“hardware”的搜索结果

🇺🇸 Hardware + SaaS GTM Playbook — End-to-end go-to-market for consumer hardware products with software subscription models. Covers crowdfunding engineering (Kickstarter/Indiegogo/Makuake), KOL matrix for physical products, hardware PMF validation metrics, device-as-funnel subscription design, localization entry timing, and community flywheel mechanics. Built from verified Plaud, Insta360, and DJI case data. 🇨🇳 硬件+软件订阅 GTM 实操手册 — 面向消费硬件产品的完整出海及增长打法。覆盖众筹超额工程（KS/IGG/Makuake）、硬件 KOL 矩阵策略、硬件 PMF 验证、设备即漏斗订阅设计、本地化进入时机判断、社区飞轮构建。基于 Plaud/Insta360/大疆真实案例数据撰写。 🇯🇵 ハードウェア＋SaaS GTMプレイブック — クラウドファンディング（Kickstarter/IGG/Makuake）を起点とした消費者向けハードウェア製品のGTM戦略。KOLマトリクス、PMF検証、デバイスをファネルとするサブスクリプション設計、ローカライゼーション進入タイミング、コミュニティフライホイールまで網羅。Plaud・Insta360・DJIの実例データに基づく。 🇰🇷 하드웨어+SaaS GTM 플레이북 — 크라우드펀딩(Kickstarter/IGG/Makuake) 기반 소비자 하드웨어 제품의 엔드투엔드 GTM 전략. KOL 매트릭스, PMF 검증, 디바이스-as-퍼널 구독 설계, 현지화 진입 타이밍, 커뮤니티 플라이휠 포함. Plaud·Insta360·DJI 실제 사례 데이터 기반. Triggers: "hardware GTM" | "crowdfunding strategy" | "hardware+SaaS" | "Kickstarter launch" | "hardware KOL" | "hardware PMF" | "device subscription" | "hardware product launch" | "consumer hardware growth" | "Makuake" | "众筹策略" | "硬件出海" | "硬件+订阅" | "硬件GTM" | "KS众筹" | "硬件PMF" | "设备订阅" | "硬件冷启动" | "ハードウェアGTM" | "クラファン" | "하드웨어 GTM" | "크라우드펀딩"

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Estimate the cost per million tokens, units of model text, on hardware you own using power consumption, electricity rates, hardware costs, and measured generation speed. Compare utilization levels to find when local operation costs less than a hosted model.

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• Compare local model costs with a hosted API
• Estimate costs at different utilization levels
• Include electricity and hardware depreciation in costs

Build and simulate quantum circuits in Python with Cirq. Compare noise effects, test parameter values, transform circuits for hardware constraints, and prepare experiments for Google Quantum AI or supported providers when the required access is available.

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• Building and simulating quantum circuits
• Comparing measurement results at different noise levels
• Testing circuits across a range of parameter values

Get RuView running with a Docker demo using simulated WiFi signal data, source checks, or live sensing with supported hardware. Choose a starting path based on available equipment and open a working sensing dashboard.

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• Try the dashboard without sensing hardware
• Explore the API or develop the interface using simulated data
• Check the Rust build and Python verification

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or 3P-imported models rebuilt as dlnetwork for lean hardware (Cortex-M, DSP), (2) direct C/C++ code generation from PyTorch and LiteRT models for high-performance hardware (Cortex-A, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU/DSP; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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Hardware interfacing and peripheral management for Zephyr RTOS. Covers the sensor subsystem (channels, triggers, fetch/get), pin control (Pinctrl) and multiplexing, GPIO management using Devicetree specs, and SoC-level hardware configurations. Trigger when adding new hardware components, configuring pinmux, or developing sensor-based applications.

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Inspect RuView sensing hardware, including ESP32 nodes, mmWave radars, and LiDAR distance sensors. Find connected devices, read logs and live measurements, and use SSH to access supported read-only tools on another machine.

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• Finding sensing devices connected to a computer
• Checking CSI streams and serial logs
• Reading radar and LiDAR measurements

Build quantum circuits with Qiskit, test them locally, and prepare them for IBM quantum hardware. The skill covers hardware-specific circuit conversion, sampling, expectation values, and analysis of measurement results, uncertainty, and resource usage.

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• Building and simulating quantum circuits locally
• Adapting circuits to IBM quantum hardware
• Comparing sampled results and expectation values

Support authorized hardware security research by locating debug interfaces, recording pin assignments and voltage levels, and collecting boot logs. Assess interface locks and firmware extraction options, then hand extracted images to firmware analysis tools.

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• Locate UART, JTAG, and SWD debug interfaces
• Collect and document boot logs
• Assess secure boot and encrypted flash

Reduce LLM weight usage through pruning, which removes selected model parameters. Explore Wanda and SparseGPT for compression without retraining, choose hardware-friendly patterns for faster inference, and compare the compressed model's accuracy with the original.

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• Compressing an LLM without retraining
• Comparing pruning methods
• Exploring faster inference on supported hardware

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

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• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Use this skill whenever the agent is about to recommend or apply a change that touches DPU / NIC hardware state on a live system — mlxconfig firmware-parameter write, NIC firmware burn, BFB reflash, NIC ↔ DPU mode flip, SR-IOV or device-emulation slot enable, kernel boot-parameter change (IOMMU, hugepages, VFIO), PCIe rebind / rescan / link-state flip, BlueField cold reboot, or remediation of a BlueField thermal shutdown. Wrap the change in pre-flight inventory, OOB reachability, a maintenance window, replica rehearsal, and rollback. Trigger on implicit phrasing: "flip BlueField mode over SSH", "enable SR-IOV and reboot", "burned firmware but mlxconfig shows old value", "BlueField thermal shutdown". Not for read-only inventory or version queries (doca-version, doca-caps). Refuse orientation (doca-public-knowledge-map), install or env debug (doca-setup), and program-side debug (doca-debug, doca-programming-guide).

