Unlike chess or Go, where both players see the entire board, poker involves hidden information, your opponents’ hole cards. This makes it an incomplete-information game, which is far more complex mathematically. The AI must reason not only about what could happen, but also what might be hidden.
Even in 2-player No-Limit Hold’em, the number of possible game states is astronomically large — on the order of 10³¹ decision points. Because players can bet any amount (not just fixed options), this branching factor explodes far beyond games like chess.
Good poker requires bluffing and balancing ranges and deliberately playing suboptimally in the short term to stay unpredictable. This means an AI must learn probabilistic, non-deterministic strategies, not fixed rules. Plus, no facial cues or tells.
Humans adapt mid-game. If an AI never adjusts, a strong player could exploit it. If it does adapt, it risks being counter-exploited. Balancing this adaptivity is very difficult in uncertain environments.
I agree with most of this except the overcommunicating part. If you're setting the right expectations, overcommunicating may be mistaken for micromanaging.
Managers are judged by outcomes, not by contribution. Good leaders know how to shield their team from the chaos above while shielding the managers above from the challenges of managing many different personalities. They are a conduit for collaboration and inspiration.
Bad leaders are chaos agents and are the main drivers of attrition and should be rooted out quickly. They are a poison pill.
This reminded me of an old app that would scan the MAC addresses of devices already connected to a paid WiFi network. You would then just change your MAC to one that already paid for the WiFi, and then reset it once you were done.
Yes. It’s a bad (maybe even illegal?) thing to do. The victim device will start losing packets, and it’ll look to them like the network is just unreliable. Eventually they give up and disconnect, and you take full control.
This needs to be a model for other states to follow. Too often, incarcerated people are left with very few real options to have a viable career beyond some sort of physical trade like construction, hospitality, or food service. And while all of those career options are great, they do not often provide a real living wage.
Hopefully, we see more of this throughout the country!
While I agree with some of the statements made in the article, I don't think everything is doom and gloom. There is still a place for early-stage VC for founders looking to scale.
While I agree that AI is making it easier to launch things, there are so many other ancillary things that require human capital that AI still hasn't solved for, and I don't see that changing in the next 10-15 years. Call me a boomer, but there are a lot of companies that need to grow, and VC funding is a great solution for them.
I will say, however, seeing larger funds change to evergreen funds or even to a more PE model should make everyone pay attention. Adapt or die, right?
I'm feeling a little bit of buyer's remorse for that same reason. I love my Canon, but having a closed loop lens system is not ideal and is insanely pricey. All of the "adapters" that allow you to use 3rd party lenses suck.
Thank you for building something like this. Financial modeling can be daunting, but I think it's the most important tool for entrepreneurs to have in order to understand how best to run their business. Can't wait to spend some time playing with this tool!
Even in 2-player No-Limit Hold’em, the number of possible game states is astronomically large — on the order of 10³¹ decision points. Because players can bet any amount (not just fixed options), this branching factor explodes far beyond games like chess.
Good poker requires bluffing and balancing ranges and deliberately playing suboptimally in the short term to stay unpredictable. This means an AI must learn probabilistic, non-deterministic strategies, not fixed rules. Plus, no facial cues or tells.
Humans adapt mid-game. If an AI never adjusts, a strong player could exploit it. If it does adapt, it risks being counter-exploited. Balancing this adaptivity is very difficult in uncertain environments.