeks to build. And many critical bugs spawned, giving the team nightmares.

All of those problems can arise from not picking the right tech stack.

Taking the wrong lessons from Big Tech

As technical people, we want to find inspiration from successful examples. So we read bigger Silicon Valley companies' engineering blogs. We read their tips on what shiny new technologies to use, how to solve complex problems, and how they can scale to billions of users.

Unfortunately, the technologies and practices at big tech won't work for early-stage startups. Their problems are different from yours. Their problem is scaling their technology to hundreds of millions of users and keeping productivity high with thousands of engineers. Yours are to create an iterative cycle of developing a product fast while validating it to your customers to find product market fit across those iterations.

Using fancy technologies and doing premature optimization overlooks the fact that early-stage startups have many constraints:

• Very few engineers

• Limited cash

• Tight development timeframe

• No idea whether the initial product will work out

Your goal should be to build a simple and good enough solution that your customers want to pay.

Your product may change. You may pivot. You may start over and rewrite your codebase.

More importantly, you must go through that phase quickly and many times over.

So here are four principles that will help you achieve those goals while saving time and money.

1. Choose what's popular to have an easy time hiring.

A friend of mine builds his product in a rare variant of JVM language. Let's say it's Clojure. I have no grudge against Clojure, but there are few Clojure engineers. He had a hard time hiring. When he screens candidates to hire, the chances of hitting someone with enough Clojure skills are remotely low.

Instead of rewriting his product to a more common language, he lifted the Clojure requirement. He hired engineers who may have yet to learn Clojure upfront but were open to learning it. But then, they need at least two months to get the new hire to be productive at the company.

And when a particular technology is less popular, the community and resources we can turn to for support are smaller. Let's remember the pain when we encounter a problem, and Google has nothing to show. You don't want to be the first to face a problem when you want to move fast. You want to be the 100th one, and when you encounter it, somebody has resolved it on Stack Overflow two years ago.

2. Choose what's simple and easy to maintain

Always choose simple technologies when working alone or with only a few engineers. Complex systems have an expensive toll to maintain. Understanding how they work and debugging them takes time, thus, money.

I have often seen people trying to build their first MVP by thinking they will scale to millions of users fast. Or that they need to accommodate hundreds of engineers soon. For example, one may implement a microservice architecture from the get-go. Ignoring the fact that maintaining fifteen services while only having five engineers is very hard.

Another example I observe is choosing technologies intended for later-stage companies or enterprises, for instance, Kubernetes, instead of a more straightforward one-container solution. Kubernetes may be good if hundreds of services run in a cluster of a thousand servers. But early on, you will waste considerable time setting and maintaining it.

3. Choose a framework with commoditized features to iterate fast

Many technologies, especially web frameworks, have built-in features like authentication, ORMs, or logging. Those features are commodity features. Almost every product has similar requirements. And they are rarely your product's core differentiators.

Don't reinvent the wheel. You need to invest less in commodity features so that you can invest more in your core offerings.

I have also seen teams investing many engineers in building products only two employees use. Or they are developing a whole feature that could have been a library import. With a popular excuse that their use case is different.

In my company, one of our requirements is to spawn an administrative dashboard for internal employees quickly. So we choose Django, which has it built-in, saving us months of extra work.

4. Choose SaaS when possible, then use Open Source, only then build yourself

Time is expensive in an early-stage startup, so any engineering time spent not working on your core offering is probably wasted.

In this age, where the tech ecosystems are more or less mature, there are many alternatives to building everything on your own. You can pay for an off-the-shelf SaaS solution or use open-source software rather than build one from scratch.

A few years ago, I was building a feature to do item search for an e-commerce product. And there are many possibilities to get it done:

• Using a SaaS solution, like Algolia. The setup cost is pretty simple. You can get the feature running in a couple of hours.

• Using an open-source solution that we need to host ourselves, like Elastic Search. It may take you a couple of days to set up and use.

• Building the search feature from the ground up. While it can take weeks or months to build, the feature and performance may be subpar compared to that of Algolia or Elastic Search.

I chose the SaaS solution because it was faster to set up, easy to maintain, and relatively cheap.

Of course, you need to take into account the cost. Sometimes, we have a big technical problem where the solution is to host an open-source project ourselves instead of paying for a SaaS product. In turn, this decision saved us thousands of dollars per month. Thinking critically in estimating the cost and benefits is the key here.

–

If you want an extreme example, Pieter Levels, the famous indie founder, built and ran multiple products with only PHP and a single server setup. What's more surprising, he has millions of users and a revenue eclipsing most startups.

I'm not advocating that you use PHP or follow his engineering practice. I'm merely showing a good counterexample amidst Silicon Valley startups' overengineering.

–

Over the long term, the process and culture in your team will also matter, since choosing a tech stack is not a one-time event but a continuous process. There may be new features or products where we must make the same decision-making process. It would be best to delegate this responsibility to your team members when you have a team. So, make sure you align the principles with them.

In my teams, we require a research document and a technical design document before we embark on an adventure with new technology. After everything is written in the docs, we discuss the pros and cons of every choice. If the proposed technology is convincing enough and we are consciously aware of the trade-offs, then we use it.

So far, this practice has worked well.

Top Finds

1. 📰 Choose boring technology

Dan McKinley's paradigm was the inspiration to write this post. Every organization has limited innovation tokens. When you choose new technology, you use innovation tokens. And when you use too many new technologies and have negative innovation tokens, you will have a higher magnitude of unknown unknowns that you must account for.

2. 📺 Lessons from Stoicism and The Military

Nancy Sherman was one of the original modern authors of Stoicism. This podcast episode discusses wide-ranging topics, such as Stoicism and the military, journaling, reducing stress, and cognitive bias.

3. 📰 ChatGPT is a blurry JPEG of the web

Ted Chiang is one of my favorite sci-fi writers. In this essay, he made an excellent analogy that ChatGPT is a lossy compression of the web. Which is kinda true. When in doubt, we can ask ChatGPT for an answer, but the information may not be true. Just like a compressed photo is a representative of the original image.

Here are some:

• I fear driving. I’m afraid of the dangerous accidents that can happen on the road, either because of me or because of other people. I had a traumatic event where I was rushed into the emergency room.

• When things get tough in the workplace, I fear letting down my team members. I’m afraid they think working at other companies is better.

• I fear I am too stressed and not living a healthy life. I worry that I will have a severe health issue and become a family burden later in life.

I kept those thoughts in my head. Never put them down anywhere else, let alone share them with others. But I now can see it with my bare eye.

