Okay! Chances are that we are prepping for Personal.ai's Product Hunt launch as you are reading this. While I've seen plenty of blogs publish their experiences following their launch, I wanted to share our preparation and the crazy days counting down to our big launch date. While I have built startups before, I have yet to experience launching a product on Product Hunt. Admittedly I'm very nervous, but among all of the firsts I've experienced in the startup world, one process has helped the team stay consistent: Learn, Learn, and Act.

Some of Amazon's warehouse workers don't have to choose between work and play with the introduction of company-created video games that turn tedious tasks into productive fun. A detailed report from The Washington Post describes how the company has installed screens at many of its warehouse workers' stations that allow employees to turn tasks like assembling orders and moving items into competitive games. Game titles include options like MissionRacer, Dragon Duel, and CastleCrafter and typically involve a productivity-based point system. The more tasks a worker completes, the more points or progress they make in the game. Amazon is using video games to help increase productivity and make working in its warehouses less tedious.

In 2016, thieves stole $16 billion, up almost $1 billion from 2015, using either stolen credit or debit cards, or by creating fraudulent accounts. At first blush, it would appear that the good guys are rapidly losing ground, but those numbers do not tell the whole story as losses per victim, during that same time period, dropped an impressive 11%, falling from $1,165 to $1,038 for each targeted individual. The average out-of-pocket cost to the consumer in these cases decreased from $56 to $48. The increase in fraud is partially due to the rise in the number of scammers and is also because of yearly inflation. Artificial intelligence (AI) and machine learning (ML) played a significant role in the decline of loss-per-victim, and seem ready to drive down losses even further.

We describe the experimental development of an AI player that adapts to different point systems for Parameterized Poker Squares. After introducing the game and research competition challenge, we describe our static board evaluation utilizing learned evaluations of abstract partial Poker hands. Next, we evaluate various time management strategies and search algorithms. Finally, we show experimentally which of our design decisions most signicantly accounted for observed performance.