I cloned the backend for Truco and gave Claude a long prompt explaining the rules of Escoba and asking it to refactor the code to implement it.
Also, a refactor is by definition rewriting code without changing the behaviour. Worth knowing the difference.
I thought this was the start of a joke or something, I guess if you use LLMs you are a "LLM lover" then.
That’s why we’re not suddenly drowning in brilliant Steam releases post-LLMs. The tech has lowered one wall, but the taller walls remain. It’s like the rise of Unity in the 2010s: the engine democratized making games, but we didn’t see a proportional explosion of good game, just more attempts. LLMs are doing the same thing for code, and image models are starting to do it for art, but neither can tell you if your game is actually fun.
The interesting question to me is: what happens when AI can not only implement but also playtest -- running thousands of iterations of your loop, surfacing which mechanics keep simulated players engaged? That’s when we start moving beyond "AI as productivity hack" into "AI as collaborator in design." We’re not there yet, but this article feels like an early data point along that trajectory.
Each one would require a different kind of model and model technique to make, so I wouldn't be surprised that ChatGPT has issues with it. A sprite animation loop would be better done by a potentially specialized video-oriented model, for example, and the current image and video models are barely trained on that kind of video data.
We definitely saw an explosion of good indie games by around early half of 2010s. Whether it had anything to do with Unity is another moot point.
Maybe something else is currently holding back another bunch of good ideas in gaming. Once another threshold gets lowered, we will see another wave of good games enabled by by that, and a return to the average rate of creation again.
In the case you're working as part of team large enough to have dedicated programmers, the majority of the roles will usually be in content creation, design and QA.
How is AI supposed to simulate a player, and why should it be able to determine what real people would find engaging?
I don't think it's much of a stretch to take this data over multiple games, versions, and genres, and train a model to take in a set of mechanics, stats, or even video and audio to rate the different aspects of a game prototype.
I wouldn't even be surprised if I heard this is already being done somewhere.
Whether that set is actually useful is a separate issue but someone is trying this over there for sure.
Yes, that's how games like Concord get made. Very successful approach to create art based on data about what's popular and focus groups.
I think what the previous comment meant was that there is data on how player play, and that tends to be varied but more predictable.
Edit: yup, it shut down nearly a year ago
Compare that to Helldivers 2 (online-only live service game, same platforms and publisher) which had a lot of personality (the heavy Starship Troopers movie vibe) and some unique gameplay elements like the strategems.
Where we used AI (machine learning, not LLM) was in terms trying to figure out what kind of human you would want to play with. We also used machine learning to try figure out what cohort of players you were in so we could tweak engagement.
Where LLMs could really shine, in my opinion: Gamers love to play people, not AI (now). People are unpredictable, they communicate, they play well but in ways a human could (like they don't have superhuman reflexes or speed). You can play all kinds of games against AI (StarCraft, Civilization, training of all kinds of FPS) but it isn't fun for long because you see the robotic patterns. However, an LLM might be able to mix it up like humans, talk to you, and you could probably make it have imperfect reaction time, coordination, etc. That would really help a lot of games that have lulls in human player activity, or too much toxicity.
I would be shocked if some games aren't doing this now. It seems like it still be hard to make a bot seem human, and it probably only works if you sprinkle it in.
I'm sure you could conjure up any number of ways to do that, but they won't be trivial, and maintaining those tests while you iterate will only slow you down. And what's the point? Even if the unit-move-and-attack test passes, it's not going to tell you if it looks good, or if it's fun.
Ultimately you just have to play the game, constantly, to make sure the interactions are fun and working as you expect.
You use a second enemy that spawns, moves towards the "enemy", and attacks.
You can easily write a 'simulation' version of your event loop and dependency inject that. Once time can be simulated, any deterministic interaction can be unit tested.
Like letting speed runners skip half your game. :)
The real reason? It's because writing tests is a different skill and they don't actually know how to do it.
https://www.youtube.com/watch?v=AmliviVGX8Q (kovarex - Factorio lets fix video #1)
Games have goals, and players are prone to 'optimising the fun out of games', by doing some save strategy over and over again to reach that goal, even if it's not fun. Think eg grinding in an RPG, instead of facing tough battles with strategy and wits and the risk of failure.
Even if AIs are terrible at determining what's engaging, you can probably at least use them to relatively quickly find ways that you accidentally opened that let players get in the way of their own fun.
But we did? We've come a long way from the limited XBLA catalog. It didn't happen overnight, but doubtless we wouldn't have the volume of games we have today without Unity, Godot, Gamemaker, Renpy, RPG Maker...