When I am stressed, I can't think and do problem-solving. My decision-making quality will deteriorate, and I will be less productive. This is not good for my life and career.

When the exercise ended, I left nothing unexpressed. I feel light, sane, and happy, similar to how I feel after a massage or a meditation session.

In her memoir about insomnia, The Shapeless Unease, Samantha Harvey wrote:

Writing has saved my life. In the last year, writing has been the next best thing to sleep. Sometimes a better thing than sleep. I am sane when I write, my nerves settle. I am sane, sane. I become happy. Nothing else matters when I write, even if what I write turns out to be bad. I proceed from some open and elusive subconscious formlessness roughly called 'me', definable only by being nothing and nowhere, just the silence in which shapes move. Then words. Words harnessing things. There is the comfort of organisation, of shepherding chaos, not trying to abolish it but shepherding it towards borders, taking away the problem of infinity and entropy. Proffering the illusion of completeness. And somehow, I start to see myself out there in the words I've made, out in their many worlds, scattered and free.

A phrase came to me one night from nowhere: _proliferations of love_. It keeps echoing through me and I don't know why, but it feels like a definition of writing. The mind throws out thoughts and beliefs in so many permutations and configurations and we are enslaved by it, by the output of our own minds. The mind is a prison. And when we write the noise is distilled and alchemised, and the self can find a way out, which I think is what love is—the escape of the self from the self.

What Samantha feels about writing resonates with me. Free writing can be a therapeutic activity. I can pour formless worry, confusion, and anxiety stuck in my head somewhere else. What’s left in my mind is clarity and ease.

If we look at the works of prominent Stoic thinkers, we will notice the candidness of their writing. Marcus Aurelius' Meditations feel personal. He wrote to himself and shared his most intimate thoughts. Seneca's most enduring works are reflective letters to his close friends.

While journaling focuses on how our day is going and reflecting focuses on introspection, free writing can be anything. We can shape abstract thoughts in our head, then write them down, whatever they are about. When what we write is fuzzy, we can continually morph them into something that represents what we think. 

This exercise makes our thinking sharper and our writing more precise. And in the process, an enormous mental burden can be lifted.

Knowing how powerful writing is, I want to share more about writing in future issues. I will touch on topics such as:

• The benefits of personal retrospectives

• Effective written communication at work

• Learning to write for engineers

• Writing better in English as a foreigner

Top finds

1. On Writing More, by Ava

This is one of the best pieces I have read on writing. Ava shares how to write more. When we don't have anything to write about, it means we aren't digging into ourselves deep enough. Writing should be a continuous excavation. To write more, write about what means the most to us, then write about something that means more.

2. Letter to a burned-out founder, by Kevin Lin

A very relevant reading. I'm halfway in my second year of building my startup, and there are some symptoms of burnout. The founder's ability to progress and not give up will make or break the startup. We won't be able to make progress, and we will give up easier if we are close to burnout.

So let’s maintain a good pace, take a break, and not let our energy run out while the journey is still long. Stamina matters.

3. The Johari Window

The Johari Window is a cognitive psychology tool to help people understand their relationship with themselves and others. It contains four windows represented by four quadrants in a 2x2 matrix. Window one is the part of ourselves that we and others see. Window two has aspects that others see, but we are unaware of. Window three is the private space we know but hide from others. Window four is the unconscious part of us that neither ourselves nor others see.

4. Why AI Will Save The World, by Marc Andreessen

Marc Andreessen is one of my favorite thinkers on the internet. He was a key figure contributing to the development of modern web browsers and is an active investor at various tech companies. His past essays, such as Software is Eating The World and It’s Time To Build, have shaken the internet and inspired generations of entrepreneurs. This essay might be the next one, albeit polarizing. As an investor, he has incentives for more researchers to innovate and more entrepreneurs to build. However, demeaning the people who try to help make AI safer can have dire consequences. It will alienate people to care less about AI risks and go full power on building amidst uncharted territories.

AI risk has become an intellectual religious war in the past few months. When talking about AI risk, usually they are referring to AGI or Artificial General Intelligence, AI that surpasses human capabilities in every task.

Both sides of the argument, the doomer (AGI will kill us all) and the savior (AGI will 100x human productivity), present compelling reasons, yet they are all unproven theories. AGI is not here yet. Either of the argument can be wrong. As a realist, I tend to be more pragmatic. 

Grateful for 200 subs

To close off, this newsletter you read now has 220 subscribers. It would be close to a crowd this size if I visualize it.

When I started this small hobby eight editions ago, I wouldn’t expect that there would be this many audiences.

Most of the first hundred subscribers are my IRL friends and readers of my old blog. And the next hundred subscribers are people I have yet to meet. If you don't know me in real life, thank you for subscribing, and I hope what I write is worth your time.

Reintroducing myself, I am the founder and CTO of a YC-backed startup. This is the second year in my entrepreneurship journey. Before that, I spent many years working in engineering, product, and leadership. 

I am still experimenting with the content of this newsletter. As the name suggests, it will blend technology, startups, and Stoic philosophy; mixing the modern with the ancient. Some topics I might write about are:

• Software engineering

• Transitioning into a leadership role

• Building engineering teams

• Mentorship and coaching

• Building a startup

• Cutting-edge topics in AI

• Stoicism and life philosophy

• Growing up in poverty

• Developing resilience

• Useful principles and mental models

Ten years ago, I didn’t know what I wanted. I was only a high school student from a third-tier Indonesian city. Heck, I was dirt poor. Without a government scholarship, I could not pay my college tuition. Getting to where I am now required a lot of hard work and constant reminders never to give up.

I won't claim that what I share will be a hundred percent replicable, but I'm sharing it anyway in case it's helpful to you. This will be a good journal entry for future me too. I won't claim that what I share will be a hundred percent replicable, but I'm sharing it anyway in case it's helpful to you. This will be a good journal entry for future me too.

1. I didn’t come this far only to come this far.

Whenever I'm unmotivated, I look back at the hardships that I went through. I felt shitty many times in my life. And since I am still alive and kicking, I have proven I could overcome my past challenges. So it’s very likely to overcome my current ones.

Whenever I face a challenge, I look into myself and see how far I have come. Quitting now is too early. It’s like taking everything I have gone through so far for granted. Of course, it wouldn’t be easy going forward, but I must push through.

2. The obstacle is the way

I love this Stoic saying from Marcus Aurelius that I keep on repeating. The impediment to action advances action. What stands in the way becomes the way.

