'backend' 검색 결과

Design mock APIs, simulated services that reproduce expected responses, authentication, errors, and delays. Define scenarios and repeatable test data to support frontend development, integration testing, and realistic demos before the real backend is ready.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Developing a frontend before the backend is ready
• Simulating a partner or third-party API
• Preparing a demo with realistic API responses

Design APIs and interfaces between parts of an application with clear input and output types, consistent errors, and predictable names. The guidance helps you extend existing interfaces while preserving compatibility for the code that uses them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing new REST or GraphQL endpoints
• Defining contracts between frontend and backend
• Reviewing changes to a public interface

Design and improve server-side applications using patterns for Node.js, Express, and Next.js API routes. Code examples cover API structure, database access, caching, authentication, error handling, and background jobs to guide implementation choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Organize API endpoints and application logic
• Reduce unnecessary database queries
• Add authentication and permission checks

Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models using the same benchmark tests
• Run large evaluations on a Slurm cluster
• Evaluate safety and image understanding

Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.

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• Running large models with limited GPU memory
• Compressing a model with domain-specific sample text
• Serving AWQ models with vLLM

Install Cursor as an agent backend for NanoClaw groups using its local SDK. Set up authentication and container integration so groups can use Cursor for planning, MCP tools, streaming responses, and session continuation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Install Cursor as a NanoClaw agent backend
• Configure Cursor account sign-in or API key authentication
• Switch a specific NanoClaw group to Cursor

Plan a Zoom meeting bot or recorder, including how it joins meetings, handles live audio, video, and transcripts, and connects backend services for storage, post-processing, and event handling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a bot that joins Zoom meetings
• Planning live audio, video, and transcript processing
• Organizing post-meeting storage and retries

Find the engineering specialist skill that matches a task, from system design and frontend development to testing, security, and AI. This index explains each area of focus and directs agents to load the relevant skill’s instructions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a skill for system design or implementation
• Finding guidance for testing and code review
• Locating specialists for AI and data work

Run commands and coding agents on existing cmux Cloud machines, monitor work that continues after disconnection, and present results through workspaces or service URLs. The skill guides machine selection, background execution, and result verification.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run long commands that continue after disconnection
• Assign coding work to a remote agent
• Monitor remote work without opening panes

Evaluate changes to OpenSEO skills by running fresh Codex sessions against a local backend. Compare saved reports using weighted criteria, then inspect tool logs to understand failures and check whether revised instructions improve output.

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• Comparing report quality before and after skill edits
• Checking revised instructions across different websites
• Scoring SEO audit reports against defined criteria

Set up a new iii backend from engine installation to a working HTTP endpoint. Guides project creation, SDK installation, a first worker in TypeScript, Python, or Rust, and adding existing workers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Installing iii and creating a new project
• Installing an SDK for the chosen language
• Writing and testing a first HTTP worker

Build live backends that store data, broadcast changes, and run handlers automatically. Covers subscription authorization, connection events, and atomic updates, which apply changes safely when multiple writers act at once.

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• Broadcasting live data changes to subscribers
• Checking permissions before a subscription starts
• Tracking connections and disconnections

Study a reference adaptation connecting the shared skillopt_sleep cycle to a custom DeepSeek backend for OpenClaw. It provides prototype wrappers, sample tasks, and a porting checklist for developers building an integration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Study a prototype DeepSeek backend for OpenClaw
• Identify changes needed for current engine interfaces
• Review sample tasks and environment-specific settings

Test LangBot through its WebUI and compare visible results with backend logs or network behavior. Use it to verify chat, model settings, bots, knowledge bases, and plugins, with recorded evidence for test results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify pipeline replies in Debug Chat
• Check model provider setup and test buttons
• Test bot, knowledge base, and plugin UI flows

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Add new operators to MNN through a staged workflow covering data definitions, output shapes, computation, and tests. It prioritizes combining existing operators, with CPU implementations and additional hardware backends used when needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new operator to MNN
• Implementing output shape calculations
• Combining existing operators for new operations

Triage incorrect MNN outputs and regressions by matching symptoms to focused debugging guides. Use backend comparisons, minimal reproductions, and controlled experiments to investigate memory issues, quantization, concurrency, numeric limits, caches, and inconsistent results across runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating garbled or degraded MNN output
• Comparing results across execution backends
• Tracking regressions after optimization changes

Find the right workflow for MNN CPU work, from investigating slow execution to implementing calculation routines and planning tests. Compare ARM, x86_64, and RISC-V backend structures before choosing the relevant guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing where to start an MNN CPU performance investigation
• Finding guidance for CPU kernel implementation
• Comparing ARM, x86_64, and RISC-V backend structures

Help users discover and connect apps, then run available app tools through QM’s authenticated backend. It checks connection status, presents consent options, and keeps Composio project keys out of user-facing setup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Browse available app integrations
• Connect an app needed for a task
• Reopen app connection setup

Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models on the same benchmark tasks
• Running a small evaluation sample before a full benchmark
• Running large evaluations on a Slurm cluster

Add capabilities to an existing Convex app by consulting an online catalog for matching procedures. If no procedure is available, it falls back to hosting setup or a component search, then installs and connects the selected component.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding billing to an existing Convex app
• Setting up hosting for an app
• Adding authentication or search

Turn a one-sentence app idea into a minimal Next.js and Convex project. The skill installs dependencies, starts backend and web development servers, and opens the local development URL for an initial working template.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Start a web app from a short idea
• Try a minimal Next.js and Convex template
• Generate backend code when scaffolding cannot run

Create tests for Convex functions with convex-test and vitest, using a backend that runs in memory. Check arguments, return values, authentication, indexes, errors, and scheduled functions without relying on real time or network access.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test Convex function arguments and return values
• Check behavior with different authentication identities
• Cover errors as well as successful requests

Build and update Convex backend code, including schemas, queries, authentication, storage, and scheduled jobs. Apply input and output validation, indexed data access, and deployment checks to verify the implementation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing schemas and indexed queries
• Implementing queries, mutations, and actions
• Wiring authentication, storage, and scheduled jobs