Neural Game Maker

Dazzle

Create and play games with AI

AI Coding is Gambling

GambleAI I’ve been coding a lot with AI since November, when we all noticed it got really good. And it is quite good for instantly generating something th...

Please Use AI

Be sure to use AI when making

pguso/ai-agents-from-scratch

Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.

Meet Foundry: An AI Startup that Builds, Evaluates, and Improves AI Agents

The Myth of the Instant Cake Mix

How to think about creative tooling in the new world of generative AI

The Fair Use Tango: A Dangerous Dance with [Re]Generative AI Models

By Neil Turkewitz

The Phoenix Architecture

Generative AI coding demands what we've always known: modularity, clear boundaries, disposable components. Principles that scaled human teams are now table stakes. Here, we make the implicit explicit

TextWorld: A Learning Environment for Text-based Games

We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.

Unpredictable Black Boxes are Terrible Interfaces

Why generative AI tools can be so difficult to use and how we might improve them

Learning to solve complex tasks by growing knowledge culturally across generations

Knowledge built culturally across generations allows humans to learn far more than an individual could glean from their own experience in a lifetime. Cultural knowledge in turn rests on language: language is the richest record of what previous generations believed, valued, and practiced, and how these evolved over time. The power and mechanisms of language as a means of cultural learning, however, are not well understood, and as a result, current AI systems do not leverage language as a means for cultural knowledge transmission. Here, we take a first step towards reverse-engineering cultural learning through language. We developed a suite of complex tasks in the form of minimalist-style video games, which we deployed in an iterated learning paradigm. Human participants were limited to only two attempts (two lives) to beat each game and were allowed to write a message to a future participant who read the message before playing. Knowledge accumulated gradually across generations, allowing later generations to advance further in the games and perform more efficient actions. Multigenerational learning followed a strikingly similar trajectory to individuals learning alone with an unlimited number of lives. Successive generations of learners were able to succeed by expressing distinct types of knowledge in natural language: the dynamics of the environment, valuable goals, dangerous risks, and strategies for success. The video game paradigm we pioneer here is thus a rich test bed for developing AI systems capable of acquiring and transmitting cultural knowledge.

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Why so many game developers don't want to use generative AI

With credits ranging from Dispatch and Marvel Rivals to Uncharted and Dragon Age, over 30 devs share their thoughts on gen AI

The Omg Lol Times · We have to talk about AI “art”

Note: In this post I’m going to use the term “AI” as a lazy shorthand for “generative content creation via large-scale pre-trained models” because time is tight and I’ve got arthritis. I know it’s not really “AI”, but please, let me give my typing fingers a break here.