'memory optimization' 검색 결과

Configure images and videos as surface textures in Three.js, and use environment images for backgrounds and reflections. Adjust placement, color settings, repetition, and filtering, then manage texture memory through compression, reuse, and disposal.

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• Adjust image placement and tiling on 3D surfaces
• Use HDR images for backgrounds and reflections
• Apply video or Canvas content to a 3D surface

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

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• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs

Decide whether an LLM needs additional training or whether retrieval and better prompts will suffice. Match available examples, preferences, or verifiable results to a training method, then assess model size and memory requirements before starting.

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• Comparing fine-tuning with retrieval and prompt changes
• Choosing a training method for available data
• Assessing model size and memory requirements

Improve code through repeated experiments against a measurable target, such as execution time or memory use. After defining scope and constraints, the agent tests changes, keeps improvements, reverts unsuccessful attempts, and logs results.

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• Reduce execution time through measured code experiments
• Optimize memory use or bundle size
• Compare changes using benchmark results

Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.

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• Building document retrieval for AI answers
• Combining similarity search with category or date filters
• Reducing memory use for large vector collections

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

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• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Extend the amount of text a language model can process at once. Compares RoPE, YaRN, ALiBi, and position interpolation, with implementation examples, additional training guidance, and memory optimization approaches for long document processing.

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• Extend an existing model to handle longer inputs
• Compare methods for representing token positions
• Plan additional training with long documents

Guides migration of existing codebases to Rust without unsafe code, covering architecture, implementation, testing, and output comparisons. It measures performance before and after optimization and produces an interactive HTML report documenting results and unresolved differences.

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• Migrating a GitHub or local codebase to Rust
• Comparing original and migrated program outputs
• Measuring and optimizing a Rust port

Review past Claude Code sessions and turn recurring tasks into proposed updates to skills and project memory. Replay tasks, compare scores on separate validation tasks, and review staged edits before applying them with backups.

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• Consolidate repeated Claude Code corrections into reusable rules
• Review proposed changes to SKILL.md and CLAUDE.md
• Try the cycle without provider calls using mock

Optimize and troubleshoot MNN's Hexagon DSP backend, which runs computation on a dedicated processor. The workflow uses device measurements to identify bottlenecks, compare v79 and v81 architectures, verify correctness, and investigate crashes or memory usage.

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• Finding bottlenecks in Hexagon operators
• Checking changes on v79 and v81 devices
• Investigating cDSP crashes and device disconnects

Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.

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• Improve an existing agent against benchmark tasks
• Build evaluations for domain-specific tasks
• Refine skills and memory from failure feedback

Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill guides model selection, batch processing, and memory optimization, with examples for understanding images without training for each specific task.

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• Generate captions for multiple images
• Build a question-answering system for images
• Search an image collection using text

Build searches with Qdrant that find similar content and filter results by attributes. The skill covers document retrieval for AI answers, vector storage, memory optimization, and distributed deployment for larger collections.

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• Building document retrieval for AI answers
• Combining similarity search with category or date filters
• Reducing memory use for large vector collections

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

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• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Guides PyTorch model training and fine-tuning, from choosing architectures to configuring optimizers and learning rates. Provides troubleshooting steps for unstable loss and memory shortages, plus methods for improving GPU throughput and comparing experiments.

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• Choose model architectures for the available data type and scale
• Configure optimizers and learning-rate schedules
• Investigate loss spikes, NaNs, and GPU memory shortages

Build game systems in Unity or Unreal Engine, including physics, AI, and multiplayer networking. The skill guides architecture, implementation, profiling, and testing to work toward frame rate targets and reduce memory use.

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• Implementing game mechanics in Unity or Unreal Engine
• Reducing frame drops and memory use
• Building multiplayer networking with lag compensation

Measure and improve animation performance on frontend pages. Identify offscreen activity, pause CSS and canvas or WebGL loops, audit resource cleanup, and compare runtime observations before and after changes to investigate scrolling issues and long-session slowdowns.

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• Pause animations outside the viewport
• Investigate jerky scrolling
• Audit cleanup after page navigation

Optimize .NET loops with SIMD, which processes multiple values in one operation, or review existing SIMD code. Check behavioral equivalence, memory safety, platform fallbacks, and benchmarks to establish whether the optimization is correct and worthwhile.

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• Optimize numeric loops with SIMD
• Review existing vectorized code for correctness
• Test boundary lengths and overlapping buffers

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

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Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.

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Top-level workflow skill for USD performance diagnosis and optimization. Handles slow loading, high memory, low FPS, and broad scene-optimization requests; delegates auth/runtime setup to Phase 0 owners.

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Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.

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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

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