'wheel' 검색 결과

Build and grow developer and user communities from scratch — covering ambassador programs, community-led growth (CLG), event operations, content strategy, and health metrics. Grounded in real playbooks from Notion, Lovable, AFFiNE, ClickUp, Asana, and Lark. Use when: building a community for an open-source project, SaaS product, or brand; recruiting and managing ambassadors or community leaders; planning community events (online/offline meetups, AMAs, hackathons); designing ambassador tier/points systems; setting community health KPIs; launching a Build-in-Public strategy; setting up Discord, Slack, or Lark communities; running developer relations (DevRel); or when the user asks about community operations, ambassador programs, CLG, user advocacy, KOC/KOL cultivation, UGC flywheels, community monetization, or any variation of "how do I grow my community".

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🇺🇸 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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应用从 AI for Everyone 蒸馏出的「数据飞轮 — AI产品自我强化的增长引擎」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to data-flywheel.

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应用从 AI for Everyone 蒸馏出的「试点势能飞轮 — 先求成功再求价值」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to pilot-momentum-flywheel.

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Start a new Flywheel planning round for a Forge feature. Writes the initial markdown plan to .flywheel/plans/ and gives instructions for sending it to competing frontier models. Use at the very beginning of a new feature or subsystem.

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Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

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AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

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Expert en croissance par le contenu (pillar pages, programmatic SEO, content flywheel, distribution)

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Builds CARLA (branch ue4-dev, Unreal Engine 4.26) from source on Linux end-to-end — UE4 fork, the Carla server (editor C++ modules), the LibCarla Python client wheel, and content — then verifies them against a from-source server. Optionally builds the native ROS 2 interface in (ROS2=1 → --ros2, Fast-DDS/CycloneDDS/Zenoh), which is compile-time only and cannot be enabled later. Use when the user asks to "build CARLA from source", "compile CARLA ue4-dev", "set up CARLA on Ubuntu (incl. 24.04)", "build CARLA with ROS2 support", or "produce a CARLA server + client wheel". Cooking a distributable Dist/ package is a separate skill (package-carla-ue4); running the build is run-carla-server.

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연구 보고나 사내 검토 자료를 차분한 종이 느낌의 HTML 슬라이드로 만듭니다. 짙은 파랑과 종이색, 세리프 글꼴로 구성하며 가로 스와이프와 키 조작으로 열람합니다.

• 연구실에서 연구 설계·결과·한계 보고
• 분기별 사내 검토 자료 작성
• 일본어 연구 발표 자료 제작

Create diagrams that explain systems, workflows, and data relationships using a consistent brand style. The skill guides diagram selection, layout, and readability, with HTML, SVG, and PNG output and support for importing existing diagram formats.

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• Explain system architecture
• Visualize workflows and team handoffs
• Restyle existing diagrams to match a brand

Plan and improve customer or fan communities with platform recommendations, launch plans, welcome journeys, recurring activities, and ambassador programs. The skill also reviews participation metrics to identify where members disengage and suggest practical priorities for community managers.

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• Plan a community launch from scratch
• Improve welcome journeys for new members
• Design recurring events and discussion threads

Combine laboratory animal welfare measurements into reference-relative RELSA severity scores. Analyze individual trajectories, explore forecasts for specified observation times, and derive candidate score zones, with explicit checks on normalization, reference selection, prediction intervals, and evaluation limits.

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• Combine animal welfare measurements into daily scores
• Compare group severity using a shared reference
• Explore severity forecasts at specified observation times

Set up machine learning training and inference on NVIDIA DGX Spark. Get guidance on choosing containers and packages, resolving CUDA compatibility errors, and checking that PyTorch can access the GPU before starting a workload.

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• Setting up a new DGX Spark for training or inference
• Diagnosing CUDA compatibility and import errors
• Choosing between containers and direct pip installation

Integrate or upgrade cache-dit in SGLang diffusion, the image generation system. Follow guidance for resolving code conflicts, adding cache settings, testing feature combinations, and comparing generated images using quality measurements and logs.

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• Upgrading cache-dit in SGLang diffusion
• Resolving conflicts when porting upstream changes
• Adding per-request cache settings

Compare five growth loops, mechanisms where user activity brings in more users, to find a suitable approach for a product. Develop an implementation roadmap, measurement plan, and growth estimates based on sharing, collaboration, content, or referrals.

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• Designing product features that attract new users
• Planning growth with less reliance on paid acquisition
• Comparing sharing, collaboration, and referral mechanisms

Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.

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• Choose a deployable model and supported hardware
• Estimate hourly deployment costs before approval
• Check deployment progress and test predictions

Inspect a vehicle or another object in a running CARLA simulation. Read position, speed, and acceleration from the same simulation frame, check vehicle controls and steering angle, or stream the object's state over time.

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• Checking the ego vehicle's speed and heading
• Streaming acceleration data for ten seconds
• Inspecting a specific vehicle's controls

Install Scenic, a language for describing scenarios with randomized conditions, into the Python environment used by CARLA. Check command-line tools and version compatibility, optionally download example scenarios and map assets, and save the installation paths.

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• Preparing Scenic to work with CARLA
• Diagnosing scenario model import failures
• Checking missing vehicle definition tables

Inspect data published by a ROS 2-enabled CARLA server using Docker, without installing ROS 2 locally. Check topic contents and rates, run a sensor-equipped vehicle demo, and view lidar data and supported map markers in RViz2.

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• Checking whether CARLA is publishing ROS 2 data
• Inspecting sensor data contents and publishing rates
• Viewing lidar data and road markers in RViz2

Set up ScenarioRunner, CARLA’s scenario engine, with a branch matched to your simulator version. Install dependencies, configure Python search paths, and verify imports to address setup failures before running scenarios.

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• Install ScenarioRunner for a specific CARLA version
• Switch an existing setup to the UE5 branch
• Fix imports failing because the agents package is missing

Package a CARLA Leaderboard agent into a Docker image for submission. It checks required directories and Python package files, supports ROS variants, and verifies that dependencies import and the agent loads inside the image.

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• Build a Docker image for Leaderboard submission
• Investigate missing egg files in make_docker.sh
• Package a ROS agent with its bridge

Design a content marketing system that connects posts, videos, and newsletters to sales opportunities. It helps analyze successful content, adapt one core piece for multiple platforms, plan follow-up conversations, and measure which content contributes to deals.

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• Plan a content workflow within a weekly time budget
• Adapt one article or video for multiple platforms
• Connect newsletters and engaged readers to sales conversations

Create SVG illustrations, a scalable vector image format, with thick navy outlines, vivid colors, and chunky depth. Combine a cartoon character with overlapping props for product screens and marketing cards, then render, review, and refine the artwork.

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• Illustrating 404 and error pages
• Creating onboarding illustrations
• Designing blog header artwork