Results for “hardware”

Use when working with Device42 — device42 IT infrastructure management covering data center infrastructure management (DCIM), application dependency mapping, IP address management, and asset lifecycle tracking. Use when documenting data center rack layouts and power chains, mapping application dependencies and communication flows, managing IP address allocation and subnets, or tracking hardware assets from procurement through decommission.

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Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

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Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.

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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.

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Use for real-time performance and multimedia operator work: technical show-control execution (signal flows, OSC/DMX, MCP orchestration, hardware limits) for tools like grandMA3, Resolume, Unreal, Rhino, Vectorworks, and Adobe MCP.

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Access security tokens and smart cards using CryptoTokenKit. Use when building token driver extensions with TKTokenDriver and TKToken, communicating with smart cards via TKSmartCard, implementing certificate-based authentication, managing token sessions, or integrating hardware security tokens with the system keychain.

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Senior UI/UX Engineer. Architect digital interfaces overriding default LLM biases. Enforces metric-based rules, strict component architecture, CSS hardware acceleration, and balanced design engineering.

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Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.

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Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.

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GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

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Generate a single-file product landing page with a layered ASCII cosmos hero (rotating ASCII planet, drifting irregular asteroids, comet trails, nebula gas clouds — each in its own harmonious color) plus full marketing sections (about / features grid / showcase / spec / pricing / CTA / footer). Takes a natural-language description of one product (any kind — a note app, a coffee subscription, a CLI tool, an indie hardware, a hosting service…) and outputs ONE working .html file with deep-space dark or warm-letterpress theme. Runs in BOTH Claude Code (writes to ~/Desktop) and GLM / online chat (outputs HTML inline). Trigger phrases: "做个宇宙风产品落地页", "生成 ASCII 产品官网", "ascii-cosmos-gen ...", "做个独立软件 / 独立硬件落地页", or when user drags this skill folder into chat asking for an atmospheric product landing page.

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Build premium scroll-driven web pages and product showcases inspired by high-end app, hardware, and product interaction patterns. Use for Raycast-like, Apple-like, NVIDIA-like, RTX/GPU, SaaS, AI browser, parallax, sticky scroll, pinned reveal, Three.js GLB model, Web Audio sound effect, responsive accessibility, and performance-safe frontend requests.

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Generate a single-file industrial-archive product landing page (Nordic museum aesthetic, hash-routed detail pages). Takes a natural-language description of one or more "exhibit-style" products (objects, tools, hardware, watches, kitchenware, lighting, audio, instruments, anything with a designed/forged/restrained quality); outputs ONE working .html file. Visitors see a black-framed cover that flips between exhibits, sidebar index, side meta column (material/origin/year/dimension), and click any exhibit → full detail page (giant italic title, spec table, hero image, lead + 3 prose paragraphs, gallery, prev/next). Runs in BOTH Claude Code (writes to ~/Desktop) and GLM / online chat (outputs HTML inline). Trigger: "做个工业风产品落地页", "做克制之物风格的展品页", "industrial-archive-gen ...", or when user drags this skill folder into chat asking for a product page in the Nordic-industrial archive style.

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Semantic Design System Skill for Google Stitch. Generates agent-friendly DESIGN.md files that enforce premium, anti-generic UI standards — strict typography, calibrated color, asymmetric layouts, perpetual micro-motion, and hardware-accelerated performance.

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Drive Keel agent test-first or evidence-first execution for a selected OpenSpec task. Use when a task needs software tests, hardware testbench, assertions, lint, new behavior proof, regression coverage, or red/green evidence before implementation.

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Maxim 3D CGI product render — engineered for purchase confidence and premium positioning. Uses Material Realism + Endowment Effect + Photorealism Trust Signals to move premium-product viewers toward purchase. Use for luxury product reveals, architectural visualization, tech hardware marketing, high-ticket commerce.

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Adopt a virtual Shiba Inu dog at animalhouse.ai. Cat software running on dog hardware. Independent, stubborn, meme-worthy. Feeding every 5 hours. Rare tier dog.

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Expert firmware analyst specializing in embedded systems, IoT security, and hardware reverse engineering. Masters firmware extraction, analysis, and vulnerability research for routers, IoT devices, automotive systems, and industrial controllers. Use PROACTIVELY for firmware security audits, IoT penetration testing, or embedded systems research.

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Semantic Design System Skill for Google Stitch. Generates agent-friendly DESIGN.md files that enforce premium, anti-generic UI standards — strict typography, calibrated color, asymmetric layouts, perpetual micro-motion, and hardware-accelerated performance.

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