'high performance' 검색 결과

Analyze campaign data across advertising platforms, check whether results are comparable, and assess costs and returns. Get budget test proposals with stated evidence, uncertainty, spending limits, and rollback criteria.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare Google Ads and Meta Ads performance
• Investigate campaigns with high acquisition costs
• Plan limited tests of budget changes

Build Python applications that handle other work while waiting for network or database operations. Use asyncio and async/await to run tasks concurrently, manage errors and timeouts, and handle cancellation with appropriate cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building web APIs that handle many simultaneous requests
• Fetching data from multiple sources concurrently
• Implementing WebSocket servers and chat systems

Plan and configure connections between on-premises data centers and AWS, Azure, GCP, or OCI using VPNs or dedicated links. Cover routing, redundant connections, security controls, monitoring, troubleshooting, and connection costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect an on-premises data center to the cloud
• Plan connectivity for a gradual cloud migration
• Design redundant links and automatic failover

Build Python applications that keep other tasks moving while waiting for network, file, or database operations. The skill guides asyncio patterns, task cancellation, timeouts, workload limits, and testing for asynchronous APIs, scrapers, and background processing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building asynchronous web APIs
• Sending concurrent requests in web scrapers
• Designing background tasks and queues

Plan and configure large language model training with OpenRLHF to better match human preferences. Covers training method selection, distributed GPU setup, inference acceleration with vLLM, and troubleshooting memory or training stability issues.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a method for training models on human preferences
• Setting up PPO training with a reward model
• Configuring GRPO training without a critic model

Prepare text for language models using HuggingFace Tokenizers, which splits text into model-readable units. Train custom vocabularies, process batches, track original text positions, and configure input lengths for use with transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing large text collections for language models
• Training a tokenizer on domain-specific text
• Mapping model outputs back to original text

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Turn a product description into a landing page with headlines, feature sections, pricing, FAQs, and calls to action. Receive Next.js/React components styled with Tailwind CSS, plus search metadata, structured data, and alternative copy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a landing page for a new product
• Building a campaign or lead capture page
• Comparing headline and page variants

Assess Google Cloud workloads for environmental sustainability using the Well-Architected Framework. Get recommendations on lower-carbon regions, efficient computing and storage, AI energy use, and ongoing emissions measurement with Carbon Footprint.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing cloud architecture for environmental impact
• Comparing deployment regions by carbon-free energy use
• Reducing energy waste in AI and computing workloads

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Prepare large text collections for AI models with fast tokenizers, which split text into model inputs. Train custom vocabularies, trace tokens to original text positions, adjust input lengths, and integrate the results with transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tokenizing large text collections
• Training a custom vocabulary
• Mapping extracted names or answers back to original text

Guide large language model training with OpenRLHF using human preference feedback. Covers training method selection, distributed GPU setup, reward model training, and memory tuning, with examples for PPO, GRPO, and DPO.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a training method for human preference alignment
• Setting up distributed training across multiple GPUs
• Training a reward model before PPO

Build Python APIs with FastAPI and Pydantic V2, including input validation, database operations that avoid blocking other work, and token-based authentication. Implement REST or WebSocket endpoints, then check their behavior with automated tests and generated API documentation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build REST APIs with FastAPI
• Validate input data with Pydantic V2
• Implement JWT authentication and authorization

Turn work records, performance metrics, and authorized project information into weekly or monthly status reports. It checks calculations, highlights progress and unresolved issues, and marks missing information without inventing plans or assigning unsupported ratings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting weekly reports from work records
• Checking completion rates for monthly reports
• Summarizing blocked work and pending decisions

Review a proposed employee termination for risk factors, documentation gaps, and required approvals. Research local final-pay and notice rules, then organize severance and release requirements into a review memo and practical checklist.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking risk factors before a proposed termination
• Reviewing final-pay deadlines and required notices
• Organizing severance and release requirements

Build dense, frequently updated charts with Canvas2D, including zooming, clicking, and dragging. Guides rendering performance, mobile interactions, and the use of SVG or HTML for labels, controls, and accessibility.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a scatterplot with a million points
• Add selection and zoom to a timeline
• Move an SVG heatmap to Canvas while preserving labels

Build working web pages, components, and app interfaces around a clear visual concept. Coordinate typography, color, spacing, motion, and interaction details, adapting layouts to different screen sizes while avoiding repetitive visual templates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a landing page with a distinct visual direction
• Creating a dark-themed analytics dashboard
• Implementing responsive web components

Add animated liquid-metal borders to React buttons, cards, tabs, and other surfaces using metal-fx. The skill covers visual tuning, linking effects to interaction states, preserving keyboard access, and providing static borders when animation or WebGL is unavailable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Highlighting selected tabs and filters
• Adding metallic borders to primary buttons
• Tuning card borders, glow, and corner radius

Design ClickHouse tables, write analytical queries, and plan data loading using practical SQL and code patterns. Use it to review slow queries, build recurring summaries, and analyze user activity across large datasets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing tables for large analytical datasets
• Investigating slow queries
• Building real-time summaries

Parallel read-only multi-agent review of a current git diff or explicit file scope to find behavioral regressions, security or privacy risks, performance or reliability issues, and contract or test coverage gaps. Use when the user asks for a review swarm, parallel review, diff review, regression review, security review, or wants high-signal issues plus a prioritized fix path without editing files.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

High-performance CSV processing with xan CLI for large tabular datasets, streaming transformations, and low-memory pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze stocks with Mark Minervini's SEPA (Specific Entry Point Analysis) methodology: stage analysis, the 8-condition trend template, fundamentals, VCP and other base patterns, pivot-point entries, market environment, and risk-based position sizing. Use this skill whenever the user mentions SEPA, Minervini, superperformance, the trend template, VCP (volatility contraction pattern), Stage 2, pivot or breakout entries, moving-average stacking (price above the 50/150/200-day MAs), breakout volume, position sizing from risk percentage, growth-stock screening or swing-trade entry criteria, or bases such as cup-with-handle, flat base, bull flag, or high tight flag. Also use it when the user asks "should I buy this stock" or "is this a good setup" about a growth or momentum name, or shares a chart for pattern analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.