NVIDIA built a AI that nukes 30 years of animation technology.
They open-sourced a model that generates 350,000 motion skills in real-time, at 15,000 fps with just 2ms latency.
It’s called MotionBricks, an AI framework built to generate real-time character and humanoid motion at scale.
→ Intuitive smart primitives for quick scene authoring.
→ Integrated into NVIDIA's GR00T robotics stack.
→ Zero mocap. Zero rigging.
→ Runs in real-time
From Tool Use to Tool Creation
Going further, AI shouldn't just be able to:
Use existing tools.
A truly advanced agent should be able to judge:
That a suitable tool for solving the problem is currently missing.
Heads up for Codex CLI users: avoid 0.143.0 for now.
Related issue: openai/codex#31665
With GPT-5.5, Codex may fail before shell execution:
unsupported call: exec_commandexec_command
Even pwd / ls can fail before reaching the shell.
Workaround:
codex -m gpt-5.4
@shulmnn@hookuru_ Yeah, I noticed that too. The immersion feels much stronger now. A quick bullet-time beat before the final cut-in—hazy at first, then snapping back into full color—could make it feel like coming back to reality.
@Vivek4real_@grok How is this curved line generated, and how did you create such an elegant curved road editing tool? How are the boundaries stored? Does it support dynamic snapping and matching to the curved road edges, or is it done by manually dragging?
This shift represents a move toward higher-dimensional AI experiences, where models are beginning to genuinely understand and navigate 3D environments.
These days, AI is reaching a new stage. Models such as Fable 5 or GPT-5.5 and other frontier systems are approaching the level of intelligence needed for many tasks performed by the general public.
Some of these worlds may run inside game engines. Others may be rendered directly in the browser with technologies like WebGPU, Three.js, MCP-connected tools, and similar platforms.
Starting today, I'll try to post more in public.
I want to share my AI projects, product ideas, and learning process with more people.
Build in public. Learn in public.
lv1-lv3 I have a triple-layer safeguard to ensure my tokens run smoothly. For tiers LV2 and LV3, I use a custom-built, LiteLLM-style proxy to handle the varying formats across different models—since standard token limits fall short and the Pro plan is too expensive.