I'm not sure the 2 of you are disagreeing. We definitely saw an explosion of indie games. In 2010, there were less than 10 indie games released on steam per month. By 2022, there were ~500/mo, and today there's ~750/mo (I expect that the 250/mo jump around 2022 can likely be attributed to LLMs).
What's hard to say is if this increase significantly increased the number of good games. Mostly because "good" is highly subjective, but also, I think something else happens. I've been playing games for the better part of 40 years, and what I noticed, is that in that time, the number of must play games each year has largely gone unchanged, despite the industry being orders of magnitude larger than it was 40 years ago. But that is also tricky, because 2 things happen every year, our standards get higher, and our preferences get more refined.
Think of the negative reputation the Unity engine gained among gamers, even though a lot of excellent games and even performant games (DSP) have been made with it.
More competitors does also raise the bar required for novelty, so it is possible that standards are also rising in parallel.
I don’t think we would’ve seen a Hollow Knight without Unity, built by a team of 2-3 devs.
"what's the point of vibecoding if at the end of the day I still have to pay a dev to look at the code anyway... I can't vibe my way through debugging, I can't ship anything that actually matters." [1]
LLMs are only different because the interface is more accessible. But all the same problems are still there. AI is not a panacea.
[1] https://www.reddit.com/r/ProgrammerHumor/comments/1mudy12/th...
This is not true at all. I have never worked on games and it will take me quite a while (even months) to write a "basic" game. While I know a lot of good practices about software development and decade+ of FAANG experience, I don't know the intricacies or even the basics of game development.
I recently experienced this for a different usecase. As an experienced backend developer, I wanted to automate some javascript/browser stuff. I tried on my own for 2-3 days and had couple of prototypes but nothing actually worked. I spent 2 hours with an AI and I had a working solution. We even iterated together quickly and solved some runtime issues and the solution is working for me seamlessly now.
So, I definitely see value of AI even for coding for experienced developers like myself.
You're contradicting yourself. I promise it wouldn't take you months, unless you're just a really bad developer.
And that's the part AI is not going to be able to help you with.
Just think of the speciality in which you aren't an expert, javascript/storage/networking ...
It can do this. From Atari games to StarCraft this has been a thing since before LLMs.
> surfacing which mechanics keep simulated players engaged
This it's unclear how to operationalize. Among other things, not all games appeal to all people.
AI is insane. It can do like 10,000 actions per second lol
Doesn't look exactly that to me. The author built a server, studied React, built a frontend, made the card game work.
Then, with most bits needed for a card game already in place, he asked Claude to alter the existing code to implement a different card game. Understandably, it took much shorter. But it would also take much shorter if a human engineer did the same.
I mean, the entire article is problematic as proof of anything. For starters, they didn't go through a design process for a game at all, they copied existing games. Then there are all these weird technical rabbit holes they went down that really weren't anywhere near "simplest path to MVP".
I just don't think there is anything to glean from this article. Like most posts about individual experiences with AI, it's functionally equivalent to, "I had a weird dream last night".
And this isn't surprising: git-style revision control hit the scene almost 20 years ago, it was like 5 years until it was totally dialed in anywhere, another 5 before elite companies had it totally figured out, and its been slowely diffusing since, today its pretty figured out. And this is harder to use right than git.
I think it would go faster actually if every product release, every OSS tool, every god-damned blog post wasn't hell bent on saying "its done, its solved, old way cooked, new world arrived".
We're figuring it out and it takes time. That's OK.
If it was done, then we'd be drowning in great software. We're not, we're breaking even, which is impressive for a big new thing 1-2 years in.
I think this is the core insight. An AI will not be able to experience a game (or anything else for that matter) remotely in the same way that a human can experience it. It might be able to guess, based on human rankings of other similar games. But AI will never be able to actually have fun playing your game.
This concept will define the workforce that comes out of this AI boom. Maybe an AI can write a document or code like a human, only based on past samples of similar behavior, but it won't be able to synthesize exactly what it means to be a human. The human element will still need to be traded on. Your value as a human cannot be replaced, you might just have to think differently about that value.
It's also much easier for you as a developer to digest the code if you need to refactor because you got lazy and accepted some slop.
my take: as a backend developer he was fixated with the idea of having a server no matter what (if your only tool is a hammer, everything looks like a nail), and as go was his main language he just went with that. then he figured out it didn't really have a point, but instead of just translating that simple logic to js he overengineered the whole thing and overcomplicated his design and build process by transpiling to wasm.
there are some bugs, though. i just won a couple of matches at escoba (very nice little game, i hadn't played this for ... decades!) and the game state wasn't properly reset for the next. that's probably the llm ...
