“wheel”的搜索结果

🇺🇸 UGC Matrix Growth Playbook — Scale user-generated content with AI + real creators. Complete SOP for UGC flywheel, creator recruitment, incentive design, multi-platform publishing. Proven at CPM $0.5, $10M ARR in 60 days with 70M impressions. Includes a full anonymized client case study. 🇨🇳 UGC矩阵号增长实操手册 — AI+真人创作者规模化用户生成内容。完整的UGC飞轮SOP、创作者招募、激励机制设计、多平台分发策略。已验证CPM $0.5，60天$1000万ARR，7000万曝光。含完整客户案例拆解（已脱敏）。 🇯🇵 UGCマトリックス成長プレイブック — AI＋リアルクリエイターでUGCをスケール。フライホイールSOP、クリエイター採用、インセンティブ設計、マルチプラットフォーム配信。CPM $0.5、60日で$10M ARR、7000万インプレッション実証済み。匿名化クライアント事例付き。 🇰🇷 UGC 매트릭스 성장 플레이북 — AI + 실제 크리에이터로 UGC 스케일링. 플라이휠 SOP, 크리에이터 모집, 인센티브 설계, 멀티플랫폼 배포. CPM $0.5, 60일 $10M ARR, 7000만 노출 검증 완료. 익명화된 클라이언트 사례 포함. Triggers: "UGC" | "UGC matrix" | "user generated content" | "creator program" | "creator economy" | "AI UGC" | "matrix accounts" | "矩阵号" | "UGC运营" | "创作者招募" | "UGC Program" | "content matrix" | "TikTok UGC" | "Instagram UGC"

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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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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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将研究报告或内部复盘制作成独立HTML幻灯片，以纸色页面、深蓝色章节页和衬线字体统一组织内容与指标，支持横向滑动和键盘翻页。

• 在组会汇报研究设计、结果与局限
• 制作季度内部复盘
• 制作日语研究演示资料

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

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

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

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