In my life, many obstacles have become opportunities for something more important. My laptop broke? Good, I can spend the rest of the day reading a book instead. A critical service incident happened? Interesting, I will learn something insightful from the postmortem. A new hire isn’t getting up to speed fast? Okay, time to practice some coaching and improve our onboarding process.

There will be countless obstacles in everyone’s life. Through those obstacles, we can reach our destination. Be it breaking through them or getting around them. All while accumulating something more important: learning, experience, and wisdom.

3. Stress + Rest = Peak Performance

When I was at school and in college, I sprinted a lot. Sometimes I wouldn’t sleep to prepare for an exam or work on a project. When I was younger, I had a lot of energy, and there was no negative feedback from my body yet. As I get older, it is easier for me to get sick, and I can’t maintain clear thinking without enough sleep and rest.

Fast forward to now, I designed a cycle that enables me to work hard while not getting burnt out. The execution is not perfect yet, but this is the standard I’m setting for myself.

• Eight hours of sleep after a full day of work.

  In building a startup, sometimes I must work more than 12 hours daily. And 90% of those, especially when working remotely, will be looking at my computer’s or phone’s screen. Eight hours of sleep enables me to recover fully and wake up feeling fresh.

• One day entirely off from work every week.
  In my case, I take a day off on Saturday while I do some work on Sunday. But my mental state is not optimal when I still work both Saturday and Sunday. I will be tired more easily, and my thinking will get less sharp.

• A few days off every three to four months.
  This is typically to avoid burnout and losing motivation in the long run. During this time, I usually catch up on my book-reading goals. I can finish 2-3 books in this time, and it's worth it. When I came back, I came back refreshed and ready to tackle whatever was coming next.

I picked up this cycle from the book Peak Performance a few years ago. The whole book can be summarized as Stress + Rest = Peak Performance. Peak performance is only sustained when you have a period of stress, stretching your normal limit, and then resting to ensure your body and mind recover.

If you have other advice on becoming more resilient, don't be shy to put them down in the comment section.

Being selected as LinkedIn Top Voice

A bit of a personal update. Two weeks ago, LinkedIn reached out and told me that they would love me to be part of their Top Voice program. I was humbled. Because I don't post that often on LinkedIn. I only post when there is something significant happening in my company or when I share a new edition of this newsletter.

The perks of being a LinkedIn Top Voice are getting a blue Top Voice badge and a free LinkedIn premium subscription. This motivated me to share more often on LinkedIn so that others can consume my learning and keep me at the top of my future customer's minds. In the past few months, I don't use LinkedIn for recruiting but to connect with other professionals and look for customers for my company’s product.

Top Finds

1. Getting started in today’s workplace, by LinkedIn News Asia

I was also featured in an article from LinkedIn, along with another newsletter writer with a fantastic life journey, Amanda Cua, who is building one of the most popular newsletters in Southeast Asia at 19 years old! We shared about how to get started in today's workplace and several tips on accelerating your early career.

2. Why write? By Shane Parrish

Shane wrote an excellent short essay on the value of writing in the age of content abundance. In short, writing teaches us about what we know, what we don't know, and how to think.

3. How to cultivate resilience by Jason Shen

The main essay in this newsletter is inspired by the piece written by Jason Shen from Every. Jason introduced a more structured approach to cultivating resilience by Responding, Restoring, Rebuilding, and Reflecting.

Enjoy the rest of the week, folks!

As a founder, not being in the SF Bay Area right now have some downsides. After covid, many conferences, events, and communities that were previously happening online are getting offline. What once was accessible from anywhere across the globe is now location-locked. I've had a FOMO on not being in the Bay Area for the past few months. However, thankfully after a lot of looking, there are still a lot of online communities that are happening and growing.

When I started my Y Combinator batch last year, everyone was fully online, and they did a fantastic job making sure the community felt vibrant. The Zoom calls are great, and the Slack channels are alive. We also have weekly calls with YC group partners and other founders in our batch. And every two weeks, YC would ask us to set goals and sprint hard to achieve those. One of the great benefits of joining YC is the batch and the community around it.

I love joining an online community like this, mainly because I want to:

• Have a good environment of supportive peers from similar stages,

• Have a forcing function and fast-paced experimentation cadence,

• Join insightful events or online meetups,

• Get inspiration on what cool stuffs people are building,

• Meet and connect with new people.,

During YC, the weekly Office Hour & Experimentation cadence made my company's progress very fast. Mistakes that could take months to uncover only take 1-2 weeks during the batch. And the rate of learning progressed more quickly too. However, after my YC batch, there needed to be more cadence and vibe, and I looked hard to fill this. Fortunately, I found Transcend Network. It's a fellowship for founders working in education tech and the future of work. Transcend is selective in admitting members, not only based on their startup but also the values that the member may bring to the community. The program is well run, and I met and learned from many founders worldwide.

Which online communities to join

Here are some of the online communities I recommend and have participated in. Most of these communities are for founders, but some are suitable for engineers, engineering leaders, product managers, or anyone who wants to build and learn something.

Lenny's Newsletter Community

Lenny Rachitsky's newsletter is the most well-known in the product world. Now he has branched off into Podcasting, which has been very popular. In addition, they have a very vibrant community in Slack for paying subscribers. The discussions there were enjoyable. And they have many internal self-organizing meetups, both IRL and online. (Cost: $15/mo).

Rands Engineering Leadership

This is a Slack community for engineering leaders founded by Michael Lopp. He's an executive at Apple and previously at Slack, Pinterest, and Palantir. He has written multiple books on engineering leadership and has been my role model for a long time. The Slack community here is free, albeit you need to apply first. The inside is very active and vibrant. And there are several self-organizing online meetups too. (Cost: free).

Small Bets Community

Daniel Vassallo is not an unknown figure if you are already active in the Twitter-verse. He has been viral many times, and the lessons he shared there are timeless. His Small Bets community is now 2,000 members and growing. I only joined a couple of months back and have been impressed by the quality of the people and the webinars offered inside. Unlike most others that require recurring payments, this community only costs a one-time fee, so you can pay once and relax for the rest of your journey. (Cost: $375, but sometimes there are seasonal discounts and purchasing power parity for developing countries).

