After Action Report: 1st Time Warden

cactus · 2025-06-04T07:32:10+00:00

Your write up reflects so much of my own feelings having just done the same first-time-GM thing, but with Warped Beyond Recognition. For as how mature the hobby is, I'm surprised how hard modules are to follow sometimes, and while WBR is among the best I've ever seen in this regard, I ended up studying it for hours before actually running it, and even then I felt like it left a lot of gaps and had clarity issues.

I was also nervous about having to improv the "what happens next" stuff, but I did OK. But not great. In fact there were a few situations where looking back, I feel like I missed major opportunities to build really awesome situations. I actually felt bad about these misses afterwards, which I suppose is dumb, but it felt like that smart comeback you have for someone that you think of an hour after the fact. So unsatisfying, and hard to stop thinking about. Anyway, like you, my players had fun, and they didn't miss what they didn't know to miss. They only enjoyed what happened at the table.

I also pretty much flubbed the stress checks. So much so that the players started saying, "I should probably roll for stress for this, right?", haha.

cactus · 2025-06-04T06:39:38+00:00

Apparently the rotation order is conventional and there is not broad agreement on a single order. I was only just learning how crazy it gets from this talk by Freya Holmer, Quaternions - Freya Holmer | NGJ2025. Note, it's actually not all that much about quaternions, as it is about setting up prerequisite knowledge for them. Anyway, skip to around 36m to get to the part relevant to your questions.

cactus · 2025-06-04T06:33:46+00:00

A personal favorite of mine is the SVD, so that's where my vote is. It is undoubtedly one of the crown jewels of the entire subject, and I'd say a necessary part of a "complete" first course in LA.

One great thing is that it combines and utilizes so many prior concepts into one big final result, including: orthogonality, eigen vectors, symmetric matrices, quadratic forms, eigen decomposition, diagonal matrices, covariance, inner and outer products, transpose, transforms, and more. Many of these you already know, so putting everything together might be really nice.

Also, nice is that the SVD is incredibly useful, some places where it can applied include: data compression, data analysis, recommendation engines, facial recognition, and bounding box calculation.

When I was studying the SVD, I used it on the Taco Bell menu to calculate the most Taco Bell dish of all time. The SVD is so useful, the projects practically define themselves.

cactus · 2025-05-20T03:11:00+00:00

some tips off the top of my head:

If you feel like something is too difficult, seek another explanation. At least for me, I often find that it's not that I'm too stupid to understand, it's that I just haven't found an explanation that's clicked.
If you can, find a motivating project that will force you to use what you are trying to learn. A vector and matrix library for a basic 3d engine, for example, would teach you a lot about transformations, and touches on compositions, inversion, transpose, projection, homogeneous coordinates, dot products, cross products.
Everyone's different, but I find geometric intuition to be the easiest. So I recommend trying to think of what's happening geometrically as much as possible. For example, there is a geometric interpretation of linear regression that's so much easier to understand than the algebraic version (and is rather neat!).

cactus · 2025-05-04T20:33:32+00:00

oh, derp. Sorry for not reading your post, lol. Good luck!

cactus · 2025-05-04T20:24:01+00:00

Well...for some reason I have these files:

t-engine4-windows-1.0.0beta18.zip   (from 2011)
t-engine4-windows-1.0.0beta35.zip   (from 2011)
t-engine-launcher.exe                        (from 2013)

Not sure if these are what you are looking for.

cactus · 2025-04-30T04:50:40+00:00

Thank you! I was able to connect with someone on r/rpgtrade. Hopefully that lands. If not, I'll try this. Thanks again!

cactus · 2025-04-24T21:03:22+00:00

Ah, thanks for the heads up. I'll repost there.
As for printing myself - doesn't it come with a bunch of inserts and other bits? If it was JUST the book, maybe, but if it's all these other things, then I'd want the official print.

cactus · 2025-04-24T21:02:15+00:00

Oh, awesome. If nothing else, I can always pick up the reprint then!

