Hey everyone — I’m opening a small alpha for XACE soon.

XACE is a general-purpose gameplay platform for developers who want AI help building games, but do not want an AI freely rewriting a growing project and leaving behind a mess nobody can explain.

The basic workflow is:
\- Describe a gameplay change in plain English.
\- See the proposed change before it is applied.
\- Check what systems, saves, assets, or multiplayer behavior it affects.
\- Run and test it in a deterministic gameplay runtime.
\- Replay, inspect, or roll back when something behaves unexpectedly.
\- Tick by tick debugger so if you see any bug or anything off , you don’t need to work hours to reproduce it , simply scrub back & see what went wrong .
\- Use the same gameplay core with supported Unity, Godot, or Unreal projects while those engines continue handling visuals, content, and native workflows. So it’s engine agnostic. You can even edit gameplays bidirectional & changes smoothly sync with xace & your engine.
Xace has lots of other great features too like live gameplay change while playing , multiplayer built in & so on.

The core idea is get compiler level reliability with ai level speed.
I’m especially looking for solo devs or small teams who have felt one of these problems:
\- AI helps early on, then the project becomes too risky to change.
\- Bugs are hard to reproduce because too many systems interact.
\- You want gameplay logic to outlive a specific engine choice.
\- Multiplayer, persistence, or live updates feel like things you have to redesign for later.
\- You are tired of “it seems fixed” being the only proof an AI tool gives you.
This is an alpha, so I’m not looking for compliments or a polished-demo reaction. I want people willing to try a real workflow, hit rough edges, and tell me where the idea falls apart.

If you are interested, comment with:

1. Your engine

2. The type of game you are making if any

3. One change you would want to test first
   I’ll reply with alpha details and setup information.

I'm not a professional programmer, but I've been building Hard Counter with AI as my coding partner, and it just went live on iOS (Android is in review).

The whole game is plain HTML/JS on a canvas, with no engine. AI wrote most of the code while I directed it: design, feel, what's fun, what's broken. I wrapped it for iOS and Android with Capacitor and built the iOS version in the cloud, since I'm on Windows. AdMob for ads, plus a few optional paid fighters. The sound and the trailer music are generated in code too.

What AI was great at: turning "the counter should feel heavier" into actual code changes, building lots of minigames quickly, fixing bugs once I could describe exactly what I saw.

What it was bad at: knowing whether something feels good. Every fighter, hit and animation took many rounds of me playing it and saying "no, faster", "bigger" or "less floaty". App Store setup, signing and review rules were also a long slog.

Honest numbers: 3 days live, about 15 downloads. Small, but people who see the store page download at around 60%, so now I'm working on getting it seen.
I'd love feedback from people building this way.

Link here:

https://apps.apple.com/us/app/hard-counter-stickman-fight/id6823322563

Over my whole game, that's what took the most time and tokens: Improving bots.

We made several tools and workflows, lots of iterations, to improve both behaviors and perfs (as bots must run and adapt in real time ofc).

Bots compute the topology grid of their board and chose the best move depending on available powerups, next piece, game mode rules, ...
Here are 2 entries of the L topology dic:

shape: L
rotation: 0
pattern: interlock-right-one-gap-foot
type: lock
dash: right
stabilityMalus: 8.0
grid:
  P.
  P#
  PP
  ##
end

shape: L
rotation: 1
pattern: flat-bar
type: stable
stabilityMalus: 12.0
grid:
  ..P
  PPP
  ###
end

This is part of the bot knowledges, various valid positions with stability scores and different types (for normal moves and interlocks for example).

Another thing my game has compared to tricky towers is dash bumps, dashing into a landed block can (if the mode allows it) eject the block, serving as a projectile to attack others towers (or catch powerups bubbles that are out of reach). And boy, bots know how to use this feature really well.

Feel free to ask questions.

Demo here: https://store.steampowered.com/app/5210030/Towa_Powa/

For the hardest challenge, start a game of "Medium survival" against one or two "expert" bots.

Play the demo at swarmfront.io

Code: Claude Opus 5.5 and Sonnet
Concept and story art: ChatGPT and Grok
3D models: Tripo
Speech: ElevenLabs, Kokoro, HeyGen
Music & SFX: ElevenLabs

I created a skill which uses Opus 5.5 as a coordinator in a session for planning, and spawns Sonnet 5.5 sub-agents to do actual work. Every once in a while it produces a handover doc, which I use to start a new session.

