Zachtronics wrapped up because they all got a bit burned out by the yearly release pace, and Zach tried to become a teacher. He didn’t like it, and when the rest of the team continued making games, he joined up with them and thus Coincidence. Further down the discussion I shared a podcast where he tells the story.
I’ve heard a different reason for their presence in graveyards: because yew kills grazing mammals that eat it, it was cut down everywhere that people grazed animals, which excluded graveyards
You are the second person to respond to my question that’s entirely orthogonal to the actual AI usage here with a very self-conscious screed. Go read my responses to the first one :)
Do you think the only reaction to knowing you’re not the first to do something is not to do it? Do you think I said that?
To spell it out in case it is still non-obvious: knowing this allows iteration. It allows remixing. It allows you to inspect what has come before and what it did well and where it succeeded and where it fell short and thus what you could _add_. It is an enabler of creativity! Thus I think it is interesting that GenAI may make it harder to have this experience.
My thoughts are less about the merits of creating something that already exists than they are about _knowing_ you are doing that. Which I think my post made very clear :)
I feel like prior to GenAI, you’d have had to reckon with the true originality of your idea in some form as you did the research. Creatives having to confront their own unoriginality is such a thing it itself is reflected in countless pieces of media.
So it’s interesting to me that the creator here didn’t encounter the tens of physically published versions, or the hundreds of them shipped to digital app stores, or all the codebases on GitHub, in the course of making this. I’m sure they would have done naturally prior to GenAI. Is that good or bad? I don’t know! But it’s interesting to me.
“ can it build a game idea I've had for years, in a single shot?”
Do people do no research or introspection when they’ve had an “idea for years”? There are countless examples of this exact game. I played this on the Gameboy Advance! There’s like 50 of them on the App Store right now.
The standard “this almost certainly exists wholesale in the training data” applies, but I’m also interested in how you carry an idea for years and don’t notice this, or whether the “idea” here was actually “using this thing that’s been remade thousands of times as an AI benchmark”.
There’s nothing wrong with remaking an old classic formula, especially in game dev. It’s the describing it as “an idea I’ve had for years” that rings weird.
I have alas not published it yet, but I really should. How about you?
For me, on the research front, I’m very interested in methods that can be sustainably applied in remote, resource constrained locations. Heavy cloud dependent workflows to adapt a huge foundation model just aren’t practical on an island that doesn’t have 24/7 electricity and sporadic connectivity.
Thanks for sharing these resources and your story! I followed a very similar path, and ended up doing a biodiversity related MSc, with my dissertation being a custom classifier for poorly detected species in Príncipe. BirdNET and Perch are phenomenal achievements, but struggle in regions where, ironically, most of the world’s biodiversity is. What you’re doing for Rwandan species is so important!!
Motus is also used for bats and insects! The butterfly project the parent linked to is using Motus: “ This year, new receivers have been added to Motus towers around the Southwest as well as special nodes to pick up their signals.”
Got to take part in this when they ran it at Creative Coding Utrecht. They had brought a variety of clays for us to use, most wild dug from forests in Austria. But they also had some clay from deep beneath Vienna that they got from (iirc) some new metro digging. It was a lot of fun and the end artefact is very pleasing.
> The amount of training data doesn’t matter as much as we thought.
Seems a huge assumption to me. From the data one could equally conclude that JavaScript and Python have lower code quality _because_ the quantity of training data, e.g. more code written by less experienced developers
> I dunno if the author realizes, but all the things they mentioned did materialize in one way or another, just not exactly how the hype described it.
From the post, which is not a very long one: "All of the above technologies are still chugging along in some form or other (well, OK, not Quibi). Some are vaguely useful and others are propped up by weirdo cultists"