What I learned from hiring over 70 technologists and conducting more than 300 interviews

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I strongly believe hiring is one of the most important things a manager will do. As the company evolves, the team is the essence of what the company becomes, not the other way around. Companies are created by the people that it hires, and not from the business plan. However many companies leave it up to inexperienced managers to hire without much guidance. These tips come from my personal perspective and experiences (I’ve made many of the mistakes I’m describing, or seen other people make them). I’ve hired over seventy people, done more than three hundred interviews, and have evaluated thousands of CV’s and cover letters. …

Every month I publish a blog post to summarise what I think are the highlights in content on AI & data at large. It’s my personal notebook on interesting content. Previous editions can be found here: March, April, May, June

1. Article: Eye Movements During Everyday Behavior Predict Personality Traits

Personality tests are almost always based on the big five; agreeableness, conscientiousness, extroversion, openness, and neuroticism. …

1. Article: Great Power, Great Responsibility: The 2018 Big Data & AI Landscape, Matt Turck

Matt Turck and Demi Obayomi made a fantastic overview of the 2018 landscape.

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2. Article: The Cost of Developers, Ben Thompson Stratechery

Probably the biggest news of our industry of June was that Microsoft bought Github for 7.5B dollar. That’s 4,6x as much as Google paid for YouTube, 10x what Facebook paid for Instagram. (WhatsApp was bigger with 19B.) I think Thompson’s analysis on the deal makes a lot of sense. While very expensive, Microsoft had to do something.

3. Podcast: Teaching computers to see with Dr. Gang Hua, Microsoft Research Podcast

Dr. Gang Hua explains in layman's terms how quickly computer vision progressed over the last decennia and the current challenges.

4. Article (Dutch): Wat als de melkboer artificiële intelligentie inzet?, VRT

From a practical perspective, computer vision has a lot of potential to be quickly impact many industries. While many other segments of AI need lots of data to start with (for instance NLP) with computer vision you can just gather a lot of data on the fly with cheap camera’s. This allows for more SaaS(ish) solutions (with accompanying hardware) and therefore much lower implementation costs. This is a huge game changer for farmers (and for many other industries). …


Han Rusman

Product @ Quin

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