I can't imagine state surviving a location reload with nothing on local storage and no server.
I've been participating in these for way longer than our current breed of LLMs or GenAI or engines. Back in the day, your best option was Microsoft's XNA and C#. (Unity had not been invented yet.) Plus most art looked like hand-drawn in paint, because it was. Still, we saw plenty of enjoyable games each year. And some made it to a wider audience, like Baba is You or Braid.
The coding was never the bottleneck. I strongly believe it's the communication among team members.
"Communication" within your own head is often also surprisingly difficult.
When I read code, it gets turned into something akin to an AST, where functions and calls are more of an abstract notion of input and result.
The LLM vastly simplifies the inverse process. Instead of having to go and find the way to represent an idea in code, or digging through my memory and code archives for an example, I can instead tell the LLM to write the boilerplate to initialize WiFi or whatever. It simplifies code into Lego pieces that I can assemble into a program that's more representative of my internal conception of the problem.
I've been flying through different programming languages with LLMs. When the text and grammar can be abstracted away, I can focus on the program. I was able to do this before LLMs, but with a lot more effort. Like, sure, I don't learn a whole lot about each language as I go, but that's exactly the point. The text and semantics of any one language should be largely irrelevant to the logical flow of the program itself. The text is nothing more than a semi-standardized way of representing logic in a form that can be parsed by machines and (secondarily) by humans.
First we abstracted machine code to assembly. Then low level languages like C, then higher and higher level languages until the machine itself became utterly irrelevant. It only seems natural that we'd further abstract these languages and move closer to the goal of programming instead of coding.
I don't think we've even seen hints of how this will look in the end, but I'm certain that we'll spend less and less time writing and more time programming.
AI is a tool and it’s clearer than ever to me now that the tool’s best job is abstracting tedious implementation details away from the developer, in much the same way that I never have to bother with manual memory management in my programs written in modern languages the way I once used to with c. We no longer need to deal with segfaults, so why not also no longer having to deal with tracking down documentation or examples to implement simple blocks of code.
overall I'd say around two working days. I used it as a test ground first for greenfield and then Brownfield development, so nothing serious, but I found myself in a loop of ever growing details I wanted tuned, more and more features (super rotations system, das, this and that).. I'd say it's maybe 10-20% of where I'd take it as a full game, even have a webgl version functional somewhere.. but I had to stop before I started making my ultimate Tetris since I don't want to get sued nor I have (probably) money for a full license to drive it through the end. I heard they charge a pretty penny.. but I am now confident I could drive it home and that's what I took, the experience, to other software development with llm now.
the other day there was an interesting link here on HN about parametric functions. I got an itch then as well, and within an hour or two a playground: https://www.susmel.com/graphy but also, details, details, details. If you know what you wanna see it's quite enjoyable actually.
We as a species are going to have to collectively decide it's not acceptable.
Coding is a hard part of game dev. Coming up with interesting novel mechanics or plays on known genres is rather easy but bringing them to life is hard esp the code. Multiplayer vampire survivors but with giant battletech mech customization. See, very easy. Good luck building that with an LLM.
This uses well known card games as the mechanics which is about as interesting as snake games. This is not a knock at the op. But it is clear many people here havent done much game dev from the comments.
Also, i press “x” to doubt you can build any game mechanic not because i have no doubt you are a talented engineer but because the domain is really that vast. Multiplayer netcode is a prime example and why I used an idea which had a multiplayer component that was realtime.
Ideas are cheap. Making them real is not. It is why engineers get paid so well.
I disagree. Sure, it's hard, but it's much harder to come up with novel and fun gameplay ideas. Once you have the fun idea, it's just a matter of splitting the problem in bite-sized chunks and iterating.
There is no methodology when you are faced with the dreaded blank page problem and need to come up with something out of nothing. Maybe going for a walk helps. Maybe taking a heroic dose of drugs. Maybe trying a few different things and see what sticks. It's a problem that has existed for millennia in all creative endeavours; whereas coding is "just" engineering.
I've been learning game dev the past month, had to learn a ton of maths to do anything, which was still easier than the question "what kind of game do I want to make?" which is still, to this day, unanswered. No 3Blue1Brown video is gonna help here, unlike learning how to do vector maths and what the hell is a quaternion.
