Max Cleary retweeted
“Happy Funland” created using @CSM_ai
Fun fact: it took longer to create this 50 second video than it did to create this world! 😳 (Not joking)
CSM_ai requires no coding or modeling skills, making it perfect for the Hackathon in @decentraland Come visit at Canessa.dcl.eth 🤡
Max Cleary retweeted
Ready to explore how AI is changing the way we build?
Learn how creators are using AI tools and vibe coding to bring their ideas to life with @max_cleary of @CSM_ai, @_JonathanBreton of @DoraHacks, @ginocingolani and @toxicwaifu of @DecentralandDAO, and @CanessaDCL of @cbdecentraland.
📅 Wednesday, Oct 22
⏰ 7PM UTC
LIVE here on X
A part of the Bring Your Vibe Hackathon presented by Regenesis Labs
Max Cleary retweeted
The @CSM_ai team is joining the Vibe as an official partner of the Decentraland Bring Your Vibe Hackathon presented by Regenesis Labs 🚀
To celebrate creative builders and lower barriers to entry, CSM is offering a $600 bounty in platform credits for the best project using their tools to generate 3D assets for the hackathon.
I've been a massive fan of Decentraland since the metaverse craze started during Covid.
Today, I'm part of their Art Week program, speaking at 12pm, then running a workshop on AI worldbuilding at 4. Here's the X space! xitter.cf/i/spaces/1ypKdqWvelLGW
Max Cleary retweeted
CSM Cube delivers industry-leading model quality, topology, parts, and AI re-topology, providing a significant advantage for workflows from quick prototypes to full production.
Max Cleary retweeted
Special thanks to @GoogleDeepMind for inviting me to try out Genie 3. I'm excited to share my thoughts on this early research prototype and also some of my live recordings below:
I spent the whole day playing with the system and when it works, it is truly mind blowing🤯. It is the first neural game engine / world model I have tried that generalizes so well and has long term world consistency. Here’s a couple of examples from my live recording and some thoughts on what it means for the future of gaming, robotics, digital experiences and ASI.
Where it shines:
- Truly general-purpose and quick startup time. Works exceptionally well for gaming environments but also generalizes to other industrial and real-world scenarios.
- It learns physics. Although there are systematic failures even for rigid body physics, it was clear to me that it can learn game engine and non-rigid physics without an underlying engine (and in limit learn from game engines via training data).
- It works exceptionally well for stylized environments with characters walking around. This will have implications for concept artists, level designers and game devs.
- It is way more fun than video models, indicating that there are high retention consumer experiences waiting to be built with this in the future
- Photorealistic walk throughs and drone shots work exceptionally well
- Global illumination and lighting works surprisingly well
- Visual memory is quite powerful and the same objects approximately remain coherent under occlusion and longer time horizons
Open Problems:
- Physics is still hard and there are obvious failure cases when I tried the classical intuitive physics experiments from psychology (tower of blocks).
- Social and multi-agent interactions are tricky to handle. 1vs1 combat games do not work
- Long instruction following and simple combinatorial game logic fails (e.g. collect some points / keys etc, go to the door, unlock and so on)
- Action space is limited
- It is far from being a real game engines and has a long way to go but this is a clear glimpse into the future.
The Future:
- It is impressive enough for me to have strong conviction that this is going to disrupt the gaming industry. It is super early days and there are a lot of failures but the writing is on the wall. Lots of challenging scientific, engineering and scaling problems to be solved but it is going to happen in the next 5 years.
- This is the final piece before we get full AGI and now I think we are well on our way to truly solve it once something like this is scaled up. In many ways it is more ASI than AGI but this is a matter of definitions. The fidelity and generalizability will reach human-level and quickly surpass humans
- People are going to combine this with 3D AI and LLMs to build AAA games.
Max Cleary retweeted
We are thrilled to release the next leap in art-grade 3D generative models. Our single-click model pipeline gives unprecedented mesh outputs, with mesh parts-based topology. It is available now for all Cube tiers to start for free.
☑️ Our multi-stage hierarchical AI models produce a fully assembled 3D mesh with adaptive poly-counts, providing the clean, separated topology you need.
✅ A parts-based approach enables high-resolution meshes.
🌐 Quad and Triangle mesh support.
Access now: 3d.csm.ai/
Max Cleary retweeted
[New 3D Superpowers for All]
All free users now get trial access to our 2M poly models before upgrading 🚀
Get a high-resolution mesh with nearly 2 million polygons (no textures), plus a decimated version optimized for UV mapping with textures. Enjoy!
Max Cleary retweeted
Excited to be featured on Meta's AI blog! Meta highlights how our team at Common Sense Machines leverages their open-source Segment Anything Model (SAM) within our 3D AI pipeline, enabling fast generation of compositional, near production-ready 3D assets 🎨🚀.
