Where did the demand that every game must be continually updated come from – and is there a way to avoid the friction it causes?
Building the Future of Games on ATProto | Birbhouse Games
Game platforms hold too much power over developers. We want to change that by decentralizing games with ATProto — devs own their data, players own their progress, and no payment processor can take it away.
Bungie confirms it’s ending Destiny 2 updates to focus on new games | VGC
The end of Destiny updates follows declining player numbers…
Games Done Quick's Long, Difficult Journey To A Better Gaming Future
Progress isn't linear, and there are no skips or shortcuts
Ico, disruptive game design, and experiences that live forever.
100 updates later. Why we enjoy games, why we stop, and how live game design might bring us back.
Fast and Reliable Video Game Collections | Myrient
Experience an unmatched video game archive with fast downloads and no ads.
It's Time To Rethink Everything by @t3dotgg
Not just development, distribution of software may change as well - <antirez>
Learn about Lore: next-generation open source version control
Maintained by Epic Games, Lore is designed for unprecedented scalability of both data and teams. It’s optimized for projects that combine code with large binary assets.
Play old games: They're good and good for you
With prices skyrocketing and more and more bad news about contemporary video games every day, here's a suggestion: play old games!
Only seven PlayStation games have sold more than 100,000 physical copies in the US this year, analyst says | VGC
Sony will no longer be releasing new games on disc from January 2028…
Differ - Adaptive Software
Build software that adapts to each user — without forks or feature flags. One codebase, infinite variants.
Differ - Adaptive Software
Build software that adapts to each user — without forks or feature flags. One codebase, infinite variants.
How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve across the software development lifecycle has not been thoroughly investigated. This study aims to characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle. Using the AIDev dataset, we first analyze how differences in merge rates between agentic and human generated PRs vary over time. We then identify the types of development tasks where AI coding agents are predominantly applied and investigate how these task distributions evolve across development quarters. Finally, we compare a set of key characteristics of agentic and human generated PRs, focusing on their implications for software quality and their temporal dynamics. Overall, our findings provide an empirical and longitudinal perspective on the role of AI coding agents in software development, offering a more nuanced understanding of their benefits and limitations in real-world practices.