Most of the comments lamenting the idea stage come from those who have not pushed past that. Once you do have an idea, a vision, that is when the real work begins. It is also the most difficult. For every completed game no matter how bad, there is a graveyard of thousands of incomplete projects that no one sees. People vastly underestimate the effort it takes to make a complete game.
If you do struggle with, “what kind of game”, go play games. Alot of games. Write about the games. Between the likes and didnt likes, is the kind of game only you can make.
npx http-server
Other than that. Loved the article and I love making games (haven’t shipped one in a while, I should)
It’s literally a one-liner of go to create an http server, http.FileServer that points to a directory.
go run http.go
python -m http.server
The guy goes through all this trouble with Golang, WASM, tinygo, just to field goal it on the 3 yard line.
We are judging the usefulness of a LLM in a project that is really over represented in training data (a bunch of Intro to Programming courses requires a project like this, and in southern Europe countries we have a lot of similar games like the one in the blog)
In the first year of college I reimplemented Moon Patrol in Python, I had no previous experience basically and it tooks me two/three months of work coding 3 days per week mostly.
Coding a card game is easier than that. LLMs are useful for certain things but this is not a good way to benchmark their usefulness in any type of game development coding.
This only leaves a few areas; niches which are both small and require very complex solutions; or software with low profit margins which have high risk of failure and short-lifespans (which is what most games are). Due to media saturation, for the former approach (complex niche), you basically have to market the solution to people door-to-door, one-by-one.
My experience of the game sector is that it's very difficult. Before you even begin coding, the definition of runaway success is basically "Attain a few million views, then watch traffic dry up completely as the game becomes completely irrelevant over 6 months." I could never get into games because knowing that you probably won't get recurring income is just too demoralizing as a starting point. Building a game like Minecraft is basically outside of the realm of possibility... Games like Minecraft, World of Warcraft are essentially 1 in a million games. You're better off just buying lottery tickets.
That said, I think the game sector seems to be more meritocratic than all other sectors of software that I'm aware of... Not sure that's saying much but I do think there is a correlation between quality and 'fun level' of the game and the short-term adoption of the game.
Most other sectors of tech are a maze of regulatory capture, network effect monopolies or the sector is fully government-controlled to begin with. It would be nice if governments would tell people "Don't do a startup in this sector because we already decided which company will control that sector." because it sucks to find out after building a solution for 1 year.
I cloned the backend for Truco and gave Claude a long prompt explaining the rules of Escoba and asking it to refactor the code to implement it.
But by LLM standards, feeding it the entire working codebase of a simple, similar, game you wrote by hand as context is basically doing all the work yourself still.
deadbabe•9h ago
all2•9h ago
leetrout•9h ago
tonyedgecombe•9h ago
all2•6h ago
macleginn•9h ago
schaefer•9h ago
So it’s interesting to think about what the gaps are between fulfilling a single prompt and completing a project.
shortrounddev2•8h ago
3036e4•3h ago
risyachka•9h ago
You may as well buy a shooter game starter pack or whatever that can save you >1year of coding, no llm needed.
Code is not a hard part.
Making mechanics fun and good assets is what is hard and takes forever.
Sure you can use llm to write a generic game, but its easier to find same game on github and just use that code, why would you write it again with llm.
deadbabe•8h ago
shortrounddev2•8h ago
sdwr•8h ago
hiAndrewQuinn•9h ago
TechSquidTV•9h ago
Having a good and semi unique idea, is a rare. If I had a great game mechanic idea, the rest would be trivial.
Say you do get a good game loop together that you feel will be successful. You will also now need to loop in art teams for artistic direction, music, character design, etc. A good game loop isnt enough, it needs to be presented in an equally interesting and unique way.
Finally, there is the risk. There is a massive time investment in making games, and you are catering to an audience that is not only accustomed to pirating but finds it morally righteous to steal your work. This is why app developers prefer to make iOS apps. The customers are accustomed to paying and have little interest in pirating.
post-launch and even before that, your job becomes paying and convincing streamers to play your game constantly in the HOPE people start to notice it.
All of this stress and work to hopefully just make an ok amount of money. I have so many excellent games in my steam library by indie devs that gave up after one or two very successful games. And I doubt it's because everything was going so well.
hackable_sand•36m ago
og_kalu•8h ago
https://steamdb.info/stats/releases/
mattmanser•8h ago
Which is really easy to argue it's more down to Unity + successors making game dev accessible as it starts in 2015.
No huge spike since Claude code got released or anything like that.
og_kalu•8h ago
Not really. The jump from 2023 to 2024 is bigger than the jump from 2019-2022 in raw numbers and 2020-2022 in %. So the jump of 3 to 4 years happened in a single year.
sarchertech•6h ago
Also the jump in 2024 is only around 10-15% more games than we would have expected from the previous trend. Assuming all of that is directly down to AI, I wouldn’t call that an explosion.