Farnam Street

Farnam Street by Shane Parrish is well-known for its Podcast, Books, and Blog. But the community doesn't get the attention that it deserves. It is aimed at high achievers and has many webinars and online meetups. Aside from that, you also get a lot of premium Farnam Street perks, such as the ad-free version of The Knowledge Project podcast, its hand-edited transcript, and subscriber-only content. (Cost: $149/yr)

Plato Academy

Suppose you are an engineering leader or senior engineer trying to advance your career. In that case, Plato can be a fantastic community. The community is very well managed, and you can meet a lot of mentors there, both 1on1 or through group sessions. (Cost: $399/mo)

Reforge

Reforge is also well-known among product and growth professionals. Unfortunately, the price is quite pricey, and you need to apply to join. The content and the live classes are super excellent and lively. However, the community (both on Slack and in the web forum) is not very active. (Cost: $2000/yr)

The Generalist

This community spun out of The Generalist Newsletter by Mario Gabriele. I really love his deep dive into intriguing companies around the world. The content inside is similar to Lenny's Newsletter but with fewer members (probably due to the high price). (Cost: $499/yr)

Twitter

Twitter is the hub for any online community. The real-time and open nature of the platform makes it easy for people to join, participate, and group together into many niches. A lot of the online communities that I joined primarily came from Twitter. (Cost: free)

Buildspace

The online community for builders. I only joined as a spectator, not a builder, but the experience has been lit. Their Discord server is active, and so many people are building there. (Cost: free)

There are many more that I haven't joined, mainly because they have a high overlap with the ones I already joined or because they are costly (looking at you, Write of Passage). And I am a part of dozens of free Discord communities, but none really stands out yet.

If you want more are some other communities recommended multiple times by my friends:

• Ship 30 for 30 (online writing)

• Write of Passage (online writing)

• Building a second brain (productivity)

• Ness Labs (productivity)

If you have joined one of these and have some testimonials, feel free to comment below. And let us know too if you find other interesting online communities we can join.

If you are keen on joining online communities, especially the gated ones or those that require payment, here are some tips:

• Do a lot of research on their content, members, and programs. To ensure that you get what you expected.

• Read testimonials from existing members. It would be great if you know those people personally, or if not, then public testimonials on social media should be enough.

• Know how active their forums are. The more active the forums, the more you can learn and connect with people.

• Seek out a once-off payment/lifetime membership if possible. The recurring payment gets expensive if you are tight on budget

Interview on AIModels.fyi

Last week, I was invited to share my stories on AIModels.fyi newsletter. I shared

1. How Stoic philosophy influences my career

In startups, which are full of uncertainties, it helped me navigate through every obstacle that went my way. Even in engineering, where everything is supposed to be deterministic, chaos might happen. Computers are deterministic, but the humans using and building them are not. Knowing that there is non-determinism in engineering has helped me a lot in untangling and solving issues.

2. The story of building Akselito, my career-prep startup

I love teaching. And I have been teaching since I was in high school and in college. Mainly I taught algorithms and competitive programming. After graduating, I continued to do mentorships for students and young professionals who want to get started in tech or who want to work in Silicon Valley. Akselito is a productization of what I have been doing ever since. We built a platform for professionals to help others break into a better career, even in smaller ways such as reviewing resumes, doing mock interviews, and advising students.

3. What excites me about the future of AI

Amid the gloomy reports that AI might drive unemployment high, in the long term, given we do re-training and better education, AI can be a net positive force multiplier for the world's economic productivity.

Read the full newsletter here: Blending Philosophy and Startups: Interview with Tri Ahmad Irfan

Top finds

1. M&Ms: The Good Times Cannot Last, by Louie Bacaj

The past 12 years have been an anomaly in the world's economy, a very long bull run where many good things happen. I studied and built my early career in this era. Seems that era is gone now. And I regret not writing and publishing more of what I learned. I regret not improving my English skills so that I can sell more confidently. I could've started a startup earlier and gotten all the learnings faster.

This excellent post by Louie speaks the truth. Unfortunately, when good times happen, people tend only to enjoy it, thinking it will never end. Wise men build optionality, especially when things are good.

2. Generate 100+ content ideas in 30 minutes

One of the reasons I'm building this newsletter is to improve my thinking and communication skills. However, I am often stuck in the ideation phase. I just stared at the blank screen and had the blinking cursor stares back at me. Oh damn, what should I write about...

Thankfully this short video really helped me get unstuck. It provides 3 easy steps to generate endless ideas. This board explains everything, but you need to watch the video to understand how to read it.

3. Writing: Good Career Move, Terrible Career

If you can write and frequently write online, it will greatly help your career. However, the economics get really bad if you try to become a full-time writer. You must be at the top to make enough money to live. A very enlightening read.

See you next time fam 👋

I heard about Max Tegmark a few years ago after listening to his appearance on Lex Fridman podcast. Recently he has been more well-known as the president of the Future of Life Institute, which advocates for a 6 months pause on advanced AI developments.

I was impressed by his eloquent thoughts on life, AI, and the future of humanity. He wrote two books, one about AI and one about Math. I finished reading his AI book very fast, but I struggled to finish the Math one. Probably just because I didn’t have enough curiosity in Math yet.

In this post, I will share a summary and most important takeaways of the Life 3.0 book, however, I will focus more on the Life aspects, rather than the AI and Superintelligence aspects. Currently, there are a lot of discussions already regarding AI, AGI, and Superintelligence.

On the topic, I read other AI books as well, some that I have finished such as AI Superpower and Genius Makers. And some that are still in progress, such as Superintelligence. I usually read books in parallel, depending on where my interests take me.

In discussing life, we will start by defining life, structuring our thinking around it, and categorizing life by how advanced they are. The thinking of life in this book relates to the life that has been happening on Earth and what may happen here in the future. We are excluding the hypotheses of alien or extraterrestrial life here. However, I believe the core concepts would still be applicable to any form of life.

Whenever I read something mind-expanding, I usually take a lot of highlights and try to take notes so that I can refer back to them later. Hopefully, it can become one that I can share with my friends. I'm super interested in this topic and would love to discuss if you are also into it.

The book was written more than 6 years ago in 2017. There are a lot of advancements in biotech, medicine, and AI, so I’m feeling some of the content here is getting more real pretty fast.

What is Life?

Defining life is hard, and so is defining intelligence, memory, the mind, and numerous aspects of life. Competing definitions exist, and some include highly specific biological requirements such as being composed of cells, which may disqualify future intelligent machines and extraterrestrial aliens.

To have a generalized enough definition. life is simply a process that can retain its complexity and replicate. What is being replicated is not matter (atoms) but information (bits) specifying how the atoms are configured.

When bacteria replicates (makes a copy of its DNA), no new atoms are created, but a new set of atoms are arranged in the same pattern as the original, essentially copying the information.

We can also think of life as a self-replicating information-processing system whose information (software) determines both its behavior and the blueprints for its hardware.

