Same orc village task, same prompt, same local stack on a €200 RTX 3060, but this time with Sonnet 5.5.
Wow. Sonnet made it, at a level close to Opus 5.5. The pose animations are more glitchy, which is the hardest part of the whole pipeline, but the scene holds together: models, motions, SFX, voices, all in a real project.
For context, the previous Sonnet couldn't produce anything working on a task like this. That's a big jump in one generation.
An orc village, built end to end by godogen in one shot by Opus 5.5.
Every model, texture, animation, sound effect and voice line was generated locally on a €200 used RTX 3060, with no asset APIs.
And this isn't a video model. It's a real Godot project, rendered live by the engine. 🧵
The setup: one scene prompt, Opus 5.5 running the whole pipeline, ~9 hours, no human edits.
Everything runs on a 12 GB home GPU:
- Qwen-Image 2.1 for concept images and textures
- TRELLIS.2 for image → 3D meshes
- Auto-rigging plus Kimodo for text → custom motions (hammering, chopping, shouting)
- Lip sync for talking characters
- Stable Audio for SFX and ambience
- Qwen3-TTS for stylized voices
Each part does a great job on its own, and Opus 5.5 was impressive at organizing all of them into one coherent scene.
It's not perfect. Motions aren't always precise, and held props sometimes drift off the hands. That's a matter of iteration, not a wall.
Why this matters: diffusion video gives you pixels. This gives you a game world: meshes, rigs, animations and audio you can open, edit and play.
github.com/htdt/godogen_asse…
What this means for godogen:
1) Local assets become the default path. Paid APIs stay as a fallback for what local can't do yet (animated 2D sprites, music, emotional voice acting) or where quality is critical.
2) Godogen games now have sound. Until now it was graphics only; now the agent generates SFX, ambience and voice lines along with the art.
It's on a branch while I test the local setup on more games: github.com/htdt/godogen/tree…
Retiring the snowboarder test: Fable 5.1 and GPT-6 Astra both one-shot it, no notes.
March 2026: the first reaction was "this is fake." September 2026: nobody blinks. Both the models and the expectations moved that fast.
cool idea. splitting reasoning and generation across a frontier + edge model, with the small one doing the writing locally, is the shape a lot of us want. waiting on the technical details.
meet @mostik_ai!
what happens when you put 12 PhDs in one room for four months? first place on the ARC-AGI leaderboard, which I can't say much about while the competition is still running. and this, which I can.
everyone's arguing about whether open models will catch up to frontier models. we think it's the wrong question. here's the one we pose: why does a frontier model have to generate your answer at all, when the only thing you need from it is the reasoning?
we do this by enabling models to communicate in latent space. through our protocol, hidden states pass straight from a frontier model into a small one running on your infrastructure -- no text between them, and neither model is fine-tuned. two models from different families, sharing reasoning, both left untouched.
how do we know it works? we tested it on a setup where a 753B model reads the problem, and a 4B edge-class model writes the answer. with this approach, we get results 80% as accurate as the frontier model, but at 20x faster performance.
we're committed to preventing frontier model lock-in and are already partnering with inference providers to accelerate open-weight adoption. we've done this between 15 of us, in four months, 12 PhDs and a Fields medalist, backed by @generalcatalyst
WIRED has the first external account of the company and the work: wired.com/story/russian-star…
full writeup, the setup, and all the numbers: mostik.ai/read-more
been building this loop since jan 2025 and the awkwardness shrank fast. first version was deterministic capture into a separate vision model that never saw the code. then models could do it themselves if you told them how. current version is one sentence: don't trust the code, verify visually. that's enough for opus 5 and gpt 5.6.
the gap left is choosing what to look at. picking screenshots is the same bias as reading your own code, you check the parts you expect to matter. playing start to end removes the choice.
We're starting to leave the territory where you'd test an LLM by e.g. "create an svg of pelican on a bicycle". As one idea to generalize it, I was interested what Opus 5 would do if I gave it the first paragraph of the Lord of the Rings, a 1M token budget (~$10) and asked for three js render of it. Opus went off for ~2 hours and wrote 5500 lines of code that (procedurally) rendered the story. It's kind of janky but fun. But it's a bit mindboggling that the LLM has to place and orchestrate various polygon assets in (x,y,z) coordinates and write code that animates it all, and that it even does anything at all.
I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom but LLMs have all the stamina and patience in the world, so it's an example where we go from "no one would ever do this" to "sure, why not, it's ~free". There might be a lot more. But I'm excited about creating hyper custom worlds that you can imagine dropping players into, e.g. here to participate in the LoTR story as a spectator NPC, or one of the characters, or etc. Something like an ephemeral GTA of X on demand.
Last thought is that the domain of worlds/games exposes a weakness in LLMs: they can't easily audit their work because they aren't able to efficiently and natively perceive videos or play games within them. Here, Opus 5 had to very slowly and painstakingly take screenshots at different points, and it messed up a few times and created a bunch of jank. An example of raw capability (multimodal, gameplay) that I think is still quite lacking.
Pushed Opus 5 as far as I could on gamedev. Gave it this video as a reference and asked for a Godot reimplementation.
Closer than I expected. Still a gap, and it's shrinking each release.
Not one-shot. ~10 iterations on small fixes. All assets generated by the godogen skill, fully autonomous. Character animation goes through my Kimodo adaptation github.com/htdt/kimodo-pract…
Not one-shot. ~10 iterations on small fixes. All assets generated by the godogen skill, fully autonomous. Character animation goes through my Kimodo adaptation github.com/htdt/kimodo-pract…
Opus 5, snowboarder test, one shot. On par with Fable, ahead of every other model I've run. No visual defects on the first pass, and the sliding physics feel right.
Snowboarder one-shot on Kimi K3 (OpenCode, Godot). Almost no visual bugs, task cleanly implemented. Better than Sol 5.6 and Opus here. @Kimi_Moonshot
my snowboarder test on gpt-5.6-sol-xhigh: slightly better than 5.5, but not a breakthrough like fable
New: We added a /game-dev skill to Manus.
You can now describe a game and Manus will build it for you.
Working gameplay, deployed to a link you can share with anyone.
No game development experience needed.
Thanks to @alex_erm for the open-source work that made this possible!
ran the same snowboarder task on plain claude code w/ fable 5 — no godogen skill, no planner, no docs.
didn't ask it to verify anything. it screenshotted and checked its own output at each step. the QA loop i built by hand — it just did that itself.
first godogen run on fable 5. one shot, no interventions, and the riding physics are actually fun. first time a game crossed from "somehow works" to "interesting to play."
Antigravity cli (Gemini 3.5 flash) as host agent for Godogen, one shot on Godot. Gemini cli failed the same build.
Google has been investing in vision for years. a Gemini host should have an edge there, and Godogen's verification loop runs on screenshots.






