Software Engineering Daily

When an engineer has offered a job at a tech company, their compensation is often partly in cash and partly in equity–shares of the company. How should an engineer evaluate that offer? How should they negotiate? In the world of equity compensation, costly and avoidable mistakes are routine, and this hurts both companies and employees.

Josh Levy was on Software Engineering Daily previously to talk about the Amazon Web Services open guide, which was one of the most popular episodes we have ever had. In this episode, Josh and I are joined by Joe Wallin, a lawyer who has been involved in startups for many years.

Josh and Joe are the authors of the The Open Guide to Equity Compensation, which is a resource designed to clear up the confusion around stock, options, and fundraising. It’s a tremendously useful and concise overview of what an engineer or founder needs to know when it comes to equity financing.

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At the first Software Engineering Daily Meetup, the speakers explored a range of topics. A few weeks ago, we published Courtland Allen’s talk about how to build a small software business. In today’s episode, we are publishing Haseeb Qureshi’s talk, called “Everything That Rises Must Converge: Why Engineers Disagree About Everything (And Why Fraudsters Do Too).”

This talk explores philosophy, poker, software engineering, fraud, and a basket of other topics. These topics might sound like a random collection, but Haseeb currently works as an engineer on Airbnb’s Risk team and he is the author of How To Be a Poker Player, which is a book on the philosophy of poker.

The audio is taken from the Meetup and we cleaned it up as best we could. We will do a better job with the audio from the next Meetup–which is Thursday, March 9th at Galvanize! Check out our Meetup page for more details.

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The impact of artificial intelligence on our everyday lives will be so profound that our modern institutions will change completely. Employment, government, romance, social norms–all of these things will be upended. To see the signs of this coming, you no longer have to read science fiction. Every week, there are blog posts, news stories, and videos chronicling our strange, exciting time.

Rob May is an investor in artificial intelligence companies and is CEO of the AI company Talla. Every week, he puts out the Technically Sentient newsletter, a compilation of the best pieces of information about AI over the past week. Each newsletter also contains a short essay by Rob in which he gives a big picture perspective on what he is seeing in the AI space.

This was an illuminating conversation with Rob about the implications of artificial intelligence and the topics he writes about in Technically Sentient.

I first saw Rob speak at the Launch Scale Festival, which Jason Calacanis puts on as a free event for people who have started companies.

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If you wanted to build a machine learning model to understand human health, where would you get the data? A hospital database would be useful, but privacy laws make it difficult to disclose that patient data to the public. In order to publicize the data safely, you would have to anonymize it, so that a patient’s identity could not be derived from data about that patient–and true anonymization is notoriously difficult.

In every industry where privacy is a concern there is a similar challenge. If there is no place with public data sets, there is no place where the machines can go to learn. The possible machine learning applications that we can build are limited by the data sets that are available.

Auren Hoffman started his company SafeGraph to unlock data sets so that machine learning algorithms can learn from that data. In this episode, we talk about the machine learning landscape in both the short- and long-term time horizons. We also discussed some of Auren’s strategies for building companies, which have been crucial for me in thinking about how to build Software Engineering Daily.

Where Should Machines Go to Learn?

Quoracast Episode

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Data scientists need flexible interfaces for displaying and manipulating data sets. Data engineers need to be able to visualize how their data pipelines wire together databases and data processing frameworks. DevOps engineers need dashboards to understand their monitoring data at a high level. All of these programmers are building data applications.

Data applications let us visualize and manipulate data sets effectively. In today’s episode, Dave King joins the show to describe the growing importance of data applications. Many knowledge workers use flexible tools like spreadsheets and business intelligence applications. But when you build a domain specific data application for a knowledge worker, you can unlock a higher degree of leverage for that knowledge worker. We discuss data applications and the future of knowledge work in this episode with Dave King.

Full disclosure: Exaptive is a sponsor of Software Engineering Daily.

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