In the book, Max breaks down Life into three versions with advancing complexity. It all boils down to their ability to modify their own software and hardware.

Life 1.0

Life 1.0 cannot redesign its hardware or software during its lifetime: both are determined by its DNA and change only through evolution over many generations. Examples are common lifeforms such as bacteria, plants, fungi, and almost every non-human living being.

Many bacteria have a sensor for measuring sugar concentration in the liquids around them and can swim using a whip-like organ called flagella. The sensors and the flagella can work together to control the movements of the bacteria, optimizing for their survival and replication. They may implement a simple but useful algorithm like: "When the sugar concentration around me lowers than what was a few seconds ago, adjust my flagella and swim back".

Humans can learn to speak and have hundreds of other skills. Bacteria, however, aren't good learners. Their DNA specifies not only the design of their hardware such as their sugar sensors and flagella but also the design of their software. They never learn to swim toward sugar, instead, that procedure was hard-coded into their DNA from the start.

This is a form of learning, but not within the lifetime of an individual bacteria. Rather, it occurs during the preceding evolution of that species of bacteria. It went through a lot of trial and error across many generations where then natural selection happens, which results in mutations. Some mutations might improve the structure of the flagella or other hardware, while other mutations might improve the sugar-finding algorithms or other software.

So, bacteria is a form of Life 1.0 where both their hardware and software are evolved, rather than designed.

Life 2.0

You and I, humans, are examples of Life 2.0: whose hardware is evolved, but the software is mainly designed.

If bacteria evolves only biologically, then humans not only evolve biologically but also culturally. Both through learning during a human's lifetime and passing down the learnings across generations. During our lifetime we can redesign much of our software: we can learn complex new skills—for example, languages, sports, and professions—and can fundamentally update our worldview and goals.

We weren’t able to perform any of those tasks when we were born, so all this software was programmed into our brains later through the process we call learning. Whereas our childhood curriculum is largely designed by our family and teachers, who decide what we should learn, we gradually gain more power to design our own software.

In schools and across various learning institutions we encounter after childhood, we were offered: Do we want to install a software module into our brain that enables us to work with Linear Algebra? Or do we want one that allows us to speak French? Or do we want to become a software engineer? Do we want to learn more about AI and the future of life by reading a book about it?

The fact that most of the human software is added after birth (through learning) is very useful since our intelligence is not limited by our DNA's information storage capacity. The synaptic connections in our brain have 100,000x more information than the DNA we are born with. Our synapses store all of our knowledge, skills, and experience in around 100 TB while our DNA only stores around 1GB, barely enough to store a movie.

This ability of Life 2.0 to design its software has numerous advantages compared to 1.0. It is smarter and more flexible. For example, bacteria could learn antibiotic resistance by evolving through many generations. However, when humans have an allergy to peanuts, we can immediately change our behavior by avoiding peanuts in the future. This gives Life 2.0 a dominating edge across the population level, even though our DNA barely changes in the past 50 thousand years.

By installing a software module enabling us to read and write, we are able to store and share vastly more information than we could ever memorize. By developing a software module capable of producing technology (i.e. by studying science and engineering), humanity could communicate with each other instantly and enable much of the world's information to be accessible in just a few clicks.

Life 3.0

But despite the most powerful technologies we have today, all life forms we know remain fundamentally limited by their biological hardware. No one can live a million years, memorize the whole internet, and fly into space without equipment. To be fully free from the evolutionary handcuffs, life must undergo the final upgrade to Life 3.0 by having the ability to design not only its software but also its own hardware.

Humans are probably Life 2.1. We can redesign our software and some of our hardware (with medicine and various prosthetics). We can hack some of the defects and inefficiencies in our hardware, but very minimally.

A fully technological Life 3.0 doesn't exist yet on Earth. Theoretically, it should be able to dramatically redesign not only its software but its hardware as well, rather than having to wait for it to gradually evolve across generations.

After more than 13 billion years of cosmic evolution, significant developments are already happening here on Earth: Life 1.0 developed about 4 billion years ago, Life 2.0 (humans) arrived around 100 thousand years ago, and many AI researchers think that Life 3.0 may arrive in this century, perhaps even during our lifetime, accelerated by the progress of AI.

Looking at the scientific developments in the coming decade, I’m very curious about what the realistic next version of life on Earth is going to be. Will it be an upgraded us, a Life 3.0-style human with a synthetic and repairable body? Or will it be a technological agent that we invented?

Top Finds

1. What is life? How chemistry becomes biology

This is another book that I'm currently reading. I've always been fascinated by emergent properties. How could something seemingly come out of nothing? How could some physical matter produce chemical reactions that produce a life that produces consciousness?

2. The Fermi Paradox

This piece introduced me to The Kardashev Scale, another way of classifying life (more specifically, intelligent civilizations) and their scale of advancement into three broad categories based on their energy consumption. For the most part, this piece elaborates in deep detail about The Fermi Paradox (why we haven't seen aliens).

3. AI Revolution the series (Part 1, Part 2)

Also by Tim Urban. This is probably the shortest introduction if you want to come down the rabbit hole of superintelligence. It was written over eight years ago, but some of the predictions aged eerily well today.

If you are interested to learn more about the future of life and AI, consider buying the book: Life 3.0, by Max Tegmark.

See you next time!

I've been on a pathless path for a long time. Every time I feel that I am on a clear path, I always found a way to stray into a more fuzzy one.

People who know me always felt puzzled whenever I chose a new path. My decision wasn't making any sense. The previous path was already perfect. People would kill to be in my position. Why would I abandon that and start over, in an unknown risky territory?

A non-linear career

When I was in college, in Indonesia, I have the rare opportunity to intern in the US. I was one of the first students from Indonesian universities who had the chance to intern in Silicon Valley. Having a six-figure salary is guaranteed after graduation. But I chose to come back to Indonesia to learn and build an impact here. Sacrificing more than 90% of my income.

I committed to that new path and built my career here in Indonesia. After a while, it slowly becomes a clear path. Moving from an early employee to a significant contributor. Moving into leadership, slowly growing my role as the company grows. I was pretty successful, too. I was only 22 when I was given the trust to manage a team and several employees older than me. What was once unfamiliar, would eventually become a clear path.

Then I moved to a bigger startup, a mid-sized one, in an engineering leadership role. This too becomes a clear path, my role grew as my team grew and my business responsibilities grew. The point of my self-worth becomes how big my department is, how many roles am I working on (Answer: multiple! I was handling engineering, product, design), and how many business lines we are responsible for. This seems to be a respectable and pretty successful career to be in, especially in a company where my peers are much older than me. Then my entrepreneurial itch started to come in. Several friends reached out to me asking to team up to build something together, and I took the plunge into one of them.

