Learning to Play Tic-Tac-Toe with Jax

A technical article published on January 4, 2026, details the process of learning to play Tic-Tac-Toe using Jax. The content was shared on Hacker News, a popular platform within the Y Combinator ecosystem. The post garnered 4 points and 0 comments, indicating a modest level of engagement from the community.

The article focuses on the intersection of machine learning and game theory, utilizing the Jax library for implementation. It serves as a resource for developers interested in applying machine learning techniques to simple games. The lack of comments suggests the content may be highly technical or niche, appealing primarily to readers with a specific interest in Jax and reinforcement learning.

The article titled "Learning to Play Tic-Tac-Toe with Jax" was published on a personal blog. It addresses the application of the Jax library in a machine learning context. The specific focus is on training a model to master the game of Tic-Tac-Toe.

Content on the web is often shared to communities for discussion. In this instance, the article was posted to Hacker News. This platform is known for sharing technical news and startup culture, often linked to Y Combinator.

The reception of the article on Hacker News was measured by standard metrics. The post received a total of 4 points. This score reflects the number of upvotes minus downvotes from users.

Interestingly, the discussion thread contained 0 comments. This indicates that while some users found the content valuable enough to upvote, there was no public discussion or questions raised in the comments section. This can often happen with highly technical content that requires deep expertise to critique or discuss.

The core subject of the article is the use of Jax. Jax is a Python library designed for high-performance numerical computing and machine learning research. It is often used for its ability to differentiate code efficiently.

Applying Jax to Tic-Tac-Toe demonstrates how complex libraries can be used for simple logic puzzles. This serves as a foundational exercise for understanding more complex reinforcement learning tasks. The article likely covers the setup, training loop, and evaluation of the model.

The article is accessible via its direct URL. It remains available for reading by anyone interested in the topic. The lack of comments does not affect the availability of the technical information presented.

Readers looking to explore the code or concepts can visit the linked blog post. The post represents a specific snapshot of technical blogging on January 4, 2026.