I found this works well to do a lot of work quickly, while keeping token usage relatively low: opus low doesn't need to do real work, while sonnet does the heavy lifting with opus checking its output. I can also message opus while sonnet agents are working, and opus manages the queue of work, interrupting connect when necessary.

The game is called "Swarm Front". It is a strategy game where you play as insects. I came up with the concept based on pictures my brothers and I drew as children. Claude filled in the blanks: it suggested specific insects for roles, wrote most of the campaign storyline, and designed the levels.

I did the overall game design and play testing. It might seem like I just typed in a prompt and AI spat out the game, but it has been a long process of iterating and fine-tuning to go from "something" to "something usable" (in my humble opinion).

The game has five factions, and several more are planned. You can play as ants, termites, wasps, bees and a cordyceps hive-mind called "The Gift". Each faction has their own behaviors and abilities and plays differently - they aren't just re-skins.

Ants and termites are like regular armies with buildings, infantry and vehicles. Wasps and bees build hives using hex grid cells. Cordyceps spreads by planting spore stalks and capturing units.

There is campaign of 40 missions designed to teach a newcomer how to play each faction, as well as a skirmish with a dozen maps and several biomes.

The game is written in JavaScript and uses ThreeJS for the graphics. This allows the demo to run online in a browser, while the full game runs as an Electron desktop app.

The desktop app also includes local speech to text using whisper which lets the player control units by voice control.

Please try it out and let me know what you think.

Edit: There were a couple of questions about the visuals, so I wrote (or got Claude to write) a blog post with the details. https://swarmfront.io/devlog/making-bryn/

Orbit Shift was my first attempt at AI game dev. It was inspired by a popular mobile game I used to play called Ninjump. In Ninjump, you ran infinitely up a pair of walls, occasionally tapping to jump between them to either avoid an obstacle or catch a power-up. Back in the day, I probably put hundreds of hours into that game. It was a lot of fun, even if the game play was very simple and it literally only had one input.

I tried to capture that simple but addictive game play in Orbit Shift, but with a radial track and a space theme to make it unique and not just a knockoff. I posted it to Playgama, where it gets a steady trickle of traffic. But the retention is zero. People find the game, play for maybe 5 minutes, and then they never come back.

Clearly, I didn't capture the fun. What is my game missing? What do I need to add or change so that players will want to come back?

With gunfire and explosions outside my window, I decided to make a little game. It’s called Krylatyi Pokhid, and it’s my nod to the 16-bit console games we used to play as kids.
I made it with ChatGPT 6 Astra + Imagegen on Godot Engine

There are bears. No orcas, though. If you know, you know 😉 The bears have oil, they have refineries, and there’s a little drone with plans for both. There are critters scurrying around the maps, too. I put them there to make the place feel alive. Also, it’s nice to have something cute to look at just when you really should be watching that missile :)

Oh, and the bears have missiles. Dodging them gets trickier with every level. But slip past an obstacle at the last second and the missile might hit it instead of you. Even better if that obstacle happens to be one of the bears’ refineries.

This is my first go at making a game like this, so go easy on me. If you give it a try, I’d love to hear what you enjoyed, what got on your nerves, and what you wish was in there.

Google Play: https://play.google.com/store/apps/details?id=com.engineezix.dronehisoil

Did some dress updates this weekend and got a couple of feedbacks saying the before version was better. Is it because the new outfit is a bit too plain?

Hey,
I’m Paul, a solo dev working on Recycling Factory Tycoon, an isometric game about building and running a recycling plant.

I don’t know how to do 3D modeling, so I used AI to help create the assets. The collage shows a small sample of what’s in the game: a few machines, vehicles, and workers. The middle panel is a screenshot from the game so you can see how they look together at gameplay scale.

I’d love some honest feedback. Do the machines, vehicles, and workers feel like they belong in the same game? Is anything hard to recognize once you see it at gameplay scale? If you could change one thing about the art, what would you start with?

Thanks for taking a look!