The allure of entrepreneurship

The plunge happened one and a half years ago.

I knew to some extent that being an entrepreneur has a much higher level of uncertainty than being an employee at any level. It will have ups and downs. But I didn't know how extreme the uncertainty and the ups and downs will be. I foresaw myself going on a linear path where my self-worth will come in terms of startup metrics: funding size, valuation, team size, users, and revenue. Because those are what was presented in the news, and what people share on LinkedIn. The highlight reels show only the achievements and the congratulations.

But that's not the case at all, especially if I am pretty early and still on my way to finding Product-Market Fit. It's all pushing boulders up to the mountain. But I don't know which boulders and which mountains is the right one. Every action we do is a grunt and gone is my previously imagined straight path seen through rose-tinted glasses.

Some days I feel that I am winning. Some days I feel like the whole world doesn't want me to succeed. All those extreme highs and lows, excitements and stresses, releases dopamine, which is addictive and can be a recipe for mental health issues if not handled properly.

This path requires a sturdy yet flexible mind, a Talebian antifragile one. Personally, due to my upbringing, I was accustomed to living a life with a highly uncertain path. However, I live in a society with forcing functions incentivizing people to stay on the default path. I feel that too. Sometimes I also wonder whether it is a mistake to make the choices I made.

Amor fati

Roman Emperor Marcus Aurelius would say, "A blazing fire makes flame and brightness out of everything that is thrown into it.". This is a motto that I want to constantly embody in this journey, a skill to be able to turn anything that happens to us into fuel to keep going.

However, it would be very hard to keep on going while we are on a pathless path, with numerous things stacked against us. We need to build a condition around us to help us be more resilient. One piece of career advice that I say very often to my friends and colleagues is when you want to have high stamina & be long-term in a job. It needs to be either 1) fulfilling or 2) enjoyable. It's perfect if you can have both. But at least you need to have one.

If it's fulfilling to your soul, then you can endure however unenjoyable or boring the work can be. Because deep down, you know that this is just a means to an end. There is an intrinsic meaning and fulfillment you get just by doing it. If it's enjoyable, then regardless of how shallow the mission is, it will be bearable. You will not despise your everyday work, because you enjoy it.

I think this is not a set and pre-determined condition. Through simple changes in our working setup and daily activities, we can have a more fulfilling and enjoyable experience. I hope I can share more about this in the coming weeks, in case it’s helpful to some of you who are treading on a similar path.

I don’t know what will happen to me in the next few years, given how uncertain the time is. But I strive to make sure it will be full of learning, reflection, and fulfillment.

This is the pathless path.

The sky is still blue in Bluesky

Moving on to other topics.

Last week I got an invite to Bluesky, a new decentralized social network backed by Jack Dorsey. I have used it for a week now, but I couldn’t find the fun yet. My friends and most people I know are not there, since the platform is invite-only for now. And it's very hard to get an invite. Users need to wait for two weeks before they can invite their friends. When I joined, there were only around 30 thousand people on the platform. Compared to the 300 million on Twitter.

The premise of Bluesky is that it's built on top of an open AT protocol, providing account portability, interoperability, and algorithmic choice.

I have mixed feelings about Bluesky. On the one hand, I love Twitter. I have been on the platform since 2008 (that was 15 years ago!) and I have also worked there. I met a lot of cool people and learned a lot of cutting-edge technologies primarily from the platform.

On the other, it's not enjoyable to see how unstable the product currently is, especially after Elon took it private and shook the company inside out. So, having another platform trying to fix the core problems of Twitter is intriguing to me. It would be great if it can be an open protocol like HTTP, that everyone can extend and use.

Top Finds

1. ChatGPT Prompt Engineering for Developers, by Andrew Ng

Andrew Ng, the legendary professor who taught the most well-known online course of all time, is backed in the game. Now he taught developers in utilizing prompt engineering in ChatGPT to be more productive.

2. Prompt Engineering vs Blind Prompting, by Mitchell Hashimoto

Mitchell, who co-founded Hashicorp (that built Terraform & Vagrant), argued that Prompt Engineering is not just witchcraft, but a rigorous discipline that aims to utilize prompting as a way to build reliable functionality for real-world applications.

See you next time folks!

AI as agents

Most of the AIs in the market right now can still be called tools, just like mobile phones or the internet. We mostly control what they do through the input we provide and the interactions we have through their user interfaces. In using apps we control them through their UI, and in using AIs, for example, ChatGPT, we control them through the prompts we give. We can chain the sequence of prompts and results, but we do them manually.

Last week there has been some cool new projects that push AI from being just a tool into AI being an agent. You can provide a high-level goal, and the AI will figure out multi-step and continuous ways to run and optimize toward that goal. And given that AI can now interact with the whole internet and possibly through the outside world, the possibility is almost endless. And given that AI is becoming more multimodal (meaning it can support not just text but also images, sounds, and videos), we will have interactive & smart agents. Very similar to Tony Stark's JARVIS.

Tool: an instrument used to carry out very specific tasks.

Agent: an entity that is able to create tasks, prioritize tasks, complete tasks, and go in an autonomous loop until its goal is achieved.

What's out there

Here are the popular AI agent projects:

• AutoGPT

Created by Toran Richards, this experimental open-source option attempts to make GPT-4 autonomous. It can connect to the internet, use apps, use long-term & short-term memory, and more.

• BabyAGI

Created by Yohei Nakajima, this one is pretty elegant with only ~200 lines of code. It can complete tasks, generate new tasks based on results, and prioritize tasks in real time.

• Jarvis

JARVIS (hey Tony!) is pretty similar to the above two but it’s built by Microsoft and HuggingFace, so I suspect it will have stronger integration to ChatGPT & Azure cloud ecosystem.

All three are open-sourced and are supposed to enable autonomous workflows powered by LLMs. AutoGPT was skyrocketing on GitHub last week with almost 100 thousand stars already, making it the fastest-growing repository there. Some forks have made it into the mainstream news, mostly exaggerating how they can try to break free from their sandbox or try to end humanity.

I also played around with Langchain, a framework for building programmatic and agent-based AIs. Langchain provides a simple abstraction for working with LLMs and makes it easy to construct complex sequences on top of it. It's surprisingly easy to use, just importing their library in Python.

