Rigyd
Simulation-ready 3D assets for robotics simulation, at scale
What it does
Robot learning is only as good as its data. For sim-first teams, success depends on domain randomization. Train a humanoid in 1M simulated kitchens so it doesn't fail in the real one. Or train a robotic arm on thousands of objects of varying sizes, masses, and materials. Both require physics-accurate 3D content at scale. Most teams don't have it. Rigyd auto-generates and randomizes physics-accurate 3D objects and worlds at scale, unlocking sim-to-real scenarios that were previously impossible.
Does the same job
all alternatives →- PAPhysics-accurate 3D assets for robotics simulations from any inputApr 2026 · app.rigyd.com · ▲8
In the past 4 years, we developed a platform generating 3D visuals and deploying AR Try-on experiences into large footwear and retail companies. We focused on high-fidelity meshes and textures since the point was giving shoppers a realistic visual. Then robotics companies started to approach us and they asked for thousands of 3D assets that they can use in creating different simulation environments. This is crucial for them so they can achieve better sim-to-real transfer with domain randomization (train a humanoid in a million different kitchens so a real one is not a surprise when it is…





More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com


Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, May 2026
the whole month →

Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai


- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com