When looking at Arena and Wilderness Encounters and for h in hexes, you start wondering about things like this:-
https://www.redblobgames.com/grids/hexagons/
So thanks very much to redblobgames!
Remarkable, Incredible and Amazing nerdiness
When looking at Arena and Wilderness Encounters and for h in hexes, you start wondering about things like this:-
https://www.redblobgames.com/grids/hexagons/
So thanks very much to redblobgames!
https://deltasdnd.blogspot.com/2021/12/wilderness-simulator-stats.html
https://github.com/danielrcollins1/WildernessEncounterSim
“One more reflection on the Original D&D wilderness encounter charts. Last week we were using some tabulated charts to decide between two possible rules interpretations, and one was clearly much nicer. But that was based on just looking at the average EHD (Equivalent Hit Dice) for each encounter type, which is maybe a little sketchy. Since I’m obsessive about these things, I wrote a simulator program that actually rolls up the individual encounters (varying the number appearing by psuedo-random dice), and I had it spit out a thousand random encounters for each terrain type.”
This all looks like pretty reasonable results to me!
https://github.com/danielrcollins1/Arena
Looking forward to giving this a shot [with hopefully minimal swearing at java]
This code package provides routines for simulating combat in a tabletop Fantasy Role-Playing Game (FRPG) similar to Original D&D or closely-related games. Combat is done as per “theater of the mind” without tracking exact spatial locations; targets of attacks are chosen by random method (as per 1E AD&D DMG). In most cases, the intent here is to output aggregate statistics based on many trials of the game between men and monsters. This package provides only command-line, text output; there are no graphics or visualizations, and generally few options for output regarding individual combats.
For a precompiled JAR executable made from this package, and full JavaDoc pages, visit:
A very cool collection of resources to make maps and add randomly generated content to them :- https://github.com/kensanata/hex-mapping
Also for Traveller.
Possibly holidays could disappear here:…
This and Alex’s other game stuff can be found here:
https://alexschroeder.ch/wiki/RPG
Which can lead you down this enjoyable rabbit hole:
https://cosmicheroes.space/blog/index.php/2019/01/30/old-school-rpg-planet/
It was by such a fate as this that Muad’Dib met his own destiny. There was no opportunity to prevent this, no chance to save himself. The fall of a statue is an apt metaphor for the fall of a human spirit in his case. When we look at it, do we see a great mountain? Or perhaps a vast and chaotic chasm? Or a small, but solid, mass of stone?
—from ‘Songs of Muad’Dib’ by the Princess Irulan
He is an emperor like none who ever held power, a wise man to whom others are but a servant, a king of truth and a leader of power. He is not a warrior, but a wizard.
—’Muad’Dib: The God King of Arrakis’ by the Princess Irulan
It was once our Emperor Shaddam I was born. Shaddam I was a great warrior. He was a mighty ruler, a mighty warrior. His enemies could not stand against a warrior like Shaddam. And he had no enemy of the first rank. But his enemies were his friends.
—from ‘Manual of Muad’Dib’ by the Princess Irulan
In similarfashion to the previous post, however this time the prompts used were rooms 4 to 6
FRESCO OF THE WIZARDLY WORK ROOM, THE ARCH OF MIST, THE FACE OF THE GREAT GREEN DEVIL
This gives us a different room 7.
Again, that is pretty good – and a nice title for an area.
GPT-3 appeared last year:
Given that was not made openly available like GPT-2, others made an open source version, hence we have GPT-J and GPT-Neo, the latter being relevant for here.
I have taken the 1.3B parameter model and thanks to HuggingFace and their model implementations, used this Colab notebook.: https://colab.research.google.com/drive/1H2mUZsYhel4g5ZUOmZHtYIaP4gSW5_ow?usp=sharing#scrollTo=eBIcgwE1kVQK The 2.7B parameter doesn’t seem to work here – runs out of memory. Not surprising as it is 10GB.
I thought a fun holiday project would be to use this to generate soom module type descriptions.
There are certain limitations of how long the text prompt for the model to predict from can be, so for a first try I took the three entryway possibilities for the Tomb of Horrors.
Here’s the output:
As you can see, that is pretty good for autogenerated based on a few paragraphs prompts from a generic model trained on close to a terabyte of stuff from the internet, and not tuned to the experience as in something like AI Dungeo – https://play.aidungeon.io/. It even recognised the room numbering of 1, 2, 3 and continued on. It is generating text, so not going to finish sensibly all the time. Not much work for a DM to join those together and edit a sentence or two though.