Clawbernetes – Replace kubectl with conversation (Rust)
Clawbernetes turns OpenClaw into an AI-native infrastructure manager. Instead of YAML, Helm charts, and kubectl — you have a conversation. "Deploy Llama 70B on the node with the most VRAM" → agent selects the best node, pulls the image, starts the container with GPU passthrough, sets up health monitoring. "Why is inference slow?" → checks GPU temps, VRAM, CPU load. "GPU 0 at 89°C — thermal throttling. Want me to reduce batch size?" 23 crates, 74K lines of Rust, 1,866 tests, zero unsafe in core. Supports CUDA, Metal, ROCm, Vulkan, and CPU SIMD. Components: - clawnode: node agent with 80+…
What it does
In the maker’s words, at launch
Clawbernetes turns OpenClaw into an AI-native infrastructure manager. Instead of YAML, Helm charts, and kubectl — you have a conversation. "Deploy Llama 70B on the node with the most VRAM" → agent selects the best node, pulls the image, starts the container with GPU passthrough, sets up health monitoring. "Why is inference slow?" → checks GPU temps, VRAM, CPU load. "GPU 0 at 89°C — thermal throttling. Want me to reduce batch size?" 23 crates, 74K lines of Rust, 1,866 tests, zero unsafe in core. Supports CUDA, Metal, ROCm, Vulkan, and CPU SIMD. Components: - clawnode: node agent with 80+ commands (deploy, secrets, autoscale, networking, jobs, metrics) - 14 skills that teach the AI agent infrastructure ops - Fleet plugin for multi-node orchestration - MOLT: P2P GPU compute marketplace (Solana SPL) MIT licensed. https://clawbernetes.com https://github.com/clawbernetes/clawbernetes
Does the same job
all alternatives →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 · 26d ago · cactuscompute.com