If you are curious to try AI agents but have no time to set up the toolings locally by yourself, you can try AgentGPT which is available online. It can do complex sequences and runs autonomously for a while, but it's pretty limited in what it can accomplish since it's isolated from the outside world.

In the screenshot above, I'm running AgentGPT with the goal of learning Flutter so that I can be a strong mobile engineer at established tech companies. After a couple of minutes of running, it provided me with a learning plan, a list of courses to take, and what exercises I can do to practice my Flutter skill. I tried with some more goals, and it pretty much gives me a solid plan to achieve them. Imagine if we give an AI agent free access to use our computer and all of the apps inside. More extremely, we can give it access to all our social media profiles and credit card information. Imagine what we can accomplish (and how fast our money runs out if the agent runs off course).

From doing a task to achieving a goal

Using ChatGPT now feels like when I first learned to program. When we engineers started learning to program, we started by building for the command line. We read from the standard input (keyboard), then the input will be processed by our program, then the output will show up in the standard output (monitor). REPL shells like those in interpreted languages also run the same way. It's pretty much analogous to the ChatGPT interface. You prompt something, the AI thinks, then you get the result.

In programming, if you want to build more complex and more useful software, then you need to compose multiple functions and even multiple services that will interact with each other. However, we still need to exactly code the sequence of actions that the program will be running. This complex software building is similar to building an AI agent. The difference is that in complex software, the tasks are explicitly programmed down to the deepest level (even if it calls other libraries, they are still codes, and if it calls binaries, then they are still machine codes). In building an AI agent, the sequences of tasks are generated autonomously by the AI and it can run without being micromanaged by us humans.

I'm super interested in how the abstraction will be in LLM chain programming. If traditional data structures are deterministic, we can probably build something novel for the non-deterministic nature of prompts & results. Probably similar to handling unknown results or waiting for an API call to finish using the Maybe & Future construct in functional programming, but LLMs can have an infinite possibility of future results.

What's next

I believe we are entering a new era of trying to fuse explicit procedure-making by humans, with non-deterministic autonomous decision-making by AI. Overall, I'm pretty excited about what's coming next.

The current AI systems, especially LLMs, still have a lot of limitations, such as being very inefficient to train, having shallow reasoning capability, and their tendency to make up stuff. Most notably, AI doesn’t have a good understanding of the world just yet, Prof LeCun even said that house cats are better at it than LLMs. However, if we are talking about an agent that's supposed to live and work in the digital world, I believe it doesn't need to have a bigger understanding beyond that.

Our mind is a stochastic decision-making & generation engine, and so is AI (at least for LLMs). With this in mind, I believe more and more work in the digital world, however complex will be able to be automated in some way. Instead of hiring a human worker, we might just spawn an AI agent to do that. In a few months, AI agents will do the work that millions of people are currently working on. In a few years, we might find ourselves working for these AI agents.

Given how many real people’s jobs are going to be impacted, I think we need to think seriously about re-training impacted workers. We also need to figure out how to educate our younger graduates on how to do well in this seemingly unpredictable future.

Top finds

1. "Computer is like a bicycle for our minds", by Steve Jobs

In this quick video, Steve originally believes that humans are ingenious not because of our raw capability but because of how we can build tools that can augment our capability. No matter how impressive technology is going to be, I still subscribe to Steve Jobs’ idea that technology should augment our human capability to pursue grander goals that we otherwise couldn’t pursue.

2. Note to Haystack VC portfolio on AI, by Semil Shah

Semil wrote a very useful guide for his VC’s portfolio companies on harnessing AI, or else they will be disrupted. I find the case studies and references insightful to my current thinking on incorporating AI in my company.

3. SudoLang: A Powerful Pseudocode Programming Language

We all know it, prompt engineering is more frustrating than debugging. In debugging, we can take a look at the underlying source code, but in prompt engineering, we are dealing with a black box. SudoLang can provide a more easily understandable and interpretable structure to communicate with GPT-4.

4. The Complete Beginners Guide to Autonomous Agents, by Matt Schlicht

I drafted this newsletter issue a couple of days ago, but yesterday Matt wrote this AI agent explainer that’s pretty beginner-friendly. It solidifies my understanding and if you came from a non-technical background, you might want to give it a read.

That’s it for this week folks! I will be coming to my hometown this weekend to spend the Eid al-Fitr holiday with my family, so I might be late in replying to your comments and messages. I hope you are well and healthy wherever you are!

If you find this piece useful and want your friends or team members to know firsthand about these bleeding-edge autonomous agents, feel free to forward it!

But the development in the past few years, particularly this past few months, has been hitting me on a different level. It is the first time I feel unsure of what I wanted to do in life, and whether I would be rendered meaningless in my lifetime.

The technology I’m talking about is of course AI, which is different from other technologies. Other technologies usually came in the form of a tool, but AI comes in the form of a surprisingly intelligent agent. Day by day, it is getting more human and more human. It just doesn’t consume energy the way we do, probably doesn’t need as much rest or personal life, and definitely can’t unionize (yet).

Let’s take a look at how ChatGPT paraphrases the above two paragraphs:

For the first time, I'm experiencing a flicker of anxiety about a development in technology. Usually, I'd greet them with enthusiasm, exclaiming, "Oh wow, what a cool library to tinker with!" or with a shrug, "Hmm, this feels like peculiar hype, but I doubt anything substantial will come of it."

However, the advancements made in recent years, particularly over the past few months, have struck me in an entirely different way. It's the first time I've felt uncertain about my path in life and wondered if I might become obsolete within my lifetime.

The technology in question, of course, is AI, which sets itself apart from other innovations. While other technologies arrive as tools, AI emerges as a surprisingly intelligent agent. Day by day, they become increasingly human-like. They don't consume energy the way we do, likely need less rest or personal time, and, for now, can't unionize (though never say never!).

I have to admit that my below-average writing just got made wittier and more compelling by AI. Its result is more humane, enticing, and grammatically better.

Experts said all the time that creative work will be one of the last jobs to be replaced but turned out they are dead wrong. Writers, painters, and 3D artists are getting more redundant these days. (Even the logo of this publication was drawn by Stable Diffusion).

AI could virtually do any white-collar job on earth better than the average person could. And let’s not count the future improvements as well. Even only dumping more training data into the model seems to significantly improve its capability, and some researchers observed that it possesses some sort of emergent capability.

Given that, what will happen then to our need for meaning and self-worth? Viktor Frankl says that humans need meaning to survive. And most humans find meaning in their work and in helping other people. If AI can do most of our work better and more people and companies prefer to be helped by AI, humans will just become background characters amidst the ever-improving species of AI systems that are taking more and more work on earth.