From what I’ve seen, most of the growth was in NSFW shovelware and was just people noticing a business opportunity. This also explains why the number it takes in 2025 isn’t showing similar growth.
og_kalu•5h ago
No we're not. Use Wayback machine or whatever and this year is 1k+ ahead at the same date.
https://web.archive.org/web/20240822090931/https://steamdb.i...
>Also the jump in 2024 is only around 10-15% more games than we would have expected from the previous trend. Assuming all of that is directly down to AI, I wouldn’t call that an explosion.
How many games do you imagine can be released per day even with the help of current Sota LLMs ? Nevermind the fact that you have to pay $100 to distribute your game on Steam. You're not making a game you'd pay $100 to distribute in 3 days, LLM help or not.
But fair, exploded is probably overstating it.
sarchertech•5h ago
>How many games do you imagine can be released per day even with the help of current Sota LLMs ?
Given the number of people who want to make games—if code is the bottleneck, and LLMs can really make you hugely more productive, I’d expect to see an actual explosion.
My experience is that neither of those assumptions are true though.
og_kalu•4h ago
Game development is not a zero sum game. There can be multiple bottlenecks or difficult hurdles.
>and LLMs can really make you hugely more productive, I’d expect to see an actual explosion.
Well growth was double the previous year. Maybe you might not call that an explosion, it's still a very noticeable uptick.
sarchertech•3h ago
I think it’s much more likely that LLMs don’t actually boost productivity all that much.
Profan•8h ago
Just look at something like ludum dare and all the top entries (out of thousands of games submitted) are all usually quite polished given the timespan.
sarchertech•7h ago
The open secret is that they might not start coding or building assets until the start time, but they have spent a lot of time thinking about the ideas before then (even when the "theme" isn't known before hand people tend to make ideas fit theme with tweaks), which just speaks to the "code is not the bottleneck" thesis.
MarceColl•6h ago
sarchertech•6h ago
deadbabe•2h ago
sheepolog•8h ago
1: Even with AI, it's a lot of work to make a full game. When most people think "I have a cool game idea", they're usually imagining something polished and non-trivial, possibly 3d. You could make a short text adventure in a few days with AI, or a very simplistic 2d game, but anything more ambitious (like 3d) is going to take a lot more effort.
2: Releasing on steam requires you to pay $100. I imagine this is a substantial deterrent for "3-day projects", unless you think it'll sell $100 worth.
3: There's more to game development than creating assets and writing code. The author of the article recreated an existing game, which sidesteps one of the most difficult parts of gamedev: design. Creating a compelling game is surprisingly difficult. Granted, you don't need a compelling game in order to release on steam, but I myself have made many prototypes over the years which I've abandoned because the idea just wasn't as interesting as I thought it would be.
4: I've made a few prototypes with AI assets, and one issue I frequently run into with image generation is: it still takes a fair amount of work to generate the same character in different poses, facial expressions, outfits, etc.
5: There is still considerable prejudice against using AI to make game assets. I think some people (myself included) are hesitant to release a game with lots of AI generated assets at the current moment, for fear of public backlash. Eventually that will calm down and it will become more socially acceptable to use AI to generate game assets.
I am bullish about AI improvement over the next decade, and I think we'll gradually see all of these issues resolve themselves as AI improves. But at the present moment, it's not quite as easy as the article makes it seem.
mattbuilds•8h ago
Also the idea that a dev who could making a game in 24 hour would create something professional and polished in 3 days is a joke. The answer to “where are all the games” is simple: LLMs don’t actually make a huge impact on making a real game.
socalgal2•8h ago
…Joking…. For now
rustystump•6h ago
mattbuilds•6h ago
rustystump•3h ago
shortrounddev2•8h ago
jayd16•8h ago
qnleigh•8h ago
znort_•6h ago
zerr•8h ago
Watching LLM generating the code doesn't help with producing the dopamine.
brookst•8h ago
Xss3•7h ago
Both groups wanted to make games.
maloga•6h ago
rustystump•6h ago
There are a bunch of games made using heavy gen ai but it is usually for art and dialogue. Most players can tell quickly and drop the game. Games are fundamentally creative things and most interesting art work was not done in 5s with a prompt.
Ekaros•6h ago
All of these take time and many of them are iterative processes where you might not even know if it fits or is right before multiple tries.
jama211•6h ago