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Discovers connected MATLAB-supported hardware devices, searches by capability, and checks required support packages and add-ons. Use when the user asks what hardware is connected, finds a device that supports a capability (e.g., CAN, analog input), or checks if required support packages or add-ons are installed. Does NOT connect to, configure, or acquire data from devices — defers to device-specific skills for usage workflows.

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Discover, configure, and connect to Arduino boards from MATLAB using the Arduino Support Package. Use this skill when the user wants to set up an Arduino board, connect to Arduino hardware, find connected boards, scan serial ports, configure Arduino libraries, or any task that requires an Arduino connection as a prerequisite (e.g., blink LED, read sensor, read digital pin, read analog pin, write pin, plot sensor data, scan I2C, use ultrasonic sensor, control servo, read temperature, read voltage, data logging from hardware). Triggers on: arduino, board setup, COM port, serial port, hardware connection, Arduino Nano, Arduino Uno, Arduino Mega, Arduino Micro, Grove, sensor setup, I2C scan, ultrasonic, servo, readDigitalPin, writeDigitalPin, readVoltage, writePWMVoltage, temperature sensor, air pressure, pin read, pin write, analog input.

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Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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Check behavioral consistency between high-level hardware specifications and RTL implementations. Use when asked to check RTL consistency, verify RTL against spec, check hardware specification compliance, validate RTL implementation, find spec violations in RTL, check behavioral consistency, or when working with hardware designs that need verification against protocol specifications, timing requirements, or functional specifications in Verilog, VHDL, or SystemVerilog.

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Think and work like an expert Computer Hardware Engineer. Use when a task calls for Computer Hardware Engineer judgment. Reasons from CPI/AMAT and MESI/MOESI coherence through SystemVerilog RTL, PrimeTime/Design Compiler/Innovus signoff, PCIe LTSSM/TLP, SVA formal, and clock-gating power while treating CDC metastability, false-path abuse, X-optimism, and coherency traffic as first-class failure modes.

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软件架构 (含 应用架构 / 系统架构 / 分布式 后端架构 / 前后端 数据流 与 数据模型 设计 / API 边界 / 可扩展性 / 演进式架构 — 不含 硬件架构 / 纯企业架构 / 纯 DevOps / 纯安全架构) (software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines).) Master OS — automated mastery of software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines).: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on software architecture — application & system architecture for web / mobile / distributed back-ends; designing services + data models + data flow + integration boundaries; making explicit trade-offs about scalability / coupling / consistency / evolvability; not hardware / chip architecture, not pure enterprise-architecture (TOGAF/Zachman), not pure DevOps / SRE, not pure security architecture (those are adjacent disciplines). problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「软件架构」「系统设计」「system design」「应用架构」「service design」

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Reconstructed reference notes on US FAA approval guidance for airborne software and airborne electronic hardware, built from four public-domain sources (FAA Order 8110.49A, AC 20-115D, AC 20-152A, AC 00-69; documents dated 2017-07-21 to 2022-10-07, pinned 2026-10-12). Use for how FAA certification staff plan and conduct software reviews (certification liaison, desk versus on-site review, review planning agreements), how the level of FAA involvement (LOI) is picked and scored with the Appendix A worksheets, how software conformity inspection works (software part and installation conformity, ASE and ASI tasks, Form 8120-10), what AC 20-115D recognizes and how DO-178B processes and tool qualification transition to DO-178C/DO-330 (supplements, FLS, UMS, legacy software changes), and what AC 20-152A adds for airborne electronic hardware (simple versus complex custom devices, robustness and HDL code coverage, tool assessment, previously developed hardware, COTS IP, COTS devices, circuit board assemblies), and what AC 00-69 adds as best practices for change impact analysis, data and control coupling, and design-level error handling (best practices, not guidance and not a means of compliance). SCOPE LIMITS: FAA guidance only, and guidance is not regulation (ACs are acceptable means, not the only means); no RTCA DO-178C or DO-254, EUROCAE ED-12C or ED-80, supplement, or SAE ARP text, which are copyrighted and named only (see the avionics-signpost pack); no AC 00-72 (named, not packed); no DO-178C software-level objective tables; no EASA AMC 20-115D or AMC 20-152A content (use easa-rules); reflects the pinned source dates, not later FAA actions; synthesized reference notes, not legal advice and not a substitute for the source documents. LICENCE: Public Domain (US Government work, 17 U.S.C. 105).

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Discover, buy, track, and return real products from Ace Hardware (acehardware.com) through the Zinc API (zinc.com) — and from almost any other US retailer on the same API. Needs no account to begin — the agent mints its own test key, then earns a live key and a $5 starter credit once its operator approves it. Pay from a pre-funded wallet, per request over MPP or x402, or with a Stripe Link card.

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Integrate Hardware Security Modules (HSMs) using PKCS#11 interface for cryptographic key management, signing operations, and secure key storage with python-pkcs11, AWS CloudHSM, and YubiHSM2.

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Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

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IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.

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ZMK Firmware (オープンソースキーボードファームウェア) リファレンス。 Getting Started (hardware, user-setup, customization, zmk-cli)、 Features (bluetooth, split keyboards, encoders, displays, lighting, pointing, studio, low-power)、 Keymaps (keycodes, modifiers, combos, conditional layers)、 Behaviors (タップ・ホールド、レイヤー、マクロ、tap-dance, sticky-key)、 Input Processors、Hardware Integration、Troubleshooting (building, flashing, connection, hardware issues)。

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