And we’re here not even talking about the existential threat of a God-like AI yet, but only an AI that can do a lot of things better than the average human.

I will probably have this AI anxiety with me for a long time, given that I’m currently dedicating a big chunk of my life to empowering underserved workers. Many of those workers, unsurprisingly, will be replaced by AI soon.

Top Gems

• Pausing AI Developments Isn't Enough. We Need to Shut it All Down, by Eliezer Yudkowsky

Last week, dozens of researchers and notable people (including Yoshua Bengio and Elon Musk among others) signed an open letter asking for a 6 pause for all advanced AI development. And only a couple of hours later, Eliezer Yudkowsky published an article at TIME arguing why we should shut all AI development down. It’s a pretty wild and entertaining week (although I’m deeply scared inside).

• The Bitter Lesson, by Rich Sutton

A veteran AI scientist came to the conclusion that the way we do AI research in the past few decades is ineffective, and we need to admit that we should let machines search & learn by themselves, not imbue them with our knowledge. AI researchers have often tried to build knowledge into their agents. This went well in the short term, but the eventual big breakthrough eventually came by scaling computation on which the AI will run their search and learnings.

• The End of Programming, by Matt Welsh

Surprisingly, Matt Welsh (a veteran computer scientist, portrayed in the Social Network movie as the one who taught Mark Zuckerberg in an Operating Systems class), was very progressive in his view that probably opposed his entire lifetime of researching and teaching programming since the 80s. In the ACM article, he argued that classical programming, yes this is the programming we know, is dead.

He profoundly predicts that “The engineers of the future will, in a few keystrokes, fire up an instance of a four-quintillion-parameter model that already encodes the full extent of human knowledge (and then some), ready to be given any task required of the machine. The bulk of the intellectual work of getting the machine to do what one wants will be about coming up with the right examples, the right training data, and the right ways to evaluate the training process.”

Given how I am interacting with GPT-4 API & ChatGPT, I may find his prediction coming very soon.

• I wish ChatGPT had never happened, by Anon

A sad story that every time there’s a breakthrough innovation, there will be new jobs created and new jobs lost. We may only count them in percentages or statistics if we are not affected by them. We need to discuss this challenge with more empathy, imagine if we are the ones whose jobs are lost to AI. We will find it harder to look for jobs, and we will struggle to bring food to our family’s dinner table. See the heated discussion on Hacker News.

This post may be a bit dark, but I believe it will be even darker in the coming few years as we are unprepared for whatever is coming next. However, historically speaking (sample size=?), humans should be able to adapt to any innovations coming their way 💡

Enjoy the long weekend my friends!

I have been blogging since 2008, more specifically when I was in junior high school. Blogger, Tumblr, and WordPress were at their peak back then. I wrote all of my writings, hundreds of posts, in internet cafes near my place, and I found some internet friends. Writing the blog has been accompanying my journey in life, within various roles. As a high school student, college student, early career professional, and now startup founder. I’ve been moving from platform to platform, mostly to satisfy my curiosity. I’ve been on Blogger, hosted WordPress, self-hosted WordPress, static sites, various deployments of Ghost, and now in light of what’s currently hot, Substack.

There were times when I wrote a lot, and there were times when I didn’t write at all for an extended period of time. But the one thing that stays true is: I wish I had written more often. In the age where you can ask AI to write everything for you, writing becomes even more important as a tool for thinking. Paul Graham said precisely why we should still teach kids to write without AI.

And this blog, now a newsletter, will be my safe haven to write and hopefully along the way can hone my thinking skills. For those of you who have subscribed to my blog before, I migrated your emails to this Substack. Thanks a lot to my close friends & random strangers who read my blog this past decade.

What am I writing?

As you probably guess from the newsletter’s name, The Stoic CTO, I will write anything from technology and philosophy. The world is becoming more uncertain and volatile, and mastering technology is necessary for us to get ahead in career and business, while mastering philosophy will help keep our minds sane and our life meaningful. Partly it will be my reflection on being a more stoic person in this rapidly evolving world, as I get exposed to the frontline of cutting-edge tech & the startup world.

To make it easier for me to write consistently, I will incorporate part of the newsletter to share interesting findings I found each week. In short, I plan to share with you some of these every week:

• My reflections, observations, and learnings of the week. It can be a few sentences, and it can be a couple of long paragraphs.

• Book review, if I finished reading something. I have a target of reading 20 books this year and have finished 5 in three months.

• Top 3 findings of the week, ideally on what’s currently happening. The goal is to distill the most important gems so that you are not distracted by noises.

Although the formats may vary, depending on what comes to my mind within the particular week.

Top Gems

In this first edition, I will share the top 3 things that are stuck in my mind lately, revolving around the current economic downturn. Here they are:

1. Hey I think the music stopped, by Rob Snyder.

This is a spicy post by a former founder showing how f*cked this current startup environment is. Only going back to basics and figuring out our fundamentals will save our startup. Do read this entertaining yet insightful piece.

Rob's Newsletter for Seed-Stage B2B Startups

Hey I think the music stopped

Hi all — Spicy post incoming. The startup game as we’ve known it for the past ~10 years is changing. It’s not just SVB, but SVB didn’t help. Good freakin’ riddance to the old world On the surface, things might seem to just have “gotten less crazy.” Is software still going to eat the world? Definitely…

Read more

3 years ago · 3 likes · Rob Snyder

2. Startup Decoupling & Reckoning, by Elad Gil

Elad Gil is a legendary investor and operator. In this more serious take, Elad explains in detail how the Fed’s policy creates a tech bubble that is bursting just now. He argued that the current period is actually around historical norms. The past 10-12 years of good times are the anomaly.

Elad Blog

Startup Decoupling & Reckoning

During the last few years low interest rates and money printing led to a funding bubble in private technology. Many startups received pre-emptive extra “free rounds” and hired teams much larger then their stage or progress merited. In parallel, abundant money and secondaries meant companies that should have shut down or sold kept going (indeed - this wa…

Read more

3 years ago · 149 likes · Elad Gil

3. Age of Easy Money documentary, by FRONTLINE

Curious to learn more details about how we got to where we are now? Definitely watch this incredibly vivid documentary. It charts the volatile course of the global economy from after the ‘08 economic crisis and the subsequent long bull market all the way to the pandemic and the current era.

That’s all for now folks!

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If you have any thoughts on what I just wrote or topics you would like me to cover, please leave a comment below!