Making 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...
Build your First AI game bot using OpenAI Gym, Keras, TensorFlow in Python
This post will explain about OpenAI Gym and show you how to apply Deep Learning to play a CartPole game.
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.
Quickly Training Game-Playing Agents with Machine Learning
Posted by Leopold Haller and Hernan Moraldo, Software Engineers, Google Research In the last two decades, dramatic advances in compute and connecti...
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
How to Use AI to Do Stuff: An Opinionated Guide
Covering the state of play as of Summer, 2023
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
Mastering Real-Time Strategy Games with Deep Reinforcement Learning: Mere Mortal Edition
The capabilities of game-playing AIs have grown rapidly over the last few years. This trend has culminated in the defeat of top human players in the complex real-time strategy (RTS) games of DoTA 2…
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
Local AI
Resources for a local AI setup
Running local models on an M4 with 24GB memory | jola.dev
Why and How to Run Local Models in Zed
Running local models is good now