'low latency' 검색 결과

Comprehensive, measurement-driven performance optimization for latency, throughput, memory/GC, and tail behavior. Use when the user asks to optimize/speed up, reduce latency (p95/p99), increase throughput/QPS, lower CPU/memory/allocations/GC pauses, profile hot paths, or run a benchmarked perf pass (including JSONL/query-heavy code). Requires before/after measurement on a runnable workload (or an explicit `UNMEASURED` plan) plus a correctness gate.

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Investigate latency, freezes, memory growth, hitches, dropped frames, or perceived slowness with reproduction and instrumentation before optimizing.

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Systematic performance profiling and optimization methodology. Covers CPU profiling, memory profiling, heap analysis, goroutine/thread analysis, FlameGraph generation, latency profiling, database query profiling, network I/O profiling, and continuous performance regression detection. USE WHEN: investigating performance issues, optimizing slow code, reducing memory usage, diagnosing GC/CPU bottlenecks, finding N+1 queries, analyzing pprof/flamegraph output, or establishing a performance baseline. Triggers on "profiling", "performance analysis", "slow code", "flame graph", "pprof", "heap dump", "memory leak", "CPU spike", "bottleneck".

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Optimize a measured performance problem when the user names a latency/load/Core Web Vitals/resource target or current profiling evidence identifies a bottleneck; do not trigger from generic implementation, review, or an unsupported suspicion that code may be slow.

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AgentDB의 데이터 압축, 검색 인덱스, 캐시, 일괄 처리 설정과 성능 측정을 지원하여 메모리 사용량과 검색 속도의 균형을 조정합니다.

• AgentDB 메모리 사용량 줄이기
• 정확도를 유지하며 검색 속도 높이기
• 수백만 벡터에 맞는 설정 검토하기

OmniRoute로 사용하는 AI 서비스의 상태, 응답 시간, 사용 한도와 지출 경고를 확인합니다. 장애 시 연결 차단과 복구 예정 시간을 살피고 예산 상한 도달 시 동작을 설정합니다.

• AI 서비스 지연과 장애 원인 조사
• 남은 사용 한도와 초기화 시각 확인
• 지출 상한과 도달 시 동작 설정

AgentDB의 데이터 압축, 검색 인덱스, 캐시, 일괄 처리 설정과 성능 측정을 지원하여 메모리 사용량과 검색 속도의 균형을 조정합니다.

• AgentDB 메모리 사용량 줄이기
• 정확도를 유지하며 검색 속도 높이기
• 수백만 벡터에 맞는 설정 검토하기

Turn text into speech with ElevenLabs through Composio MCP. Browse voices, select speech models, check remaining credits, generate downloadable audio or stream it for playback, and retrieve previously generated recordings from history.

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• Create narration from a script
• Compare available voices and their attributes
• Check remaining credits before bulk generation

Inspect local AI coding session logs with agenttrace to identify cost spikes, delays, failed tools, and retry loops. Produce readable reports, compare attempts, and configure automated checks for unusual or unhealthy runs.

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• Investigate slow or expensive AI coding sessions
• Review failed runs before retrying
• Compare tool usage and verification across attempts

Design and refine prompts for AI applications using examples, reusable templates, and structured outputs such as JSON. Test variations for accuracy, consistency, response time, and token usage to diagnose unreliable answers and formatting failures.

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• Refine prompts that produce inconsistent answers
• Build reusable prompt templates with variables
• Design JSON outputs that follow a defined schema

Investigate SGLang serving failures by collecting server state, preserving failing requests, and replaying them on a clean target. Summarize evidence and likely causes, then choose the next debugging tool for crashes, stalls, or performance regressions.

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• Investigate failed health checks and growing queues
• Reproduce request-specific timeouts or crashes
• Investigate output changes after deployment

Build voice applications and phone agents with guidance on OpenAI Realtime API, Vapi, Deepgram, ElevenLabs, and LiveKit. Choose services for speech recognition, voice generation, and live communication, while addressing response delays and interruptions.

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• Build a voice assistant that responds in real time
• Create a phone support agent with Vapi
• Connect speech recognition and voice generation

Tune cognee ingestion speed, query response time, and LLM costs by adjusting concurrency, text chunk sizes, request limits, and model choices. Estimate ingestion costs before processing and assess options for background work and deployment scaling.

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• Estimating costs before importing documents
• Adjusting ingestion speed and request limits
• Reducing query response time

Analyze the full retained history of one NemoClaw pull request to identify contributor, review, and automation delays. Generate a summary and an interactive timeline file, then compare observed timing with a default ten-minute target or a requested alternative.

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• Find delays across a pull request's lifetime
• Compare processing time with a target
• Measure review and merge waiting time

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

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• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

Plan how an AI agent uses limited input space through reusable prompt prefixes, shorter tool outputs, summaries, and task splitting. Measure token use, response quality, and latency to choose optimizations that provide a net benefit.

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• Reducing token use from lengthy tool outputs
• Improving prompt cache reuse
• Planning context budgets for long conversations

Model ongoing requests and multi-step agent work to estimate capacity, latency, and cost per completed task. Break execution into stages, account for queues and retries, and produce workload tables with average and peak resource budgets.

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• Estimate capacity for ongoing AI requests
• Analyze queues and tool waits in agent workflows
• Compare resource costs per completed task

Inspect production AI agents in FailproofAI Cloud using the fp CLI. Review errors, run histories, quality scores, and usage, then manage issues, audits, user access, and scoped API keys for the team's deployment.

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• Investigating failed agent runs
• Checking for drops in quality scores
• Running audits and triaging findings

Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

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• Reduce memory use in an AgentDB application
• Tune search speed while preserving accuracy
• Plan settings for millions of vectors

Investigate slow interactions and rendering using performance traces, which record execution events, alongside source code. Identify the responsible code, distinguish measurements from hypotheses, and guide fixes that preserve behavior, with tests and comparable recordings for verification.

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• Investigating scroll or resize stutter
• Finding causes of excessive React rendering
• Auditing reported profiling results

Build Python simulations with SimPy to study queues and shared resources such as machines, staff, or servers. Model events over time, measure waiting times and utilization, and compare capacity choices.

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• Compare service waiting times under different staffing levels
• Measure machine utilization on a production line
• Model inventory flows between producers and consumers

Benchmark a deployed NVIDIA RAG Blueprint server using a YAML configuration. Measure time spent in each processing stage, run optional aiperf load tests, and compare response latency, throughput, and bottlenecks across concurrency and retrieval settings.

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• Find slow stages in a running RAG server
• Compare performance across concurrency levels
• Measure the impact of retrieval and reranking settings

In-memory caching in Golang using samber/hot — eviction algorithms (LRU, LFU, TinyLFU, W-TinyLFU, S3FIFO, ARC, TwoQueue, SIEVE, FIFO), TTL, cache loaders, sharding, stale-while-revalidate, missing key caching, and Prometheus metrics. Apply when using or adopting samber/hot, when the codebase imports github.com/samber/hot, or when the project repeatedly loads the same medium-to-low cardinality resources at high frequency and needs to reduce latency or backend pressure.

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Create text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot.

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