Taivo Pungas

You might imagine all great startups started from a clear mission. Sometimes they were, but usually, that story is woven in hindsight to cover up the gnarly pivots of the early days. In PR-optimised founding stories, the narrative usually goes like this. Our visionary founders found an apparent $PROBLEM in

I recently started drinking coffee again. Caffeine has always had the effect of narrowing my focus and reducing mind-wandering. The downside is, caffeine makes me less likely to get into diffuse mode, the other main mode of thinking. Focused mode means taking a direct, head-on approach to work: writing an

I've driven a Tesla only once but looking at Karpathy's recent presentation at the CVPR conference, soon no human will. I think Tesla's unique data engine is what will get them there. In their self-driving stack, Tesla has always been betting on cameras and radar. This contrasts with Waymo's bet

Peter Thiel has a 2x2 matrix: indefinite vs. definite, and optimist vs. pessimist. I want to be a definite optimist: imagine a better future and work to build it. Recently, I've been more of an indefinite optimist. Hoping that startup inspiration hits, without a concrete plan to make that happen.

Early this year, I noticed I'd put on some lockdown-weight. I wasn't super worried but wanted to get back to my typical, healthier weight level. My first intuition in this situation was, "let me go on a diet." Diets can work. But they are expensive: you spend willpower every day

You could separate work from life. As in the phrase "work-life balance", work versus life. They can be separate buckets with separate goals, time slots, people, and emotions. Separation definitely makes sense. Work lets us pay for rent and groceries, but might not be fulfilling. Time with friends, hobbies, reading,

The single biggest thing that made me a better friend, partner, colleague, and mentor came from an unexpected place. Two years ago, I was working through a breakup and rethinking how I approach all relationships. At a conference in Berlin, I happened to participate in a Circling workshop. It's is

As an employee, startup stock options are hard. I'm feeling pretty confident by now, but only because I've seen my friends get burned and been burned myself. What's so hard? At one company, my option contract was to be signed "at a future date", not together with my employment contract.

How do you end up with an outlier success? I guess the common advice would be to "leave your comfort zone". That's hard to do. In the comfort zone, everything makes sense. Things work. Clients are using the product. You might even be growing slightly. It would be stupid to

My nervous system is in a civil war over my actions. The dopamine-seeking side keeps winning. Dopamine itself is crucial: it's a neurotransmitter driving you to achieve external goals. The bad part is being driven to the easiest, least meaningful ones. I try not to feed the enemy. Soft-blocking addictive

There's an abrupt life transition almost everyone goes through: school to job. You might go from middle school to manual labour, or PhD to teacher. But regardless of details, a more gradual shift would be better. Most entry-level jobs could be apprenticeships that mix education and work. Why mix them?

Startups have high variance: the top few % reap most of the rewards. The same is true for artists of all kind, and to a lesser extent, software engineers: some Waymo engineers earn 100x the salary of the lowest-paid programmers. Risk is usually taken to be bad. But it might not

Who needs an office these days? It’s a needless expense. I currently work alone, and when I meet others I do it via video call or on a walk outside. Plus, private offices are expensive. A desk in a co-working space is 150€ — and not an improvement over my

I ran my first design sprint last week. According to the creators: The sprint is a five-day process for answering critical business questions through design, prototyping, and testing ideas with customers.The starting point of a sprint could be a problem like “website visitors don’t understand our product.” Monday

I recently reflected on my productivity and discovered, to my horror, that I’m doing pretty badly! I used to be very organized and do lots of creative, important work consistently every day for months on end. Somehow, I had lost that in the past 3-5 years. Doing what comes

Every classification task is linearly separable in the right feature space. This statement is a clue to building ML solutions that scale to many different use cases. Here’s a visual explanation. Say you have data points in a given two-dimensional vector space. If you can use a straight line

“Founders at Work” is a unique book by Jessica Livingston, one of the founders of YCombinator. It contains un-narrated, almost unedited interviews with founders of successful startups like Paypal, Hotmail, Apple, and 27 others. I’m only through about 10% of the book, and it’s clearly one of the

Lately, Twitter has been full of the 2020 US election, which has displaced everything interesting. That means it’s a good time to blog. I’ve recently been reading James C. Scott’s Seeing Like a State, which manages to combine forestry, agriculture, and land/city planning into a history

A few years ago at Starship, I contributed to the Data is the Specification manifesto. The core idea is that it's better to solve problems directly against a collection of examples, as opposed to trying to generalise the problem first and then solving the general problem. Typically you would run

Which business models benefit from AI? This is an audience question I recently got at a webinar aimed at early-stage startup founders. The answer is almost like enumerating all business models because AI is like Javascript. Which businesses could benefit from Javascript? It can create value at almost every company.

What do people mean by “artificial intelligence”? When I say “AI” I mean one of two things. First it could be the experience of a product feeling intelligent. Or I could refer to what it looks like technically: that the system uses machine learning, deep neural networks, code where you

A startup’s AI work often starts from a developer hacking algorithms on the side. When a generalist engineer with no data science background works on prediction problems, they often don’t use a dataset at all, or at best a small one. For example, in the early days of