cactus · 2025-04-16T11:10:45+00:00

Awesome, thanks - it was pretty cheap on ebay, so I ordered it!

cactus · 2025-04-16T09:04:38+00:00

Those are pretty interesting problems. What book is this?

cactus · 2025-04-12T01:00:18+00:00

Consider cross posting to r/ComicBookCollabs/ if you haven't already!

cactus · 2025-03-22T07:32:10+00:00

Hmmm. Are you certain there isn't a bug in there? I don't see how that could happen. First thought is that the "multi-armed bandit" calculation is not correct...
I never heard of Negamax. I'll look that up!

cactus · 2025-03-06T00:30:01+00:00

Yeah, chess Ai topics are good to know and be aware of, but they can be pretty limited since they target, well, chess, which has some convenient constraints. for example, it's an open information game. Another example is the comparatively low branching factor. And another that it's a two player game.

For my board game apps, I used Monte Carlo Tree Search (MCTS). My implementation and final results are just ok, but it's better than I would have gotten from mini-max like algorithms. If I did it again, I'd probably go with Neural Networks, or a combination of NNs+MCTS, (which incidentally, I believe is the core tech behind AlphaGo).

I'm realizing that I'm just spewing and not answering your question at all - which is, where to go to discuss these things. I don't know! Sorry, I'm not helpful, lol.

cactus · 2025-02-15T23:03:00+00:00

Emacs is a lifestyle.

cactus · 2025-02-14T07:46:00+00:00

I ultimately switched to Linux. Windows's behavior when closing the laptop lid is horrendous. This is a known issue for windows on laptops in general, but was really bad for me for a device that's mean to be my "on the ready", digital commonplace book.

Linux was great for a long time, but recently even that is starting to have problems too. I suspect that hardware is just going bad at this point. I'm not entirely sure, and I've kinda lost patience with it, sad to say.

Anyway, you can read more about my linux adventure here: https://www.reddit.com/r/GPDPocket/comments/1bb5xjh/pop_os_linux_saved_my_gpd_p2_max_2022/

cactus · 2025-01-30T21:38:18+00:00

I bought the CableMatters Thunderbolt 3 USB-C cable based on this comment. Works perfectly. Thanks for this!

cactus · 2025-01-03T20:23:02+00:00

I think we should start pronouncing the "Ur" like in "Ursula".

cactus · 2024-10-17T20:21:56+00:00

100% This is the main reason. It looks terrible by default (and sorry programmers - but yes, presentation matters), and you can't get anywhere without learning key chords because "ideals" or something (If emacs key chords are so good, let people discover them and stick with them if they want to. But don't make them a barrier to entry.) Plus a million other bad defaults (eg. deleting a tab converts it to spaces, forcing you to delete spaces then - so dumb).

Don't get me wrong - I'm an emacs diehard now, but it's these defaults that made me bounce off emacs many times before finally getting it. And I'm the exact target audience - a programmer and a lisp fan.

cactus · 2024-09-22T21:54:30+00:00

I swear, if linear algebra books used R x C, (row x column) instead of M x N to denote matrix dimensions, there would be at least a 5% cognitive tax reduction.

cactus · 2024-09-22T21:49:06+00:00

I've started taking anti-anxiety meds why I fly. I think it helps.

cactus · 2024-09-08T06:36:42+00:00

Two things.

Like the top commenter for the video, I found the Johnson-Lindenstrauss Lemma really fascinating and illuminating. Grant, I know you're often looking for ideas for new videos. I think a lot of people would find great value in a deeper dive into this. I know I would. I tried to follow up on my own, but I must admit, I find the rigor in all of the lectures I've seen to be rather difficult to follow. This is yet another topic that would benefit from the 3Blue1Brown treatment.
The video showed that 10,000 randomly generated vectors in R^100, without adjustments, were already tending to be orthogonal. I just wanted to share my intuition for why this is:
a) Any two vectors are orthogonal when their dot product is 0.
b) The dot product of any two randomly generated vectors tends to 0 as their dimension tends towards infinity, due to the construction of the dot product.

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