Drafting last Friday, my first hand was zero lands, my second hand was one land, my third hand (bottoming two) had three lands. However, I then kept drawing four-drop spells and no more lands for 8 turns.
So is it "on me?" Or is it just that the game just is high variance?
Free mulligan means on your third hand you would only bottom 1, not 2.
Besides, with a 37 lands commander deck, the chances that in 4 hands (if your limit is bottoming 2, 5 cards in hand) starts getting low. Around 1 in 10 and that's ignoring mana rocks (2 lands + 1 mana rock) which probably brings it much closer to 1 in 14+.
If you add the chances of more than 4 lands (also fairly undesirable) your odds of a bad forced hand climb to 1 in 7, which is still pretty far from the 50% chance the original post is talking about.
Without the free mulligan you would be at 1 in 4, which is why I said free mulligan is the way to go.
Hard real time is a thing in some systems.
Also, the current approaches might have 85% accuracy -- if the LLM can deliver 90% accuracy while being "less exact" that's still a win!
Humans do it with access to the register-level data sheets, which are only available under NDA, and usually with access to a logic analyzer for debugging.
Usually, the problem with developing a driver isn't "writing the code," it's "finding documentation for what the code should do."
... and then figuring out where the hardware company cheapened out and created a whole unfixable mess (extra fun when you first ship your first 10k batch and things start failing after the vendor made a "simple revision"). Then finding a workaround.
Ctrl-Z suspends the program in most UNIX shells. ("fg" to resume)
Ctrl-S may or may not end up stopping the program, depending on how much it's printing, and how much output buffering there is before it blocks on writing more.
All my shell RCs turn off xon/xoff -- that's a relic from the PDP-11 days we can all do without.
Windows has the Scroll Lock button that's supposed to do this if you need it, but typically, just selecting a character in a terminal emulator will stop the scroll while still buffering the output.
I don't think this analysis matches the underlying implementation.
The width of the models is typically wide enough to "explore" many possible actions, score them, and let the sampler pick the next action based on the weights. (Whether a given trained parameter set will be any good at it, is a different question.)
The number of attention heads for the context is similarly quite high.
And, as a matter of mechanics, the core neuron formulation (dot product input and a non-linearity) excels at working with ranges.
No the widths are not wide enough to explore. The number of possible game states can explode beyond the number of atoms in the universe pretty easily, especially if you use deep stacks with small big blinds.
For example when computing the counterfactual tree for 9 way preflop. 9 players have up to 6 different times that they can be asked to perform an action (seat 0 can bet 1, seat 1 raises min, seat 2 calls, back to seat 0 raises min, with seat 1 calling, and seat 2 raising min, etc). Each of those actions has check, fold, bet min, raise the min (starting blinds of 100 are pretty high all ready), raise one more than the min, raise two more than the min, ... raise all in (with up to a million chips).
(1,000,000.00 - 999,900.00) ^ 6 times per round ^ 9 players That's just for pre flop. Postflop, River, Turn, Showdown. Now imagine that we have to simulate which cards they have and which order they come in the streets (that greatly changes the value of the pot).
As for LLMs being great at range stats, I would point you to the latest research by UChicago. Text trained LLMs are horrible at multiplication. Try getting any of them to multiply any non-regular number by e or pi. https://computerscience.uchicago.edu/news/why-cant-powerful-...
Don't get what I'm saying wrong though. Masked attention and sequence-based context models are going to be critical to machines solving hidden information problems like this. Large Language Models trained on the web crawl and the stack with text input will not be those models though.
So is it "on me?" Or is it just that the game just is high variance?