The Making of AI Snake Oil

Medicine Bottles

“I felt completely trapped, a prisoner in my own body.” These are the words of Joe Morris, a filmmaker from London. After a sore spot on his tongue would not heal, Joe decided to see a doctor. He was 31 years old and a non-smoker. It was supposed to be nothing, but the MRI showed a tumour that would need to be carved out and removed, taking along with it Joe’s ability to speak.

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To forecast, or not to forecast, that is the Supply Chain Question

Supply chain

The total amount of stuff shuffled around the world is truly dizzying. And everything produced based on a wrong forecast can only become waste. There are more than $2 trillion of inventory in the United States alone, $2.04 trillion last time I checked — $2,040 billions. And that is just the United States, a country that tracks figures closely. Keep in mind the US is a service-driven economy, and a fraction of global GDP anyways.

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Scrum Vs. Waterfall: What is the Difference?

Office scene

In the last two decades, a lot of robust methodologies and frameworks for project management have established their roots deeply in the market. And to get effective collaboration and team management in the workplace, many Industries prefer methodologies to accomplish the project. However, having several methodologies as options makes the task hard, especially when each of them is unique in one way or the other.

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I’ve run 10,000 product manager vacancies through AI and that’s what happened

If I was given a dollar for every time other product managers asked me “What path to choose in career development?”, then I would have accumulated $50, or even $100. Since I am a lazy person by nature, at some point I set myself the goal of answering such questions automatically. Somehow. Plus, the quarantine brought me back to the basics of programming for which, as you know, honest product managers have no time because they need to set tasks.

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5 simple reasons why Data Projects fail

Upside down man on a jetski

Have you ever sunk weeks into building a dashboard, ML model, or datamart only to have it collect cobwebs? Or started building something and just kept building and building with no end? Or maybe you delivered insights to your stakeholders that ended up being not very insightful. Unless you’re god’s gift to data, you can probably cringe remembering a project that fell into at least one of these traps. Failing is okay and is a part of life, but hopefully, we also learn from our failures sometimes.

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