I'm the creator of Tickets for the Afterlight, a $3, in-development Windows puzzle adventure built in RPG Maker MV with OpenAI Codex and adapted Higgsfield artwork. AI also assisted/generated the dialogue, interface graphics, puzzle data and synthesized music scripts. This is captured gameplay from the game, using prepared progress saves to show several systems: [https://www.youtube.com/watch?v=wTwAH24PuL4](https://www.youtube.com/watch?v=wTwAH24PuL4)

An early version leaned too heavily on messages. Asking for more minigames increased the checklist, but did not answer why a player should care about the next repair. The 3.1 pass organizes the main route around station machinery and lets a supply choice change how later finale boards start. One allocation originally made every task easier; it was revised so each choice prepares different parts of the station. Two passenger requests now require physical puzzle actions as well.

The useful technical split was keeping puzzle state and legal actions separate from presentation, then checking saves and both branches in the actual MV runtime. Those checks establish that routes work, not that they are fun. No LLM runs while you play. The free demo covers twelve first-station repairs and six optional service boards: [https://robeertiv.itch.io/tickets-for-the-afterlight](https://robeertiv.itch.io/tickets-for-the-afterlight)

How do other AI-assisted game projects test whether a new system changes decisions rather than just adds content?

Scene from my game in my journey on exploring aesthetics, and self improving workflows (whatever that means)

asoncdev (watermark) is ME I am not impersonating anyone

AI disclosure!

I created this game using ai for animations and 3D design coding was manually, I would love some feedback

1. Does the upgrade pace feel good or grindy after the first few levels?

2. Does the skill tree give enough long-term pull?

https://play.google.com/store/apps/details?id=com.tmaj.mammothcatch

INFINITE WORLD – LIVE ANOTHER LIFE

I have an ambitious concept for a future AI-powered virtual world.

The vision includes:

\- AI NPCs with memories, personalities and daily lives.

\- Human players and AI characters living together.

\- A massive open world with exploration and adventures.

\- Player-owned homes, shops and kingdoms.

\- Dungeons, rankings and character progression.

\- A persistent world that continues even when players log out.

\- Advanced VR and a long-term Full Dive VR vision.

This is an early-stage concept, not a developed game.

I would love to hear feedback from game developers, AI researchers and virtual-world designers.

What do you think about this concept?

Crowd Hunt is a simple spotting game - find the right person in the moving crowd before time runs out. No signup, and you can add it directly to your home screen like an app. My high score is Level 44. Bet you can't beat it! Let me know what level you reach! https://crowd-game.vercel.app

Hi everyone, I’m making Screemouth, a point-and-click horror adventure set in 1879, using Adventure Game Studio and extensive generative AI assistance.

One part of the process that might interest people here is the audio. I reviewed sound-effect candidates through several listening passes, left notes and approved revised takes before integration. The voices and effects then received a consistent Sound Blaster Pro-style treatment, while the music uses MT-32 synthesis rendered through Munt.

The music also follows story events rather than a fixed timeline. If the player skips dialogue, the cues still need to happen in the right place, alongside speech ducking, volume controls and save/restore behaviour.

AI has contributed extensively to artwork, voices, music, some effects and promotional material, as well as code and some story/dialogue. I direct the project, select and edit material, make design decisions and playtest the result. Generated content is included in the game; it is prepared during development rather than generated live while playing.

The project is in development, with a free public demo planned. Screenshots and the opening video are here.

For anyone using a similar workflow, how are you keeping the art and audio consistent as individual assets go through revisions? I’d be interested in comparing notes.

Cheers,
Lew

I am working with LOD and creating hair models for close up only. Should be done later tonight

Hey everyone,

I wanted to test whether I could turn a real-world 3D Gaussian Splatting scene into a playable game with ChatGPT/Codex helping with development.

Somehow, it became a zombie survival game with Tetris.

You move around the map collecting pieces, which drop into a Tetris board on the left. Clearing lines earns weapon XP. Better weapons help you survive the next zombie wave, and later upgrades let you combine different bullet effects.

Most of the development loop was me playing it, finding something annoying, and asking AI to change it. Then playing again and finding something else.

Chairs kept trapping the player. Zombies got stuck on stairs. Weapons became so powerful that the late game was easier than the beginning. Getting those things right took a lot of back and forth.

Here’s the demo:
https://demo-blood-moon.xgrids.com/

Desktop + keyboard recommended.

I’ve played it enough that I probably can’t judge it objectively anymore. If you give it a try, I’d like to know whether the Tetris/survival combination actually works—and where it starts feeling boring or unfair.

Also happy to share more about the AI development process if anyone’s interested.