Product Management

People often ask “What is a product manager?”. I like Ken Norton’s definition: “a technical, user-focused team leader working closely with engineers and designers to guide products”. His classic essay on how to hire product managers doubles as instructions on how to be hired.

The seminal book in the space has to be Marty Cagan’s Inspired.

From the perspective of an investor-founder, Michael Seibel breaks down building products at start ups with great clarity.

If you follow one product person on twitter make it Shreyas Doshi.

A lot of product training is probably over-priced garbage, Jeff Patton’s course is not, it’s excellent.

Strategic thinking is key to product management, Good Strategy / Bad Strategy is a defining modern text on the subject. Ben Thompson’s Statechery blog and podcast analyzes tech news with a strategic lens.

If you are an enterprise product manager it pays to understand how companies ensure productivity, there is no better book on this than High Output Management.

Writing

Good writing is indistinguishable from good thinking.

The Sense of Style defines style and how to develop a classical style. If you have less time, watch the talk.

Writing Down the Bones by Natalie Goldberg, taught me about the habit of writing.

Paul Graham is sharp about many things including writing.

Jordan Peterson’s guide to writing essays explains why and how to write essays.

David Perell translates writing into the 21st century, follow his twitter.

Machine Learning and AI

AI is a powerful technology that is taking the world by storm. Learning Machine Learning has been challenging and rewarding.

For the non-technical, here is a fun illustration of how Random Tree predictive models work. This is not the type of “modern AI” (i.e. deep learning) that powers Chat GPT or self-driving cars. But this is still one of the most accurate and important predictive techniques in use today, and this illustration is an approachable way to understand how computers find patterns in data - which is the essence of machine learning.

Python is probably the coding language you want to learn if you want to get into machine learning. Learn python the hard way follows the philosophy that the hard way is the easy way. They’re right.

A primer that provides a broad framework for how to do data science.

machinelearningmastery.com teaches machine learning with a similar philosophy to Learn python the hard way.

You’ll probably need access to some data:

  1. https://www.kaggle.com/datasets
  2. https://archive.ics.uci.edu/

For more theory the acclaimed Andrew Ng explains concepts clearly, but still ties it back to practicalities. Deep Learning is perhaps the go-to for a mathematically rigorous introductory textbook on Deep Learning (of course), and it’s free.

Andreesen Horowitz is an important venture capital fund that has kept a close eye on AI for years, they’ve done a good job of collecting what they consider the canonical documents on modern AI as of 2023.

Lastly and perhaps most importantly, much thinking on AI ethics is shallow, Vervaeke is anything but.