Acerola has a bunch of great graphics videos. In this one, he talks about why PS1 graphics looked the way it did.
I learned that PS1 actually had realtime camera distance tessellation – something that wasn’t available to desktop GPUs until the introduction of tessellation shaders.
Gamelogic does a decent intro to a few simple edge detection shaders used in toon-style rendering.
8 years ago Tenkai Games Dev Room made a cool ASCII nethack-like prototype, and has only gotten like 60k views. It’s amazing how things like this exist yet nobody has seen them.
Ron decided to learn to code in 2024. He proceed to use AI to vibe-code a game called Letterlike. It’s now one of the top ranked mobile games on Steam and the #1 paid word game on Android.
He tells his story on this reddit post.
Vibe coding is here. People are building viable commercial products with less than a year of coding experience. Sure this isn’t a solution that needs a lot of security like an online service, but here it is.
David Gewirtz reviews a bunch of AI tools for coding; and does some ranking.
Article:
not-matthias has blogged his journey on writing a kernel driver with Rust.
He has a few other fun hacky posts about reverse engineering a digital jukebox in a bar to vote for his songs.
The author’s of the 3D Math Primer for Graphics and Game Development book have provided their intro book for free online. It’s basic for those getting started, but nothing beats free and the author has done a number of GDC talks.
Google published a report on it’s effort to migrate code to the latest dependencies – an often thankless task fraught with risk. Google’s code migrations involved: changing 32-bit IDs in the 500-plus-million-line codebase for Google Ads to 64-bit IDs; converting its old JUnit3 testing library to JUnit4; and replacing the Joda time library with Java’s standard java.time package. The 32-bit ID’s were particularly rough because they were often generically defined types that were not easily searchable.
They used a collection of AI tools as well as manual code reviews and touch-ups to achieve their goal. They emphasize that LLMs should be viewed as complementary to traditional migration techniques that rely on Abstract Syntax Trees (ASTs), grep-like searches, Kythe, and custom scripts because LLMs can be very expensive.
The results?
With LLM assistance, it took just three months to migrate 5,359 files and modify 149,000 lines of code to complete the JUnit3-JUnit4 transition. Approximately 87 percent of the code generated by AI ended up being committed with no changes. For the Joda-Java time framework switch, the authors estimate a time saving of 89 percent compared to the projected manual change time.
Links:
Mr-Figs asked a great question on the reddit gamedev forum: how do you handle making your own tools needed to make a game?
It used to be that building a game also meant building all the authoring tools to go along with it. With the advent and spread of game engines like Unity, Unreal, Godot (and literally hundreds of others) along with amazing tools like Photoshop and Blender, the need to make your own tooling has dramatically decreased. Almost to the point that in a majority of cases, you probably don’t need to write tools.
Even if you do find you can’t use an existing tool, others suggest using chatGPT to either extend an existing tool or a tool in the engine you’re using via their SDK. Let AI do the work for you since tools are not shipping code nor need to be overly performant.
Strict_Bench_6264 wrote up a whole blog article to describe what he learned:
After analyzing nearly 10 years of CVEs, Google researchers calculated that at least 40% of safety exploits in C++ were related to spatial memory exploits like writing to an out-of-bounds memory location.
Google researchers showed they were able to “retrofit” spatial safety onto their C++ codebases, and to do it with a surprisingly low impact on performance. They used straightforward strategies such as bounds checking buffers and data structures – as is done in other languages and released a new, safer Hardened libc++.
The results show up in this chart of segfaults across the entire fleet of computers before and after using the improvements. Their internal red team testing results were also much improved, uncovered over 1000 bugs and likely prevent 1000-2000 new bugs each year based on current development rate.
Here’s a blog post about their results.
Articles: