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Daniel Botha argues that conventional benchmarks reveal little about how models behave in long-context, interactive situations or whether they deliver the tone a product requires. He presents games as a more observable evaluation setting, because success signals are unambiguous and gameplay exposes strategic reasoning, long-term planning, and adaptation with opponents or collaborators. The post focuses on a fork of a16z-infra’s AI Town, a pixel-art simulation inspired by Generative Agents: Interactive Simulacra of Human Behavior, where characters remember conversations, maintain relationships, react to events, and stay in character. A single deploy script runs the project on Fly Machines with scale to zero, cost and performance optimizations, and support for OpenAI-compatible services, Together.ai models, and custom embedding models configured through secrets.
Conventional benchmarks are described as insufficient for assessing how models behave in the wild, especially with long contexts, dynamic interactions, strategic decisions, and the tone or feel required by a product experience.
A fork of a16z-infra’s AI Town provides a single deploy script for Fly Machines, with scale to zero, cost and performance optimizations, and configuration for OpenAI-compatible services, Together.ai models, and custom embedding models through secrets.
Games provide observable, unambiguous success signals while testing strategic reasoning, long-term planning, and dynamic adaptation.
AI Town evaluates conversational behavior through characters that remember conversations, maintain relationships, respond to new situations, and remain in character.
The fork supports scale to zero on Fly Machines and can test OpenAI-compatible services, Together.ai models, and custom embedding models.
Summaries are generated from the original by a language model. Check the original before relying on it.
See the idea
AI Town is a pixel-art simulation framework where autonomous agents maintain memories, relationships, and goals across time, creating observable environments for testing how models behave in long-context interactive settings.
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