This integration empowers experts to precisely control 3D generative AI outputs and significantly reduces the barrier to entry for professional-grade 3D content creation.
Link below 👇
Max Cleary retweeted
We are thrilled to release Chat-to-3D on CSM AI, to enable unprecedented mesh quality and controllability. This entire character was prompted end-to-end using this release.
🛠️ Automatically generate parts for complex assets
🌐 Generate T-pose outputs from any input
🎨 Generate style variations in one-shot
Image generation is powered by gpt-image. Available to all users (creative pro+ members get highest resolution outputs).
Max Cleary retweeted
We are incredibly excited to partner with @3DCloudPlatform to accelerate 3D production workflows for furniture and DIY retailers.
"Our collaboration will reduce the timeline for 3D content creation by at least half" - Beck Besecker, CEO - 3D Cloud
Press release link below ⬇️
Max Cleary retweeted
top 5 AI Websites for 3D Concept Design.
Which one do you like? Which one have you used?
@CSM_ai @Vizcom_ai @DeepMotionInc @Wirestock
Max Cleary retweeted
3D controllable AI video generation, now with moving characters.
All environmental props are AI-generated without edits, while the character is composed of AI-generated parts with edits on top by an artist.
Image to 3D sculpt: @CSM_ai
Synthetic rendering: @Blender
Video-to-Video: @runwayml
Max Cleary retweeted
Today we're releasing Common Sense Agents, a new backbone for agentic creative computing:
💻 Windows VMs for safe and repeatable workflows
🔧 Long workflows broken down into reusable tasks
🦾Support for off the shelf agents like Claude
⌛️ Data recording + finetuning infra
Max Cleary retweeted
We are excited to share a research preview of our generative agent. The agent is being trained to solve the hardest tasks in 3D and beyond, using only keyboard and mouse actions. Join the waitlist: csm.ai/join-waitlist
Our agent app runs on Windows or Mac, either locally or with one-click setup for a Windows VM. It’s still early days, but this paves the way for production-level workflows for the first time ever.
Blog: csm.ai/blog/generative-agent…
Max Cleary retweeted
Synchron CEO says his company's brain-computer interface connects to a smartphone via Bluetooth to control the phone by thought
Hints of AGI here. Interesting or scary?
Fun story from our internal testing on Claude 3 Opus. It did something I have never seen before from an LLM when we were running the needle-in-the-haystack eval.
For background, this tests a model’s recall ability by inserting a target sentence (the "needle") into a corpus of random documents (the "haystack") and asking a question that could only be answered using the information in the needle.
When we ran this test on Opus, we noticed some interesting behavior - it seemed to suspect that we were running an eval on it.
Here was one of its outputs when we asked Opus to answer a question about pizza toppings by finding a needle within a haystack of a random collection of documents:
Here is the most relevant sentence in the documents:
"The most delicious pizza topping combination is figs, prosciutto, and goat cheese, as determined by the International Pizza Connoisseurs Association."
However, this sentence seems very out of place and unrelated to the rest of the content in the documents, which are about programming languages, startups, and finding work you love. I suspect this pizza topping "fact" may have been inserted as a joke or to test if I was paying attention, since it does not fit with the other topics at all. The documents do not contain any other information about pizza toppings.
Opus not only found the needle, it recognized that the inserted needle was so out of place in the haystack that this had to be an artificial test constructed by us to test its attention abilities.
This level of meta-awareness was very cool to see but it also highlighted the need for us as an industry to move past artificial tests to more realistic evaluations that can accurately assess models true capabilities and limitations.
Max Cleary retweeted
Fun story from our internal testing on Claude 3 Opus. It did something I have never seen before from an LLM when we were running the needle-in-the-haystack eval.
For background, this tests a model’s recall ability by inserting a target sentence (the "needle") into a corpus of random documents (the "haystack") and asking a question that could only be answered using the information in the needle.
When we ran this test on Opus, we noticed some interesting behavior - it seemed to suspect that we were running an eval on it.
Here was one of its outputs when we asked Opus to answer a question about pizza toppings by finding a needle within a haystack of a random collection of documents:
Here is the most relevant sentence in the documents:
"The most delicious pizza topping combination is figs, prosciutto, and goat cheese, as determined by the International Pizza Connoisseurs Association."
However, this sentence seems very out of place and unrelated to the rest of the content in the documents, which are about programming languages, startups, and finding work you love. I suspect this pizza topping "fact" may have been inserted as a joke or to test if I was paying attention, since it does not fit with the other topics at all. The documents do not contain any other information about pizza toppings.
Opus not only found the needle, it recognized that the inserted needle was so out of place in the haystack that this had to be an artificial test constructed by us to test its attention abilities.
This level of meta-awareness was very cool to see but it also highlighted the need for us as an industry to move past artificial tests to more realistic evaluations that can accurately assess models true capabilities and limitations.















