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nmatter1
smallville
Java2023–2023
nmatter1 / smallville

smallville

Generative Agents for video games. Based on Generative Agents: Interactive Simulacra of Human Behavior

2Distinct people
3Mentions
0Explicit recommenders
836GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Generative Agents for video games. Based on Generative Agents: Interactive Simulacra of Human Behavior

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by hour3 dated mentions

Observed mentions: 2023-05-17 19:41:16 UTC to 2023-05-18 12:59:42 UTC.

0122023-05-17 19:00:00 UTC to 2023-05-17 20:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsMay 17, 19:002023-05-17 20:00:00 UTC to 2023-05-17 21:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-17 21:00:00 UTC to 2023-05-17 22:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-17 22:00:00 UTC to 2023-05-17 23:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-17 23:00:00 UTC to 2023-05-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 00:00:00 UTC to 2023-05-18 01:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 01:00:00 UTC to 2023-05-18 02:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 02:00:00 UTC to 2023-05-18 03:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 03:00:00 UTC to 2023-05-18 04:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMay 18, 03:002023-05-18 04:00:00 UTC to 2023-05-18 05:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 05:00:00 UTC to 2023-05-18 06:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 06:00:00 UTC to 2023-05-18 07:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 07:00:00 UTC to 2023-05-18 08:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 08:00:00 UTC to 2023-05-18 09:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 09:00:00 UTC to 2023-05-18 10:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 10:00:00 UTC to 2023-05-18 11:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 11:00:00 UTC to 2023-05-18 12:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-18 12:00:00 UTC to 2023-05-18 13:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsMay 18, 12:00

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
May 17, 19:002023-05-17 19:00:00 UTC2023-05-17 20:00:00 UTC110
May 17, 20:002023-05-17 20:00:00 UTC2023-05-17 21:00:00 UTC000
May 17, 21:002023-05-17 21:00:00 UTC2023-05-17 22:00:00 UTC000
May 17, 22:002023-05-17 22:00:00 UTC2023-05-17 23:00:00 UTC000
May 17, 23:002023-05-17 23:00:00 UTC2023-05-18 00:00:00 UTC000
May 18, 00:002023-05-18 00:00:00 UTC2023-05-18 01:00:00 UTC000
May 18, 01:002023-05-18 01:00:00 UTC2023-05-18 02:00:00 UTC000
May 18, 02:002023-05-18 02:00:00 UTC2023-05-18 03:00:00 UTC000
May 18, 03:002023-05-18 03:00:00 UTC2023-05-18 04:00:00 UTC000
May 18, 04:002023-05-18 04:00:00 UTC2023-05-18 05:00:00 UTC000
May 18, 05:002023-05-18 05:00:00 UTC2023-05-18 06:00:00 UTC000
May 18, 06:002023-05-18 06:00:00 UTC2023-05-18 07:00:00 UTC000
May 18, 07:002023-05-18 07:00:00 UTC2023-05-18 08:00:00 UTC000
May 18, 08:002023-05-18 08:00:00 UTC2023-05-18 09:00:00 UTC000
May 18, 09:002023-05-18 09:00:00 UTC2023-05-18 10:00:00 UTC000
May 18, 10:002023-05-18 10:00:00 UTC2023-05-18 11:00:00 UTC000
May 18, 11:002023-05-18 11:00:00 UTC2023-05-18 12:00:00 UTC000
May 18, 12:002023-05-18 12:00:00 UTC2023-05-18 13:00:00 UTC202

What Hacker News says

3 sources
A few thoughts: 1. It's interesting, but realistically I'm unlikely to get into the Java code where all the interesting logic is 2. My impression of the code is that all the interesting stuff is in World.java [1] and the many models [2] 3. I don't see obvious prompts in here. I'm guessing they are somewhere, though also built dynamically (maybe mostly through string concatenation). In my experience it's essential to be able to see and understand the prompts, both abstract/template prompts and the instantiated prompts and responses. Also, having done it both ways, I find that keeping prompts separate from code is helpful. They feel different and you iterate on them differently. 4. You should be wanting to get to a place where most of your changes are prompt-driven. That is, you may be changing code, but only because you are refactoring you
Read the rest
r prompts in some qualitative way. With some good DSL you can even support qualitative changes, like changing the chain, or making a prompt multi-stage. 5. My intuition is that an important feature of GPT to these applications is the ability to apply multiple frames to the environment. Which is a fancy way of saying many different prompts that include different system prompts and different purposes. But I can't really find the prompts here so I don't know what's happening. 6. The paper this was based on was really inefficient with calls to GPT, costing according to the paper thousands of dollars to run an in-game three-day trial. I think you're going to want to deal with that right away. You want to move more execution out of the LLM (for instance by having the LLM come up with higher-level instructions that are carried out by simpler algorithms). Also you probably need more chattiness to allow the LLM to indicate what information it wants, instead of providing the LLM with all the subjective information a character has. Anyway, a few thoughts, good luck! [1] https://github.com/nickm980/smallville/blob/main/smallville/... [2] https://github.com/nickm980/smallville/tree/main/smallville/...
In Show HN: Smallville – Create generative agents for simulations and games
nm980·May 17, 2023Submission

Show HN: Smallville – Create generative agents for simulations and games

Smallville can be used to create NPCs with the same level of realism as human players without having to pre-program interactions. The agents store and retrieve past memories which they use to create plans so they can decide where to move, what to say, and how to react to observations. Agents are also capable of interacting with the world around them to change the state of objects on their own. This project was intended to make it easy for anyone to create custom simulations and my attempt to recreate Generative Agents: Interactive Simulacra of Human Behavior. I’ve been working on Smallville for the past few weeks and hope other people also find it useful. Would love to hear any thoughts about the project and where I should take it from here.
112 points · Open discussion

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